TW201909001A - System and method for generating genetic profiling and related purchasing suggestions by artificial intelligence enhanced chat robot - Google Patents

Abstract

Presented herein are systems and methods that allow a user to interact with an artificial intelligence chatbot in order to automatically identify genetic profile tests of interest to them, as well as recommendations about health and fitness products and/or plans personalized for the user based at least in part on the user's genetic profile test results (e.g., as stored in his/her/their genetic profile). Such recommendations may include, for example, additional diagnostic tests (e.g., additional genetic profile tests, e.g., tests for particular characteristics, traits, diseases, and/or conditions), recommendations of nutritional supplements to purchase, recommendations about specific programs (e.g., meal programs, fitness programs, etc.) that are well-suited for the user, and the like.

Description

System and method for generating genetic profile test and related purchase suggestion by artificial intelligence enhanced chat robot

The present invention relates generally to systems and methods that facilitate the provision of purchase advice for genetic contour products to users.

The genome holds valuable information that can be used to better understand the characteristics of the organism. In particular, much research is being conducted to establish the relationship between the human genome and biological characteristics and traits. For example, single nucleotide polymorphisms (SNPs) are specific sites identified in specific genes that affect biological characteristics and traits depending on the particular polymorphism of the individual. Different polymorphisms of nucleotides at a particular site affect related properties or traits in different ways. Many relationships have been established between the variation of SNPs and their corresponding biological characteristics and traits, and more possible relationships are currently undiscovered and under investigation.

Personalized genetic profiles (such as LifeProfile provided by Orig3n of Boston, Massachusetts) use simple cheek swab samples to provide SNP-based assessments of various characteristics and traits to provide safe, user-friendly wisdom Phones have access to test results. The individual provides a biological sample and receives an assessment of one of their genetic profiles that is accessible for review on their smartphone. For example, individuals can understand from their genetic profiles how their genomes affect their personal health characteristics, fitness characteristics, and diet characteristics. As more linkages between genotyping data and various traits and characteristics are identified, the number of personalized genetic profile products accessible to consumers will continue to grow. The amount of this information available to consumers will expand, and it will be important to have a way to organize and present that information in an understandable, consumer-friendly way.

Many individuals regularly take vitamins, supplements, and other over-the-counter or prescription drugs to improve their physical and mental health. Usually, supplements and medicines are taken to relieve chronic conditions. For example, some individuals take glucosamine to treat joint pain. Supplements can also be taken to improve efficacy or function. For example, some individuals take supplements (such as nitric oxide) when lifting weights to promote improvements in their physical fitness and strength. Individuals often prescribe such supplements on their own based on personal research or physician recommendations. Therefore, an individual's decision depends primarily on qualitative information about how the individual feels or how a physician sounds like the patient's condition as described. Similarly, based on this qualitative information, individuals select fitness programs, diet plans, and other health and fitness related health systems. The information available through personalized genetic profile products can better inform a particular consumer's decisions about which vitamins, supplements, and / or over-the-counter medicines to take, as well as what fitness regimens, diet regimens, stress management regimens, and / or others Health and physical and mental health-related programs are best suited to specific consumer decisions. However, there is still a need to automatically identify and present a consumer with information about such suggestions in an understandable, consumer-friendly manner.

Therefore, there is a need for automated systems and methods to help individuals select relevant genetic profile tests to provide personalized information of interest, and to provide individuals based genetic profiles to provide supplements, health and fitness programs that are suitable for them, Suggested automated systems and methods for meal plans, stress management plans, and the like.

It is proposed herein to allow a user to interact with an artificial intelligence chat robot to automatically identify genetic profile tests of their interest, and / or based at least in part on the user's genetic profile test results (e.g., as stored in his / her / (In their genetic profile) proposed systems and methods for automatic identification (eg, regarding health and fitness products and / or programs personalized to the user). These recommendations may include, for example, nutrition supplement purchase recommendations, recommendations on specific protocols (e.g., diet, fitness, etc.) that are well suited for the user, additional diagnostic tests (e.g., additional genetic profiles Testing; for example, recommendations for specific characteristics, traits, diseases, and / or conditions) and the like.

In some embodiments, the artificial intelligence chatbot described herein identifies a user for purchase based on a user problem and how a user's personal genome affects his / her biological traits (e.g., health-related phenotypes). Recommendations (for example, purchase recommendations). For example, a supplement suggestion may be identified based on (e.g., related to) (e.g.,) a user's genetic profile test results (e.g., genotyping data) for the need for a particular supplement. Genetic profile test results are obtained from biological samples provided by individuals, and genetic profile test results include genotyping data associated with a range of biological characteristics. The genotyping data can be stored as a genetic profile, and the artificial intelligence chat robot can access the genetic profile of the user in response to a user question to identify appropriate suggestions for the individual. These suggestions can be associated with various characteristics of the user based on his / her genetic profile determination. For example, the genetic profile of a user may reveal: (i) nutritional characteristics (e.g., how an individual's body handles different foods and nutrients); (ii) skin health; (iii) physical fitness; and (iv) an individual Behavioral tendencies (e.g., empathy, risk of addiction, and tolerance to stress and pain), and these characteristics can be used to identify suggestions for users.

For example, based on a particular biological trait of an individual, a chatbot may identify one or more supplements that a user may want to purchase in response to a user question. For example, in response to a question about one user of nutrition, the chatbot may suggest identifying one or more nutritional supplements based on the user's genetic profile. For example, if the user's genetic profile indicates that the user's ability to handle a particular food is reduced, the chatbot may suggest nutritional supplements that help in processing the food. The user may be presented (eg, in a graphical user interface presented on a human computing device) with one or more purchase suggestions or links to purchase suggestions for such supplements. Therefore, in some embodiments, a user can easily review supplement purchase recommendations based on one of the questions being asked and information in the user's genetic profile. Users can also have the ability to purchase directly or be redirected to purchase supplements.

In one aspect, the present invention relates to a method for providing a purchase suggestion corresponding to a genetic profile test through interaction between a user and an artificial intelligence chat robot. The method includes: (a) using one of a computing device The processor receives user input of a text query; (b) identifying one or more genetic profile tests (e.g., using a machine learning module) related to the text query by the processor based on the text query; A genetic profile test is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one of a specific set of one or more SNPs (e.g., where each corresponding SNP influences the genetic profile Testing for a specific health-related trait associated with the general class of health-related phenotypes; and (c) provided by the processor to each of the one or more identified genetic profile tests (e.g., using Appearing and / or graphically displayed on a computing device of a user) includes a graphical representation (eg, the name of the test that appears as text) identified by the genetic profile test; for example, and One image associated with this test).

In some embodiments, identifying one or more genetic profile tests includes: accessing, by a processor, a database for each of a set of predefined genetic profile tests including one of the following (e.g., a set of text files, Such as an AIML file): (i) an identifier of the genetic profile test [eg, a text label (eg, representing a name of the genetic profile test)]; and (ii) associated with the identifier of the genetic profile test One or more keywords; and for each identified genetic profile test, one or more of the terms in the text query matches (i) the identifier of the genetic profile test and / or (ii) the one or more keys Match at least one of the words.

In certain embodiments, for example, each SNP of a set of SNPs for which one or more keywords are measured for a genetic profile test includes a name of a gene associated with the SNP (e.g., a gene in which a SNP occurs). A name; for example, a name of a gene whose SNP affects transcription).

In some embodiments, the method includes identifying one or more genetic profile tests based in part on information stored in one or more reference files in a database of reference files.

In some embodiments, identifying one or more genetic profile tests includes accessing a database including a plurality of reference documents (e.g., published literature; e.g., a plurality of web pages of a public database) by a processor. Where each reference file is associated with one or more SNPs measured by the one or more genetic profile tests (e.g., each reference file includes information about one or more SNPs and specific health-related phenotypes that affect them) ); Use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user's text query [for example, by comparing the text query with the text data in the reference document (for example, the written word ) (E.g., by extracting keywords from a text query and searching for matches in a reference file; for example, using receiving text queries as input and determining one or more SNPs related to the user's text query based on the reference file machine learning Module)]; For each of the one or more expected genetic profile tests, determine one or more SNPs related to the user's text query with the expected genetic profile test measurements Or more of a degree of match between the SNP set; and identifying one or more genetic profile is determined by the test based on the matching degree.

In some embodiments, the graphical representation including identification of one of the genetic profile tests includes a selectable link that is selected by the user (e.g., by a user clicking with a mouse; for example, by the user Using a touch gesture such as a touch-sensitive interface of a touch screen to face the link) leads a user to a website that allows the user to purchase a genetic profile test.

In some embodiments, the method includes causing (e.g., on a computing device of a user) to display a graphical user interface (GUI) corresponding to a chat window, wherein a text query is received via the chat window GUI and A graphical representation including recognition of one of the genetic profile tests appears in the chat window GUI as a response to one of the text queries.

In some embodiments, the method includes an interactive website (e.g., a website used by users to view genetic profile test results; for example, thereby allowing users to identify and purchase additional genetic profile tests that may be of interest to them) ) The chat window GUI is displayed.

In some embodiments, the method includes an interactive mobile application (e.g., an app used by a user to view genetic profile test results (e.g., executed on a mobile device such as a mobile phone) app); for example, thereby allowing users to identify and (eg, via in-app purchases) purchase additional genetic profile tests that may be of interest to them] within a chat window GUI.

In some embodiments, the text query is provided by a voice assistant.

In another aspect, the present invention relates to a method for providing consumer feedback corresponding to one or more genetic profile tests through the interaction of a user with an artificial intelligence chat robot. The method includes: (a) by A processor of a computing device receives user input of a text query, wherein the user and one or more genetic profiles [for example, the one or more genetic profiles represent a result of a genetic profile test performed on the user; for example, Wherein the user is a user who has subscribed (e.g., logged in), who has been tested for one or more genetic profiles and has established one or more genetic profiles and stored in accessible by the processor One of the databases]; (b) a processor based on a text query and at least partially based on the user's one or more genetic contours to identify one or more suggestions in response to received user input (e.g., , (Several) purchase suggestions) (for example, using a machine learning module); and (c) provided by a processor (for example, for visualization and / or graphical display on one of the user's computing devices) includes a or A graphic representation of one of the recommendations [for example, a name that appears as text on a suggested purchase (eg, a nutritional supplement; for example, purchase of a mobile health device); for example, an image associated with a proposed purchase, response Ask for a text description (eg, a recommended diet plan, a recommended fitness plan, etc.)].

In some embodiments, identifying one or more suggestions includes: accessing a database by the processor for each of a set of predefined suggestions including one of the following (e.g., a set of text files, such as an AIML file) : (I) an identifier of the proposal [for example, a text label (for example, a name representing the proposal)]; and (ii) one or more keywords associated with the identifier of the proposal; and for Each identified suggestion matches one or more of the text queries with (i) the suggested identifier and / or (ii) at least one of the one or more keywords.

In some embodiments, each suggestion is associated with a set of one or more SNPs, and one or more keywords include, for each SNP of the SNP set associated with the suggestion, a gene of a gene associated with the SNP. A name (eg, a name of a gene in which SNP occurs; for example, a name of a gene in which SNP affects transcription).

In some embodiments, at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one or A measurement of a specific set of multiple SNPs (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test) and is related to the genetic profile The test-associated SNP set is a SNP measured by a genetic profile test.

In some embodiments, each suggestion of one or more suggestions is associated with a set of one or more genes, and the one or more keywords include one of the names corresponding to the genes associated with the suggestion Or multiple keywords.

In some embodiments, the method includes identifying one or more suggestions based in part on information in one or more reference files stored in a database of reference files.

In some embodiments, identifying one or more suggestions includes: accessing, by a processor, a database including a plurality of reference documents (e.g., published literature; for example, a plurality of web pages of a public database), wherein Each reference file is associated with one or more SNPs and / or genes associated with one or more recommendations (e.g., each reference file includes specific health related tables regarding one or more SNPs and / or genes and their effects) Type information); use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user's text query [for example, by comparing the text query with the text data in the reference document (for example, written Input terms) (for example, by extracting keywords from a text query and searching for matches in a reference file; for example, using a received text query as input and determining one or more SNPs related to the user's text query based on the reference file A machine learning module)]; for each of one or more expected suggestions, determine one or more SNPs and / or genes associated with the user's text query with the SNP and / or genes associated with the expected suggestion and / Or a degree of matching between genes; and identifying one or more suggestions based on the determined degree of matching.

In some embodiments, the method includes automatically identifying one or more suggestions based on a mutation in one of the SNPs in one of the user's genomes (eg, identified by one or more genetic profiles of the user).

In some embodiments, at least one of the identified suggestions is associated with one or more SNPs, and for each of the one or more associated SNPs, the suggestion is associated with the SNP (e.g., via a qualifier) (Identified) is associated with a particular variation, and the identification of the suggestion is based at least in part on a particular variation of one or more SNPs associated with the suggestion that the user has one or more SNPs as if they were associated with the user Correspondence (e.g., relationship; e.g., correlation) between specific mutations identified in the genetic profile of the gene.

In some embodiments, the method includes: receiving (and / or accessing) mobile health data recorded by a mobile health device of one of the users; and automatically based on the user's genetic profile and the received mobile health data Identify one or more suggestions.

In some embodiments, the one or more suggestions include a suggested genetic profile test.

In some embodiments, the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; e.g., a blood test; e.g., all tablets an examination).

In certain embodiments, the one or more recommendations include purchase recommendations for one or more supplements (eg, nutritional supplements).

In some embodiments, the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, a brain wave feedback plan, a behavior plan (e.g., A focused program, an ADHD program), and a somatization therapy. In some embodiments, the one or more members are individualized protocols and / or treatments based on one or more genetic profiles of the user.

In some embodiments, the one or more suggestions include one or more suggested purchases of one or more mobile health devices (and / or one or more software apps operating on a mobile health device).

In some embodiments, the graphical representation including identification of one of the genetic profile tests includes a selectable link that is selected by the user (e.g., by a user clicking with a mouse; for example, by the user Using a touch gesture such as a touch-sensitive interface of a touch screen to face the link) leads a user to a website that allows the user to purchase a genetic profile test.

In some embodiments, the method includes causing (e.g., on a computing device of a user) to display a graphical user interface (GUI) corresponding to a chat window, wherein a text query is received via the chat window GUI and A graphical representation including recognition of one of the genetic profile tests appears in the chat window GUI as a response to one of the text queries.

In some embodiments, the method includes an interactive website (e.g., a website used by users to view genetic profile test results; for example, thereby allowing users to identify and purchase additional genetic profile tests that may be of interest to them) ) The chat window GUI is displayed.

In some embodiments, the method includes an interactive app [eg, an app used by a user to view genetic profile test results (eg, an app executed on a mobile device such as a mobile phone); for example, This allows users to identify and (eg, via in-app purchases) purchase additional genetic profile tests that may be of interest to them] and display a chat window GUI within.

In some embodiments, the text query is provided by a voice assistant.

In another aspect, the present invention relates to a method for providing purchase advice corresponding to a genetic profile test through interaction between a user and a voice assistant. The method includes: (a) a processor through a computing device Receiving (e.g., via a network) a structured request including a text query from the voice assistant (e.g., a processor of the voice assistant), wherein the structured request is made by the voice assistant responding to the user's voice Generate [for example, by detecting the user's voice and generating voice data corresponding to at least a portion of the user's voice and processing the user's voice data to generate a text query (for example, by generating a text voice Data (e.g., via a machine learning module) to match the text speech data with one or more identifiable text phrases stored in a database, and at least use the matching identifiable text phrases A part as an item in a text query)]; (b) using the structured request text query by the processor to identify one or more genetic profile tests related to the user's voice [for example, by using A word query (e.g., one or more terms of a text query) matches (e.g., stored in a database) one or more identifiers of a genetic profile test (e.g., via a machine learning module; e.g., Identify one by identifying one or more subroutines based on a first part of a text query and passing a second part of a text query to the identified subroutines as a variable evaluated by the subroutines. Or multiple genetic profile tests)]; and (c) providing a speech (e.g., via a network) to the speech including one or more structured responses of each of the one or more genetic profile tests Assistant, where each structured response, when executed by a voice assistant (eg, one of the voice assistant processors), causes the voice assistant to generate an audio output corresponding to the simulated voice based on the structured response, thereby providing users with Audio feedback corresponding to recommendations associated with the one or more identified genetic profile tests.

In some embodiments, step (b) includes matching a text query (e.g., one or more entries of the text query) with one or more stored keywords each associated with one or more genetic profile tests .

In certain embodiments, one or more keywords for each SNP of a set of SNPs measured by one or more genetic profiling tests include a name of a gene associated with the SNP (e.g., where the SNP occurs A name of a gene; for example, a name of a gene whose SNP affects transcription).

In some embodiments, the method includes identifying one or more genetic profile tests based in part on information stored in one or more reference files in a database of reference files.

In some embodiments, identifying one or more genetic profile tests includes accessing a database including a plurality of reference documents (e.g., published literature; e.g., a plurality of web pages of a public database) by a processor. Where each reference file is associated with one or more SNPs measured via the one or more genetic profile tests (e.g., where each reference file includes information about a specific health-related phenotype of one or more SNPs and their effects) Information); use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the text query [for example, by comparing the text query with the text data in the reference document (eg, written words) (For example, by extracting keywords from a text query and searching for matches in a reference file; for example, using a machine learning model that receives a text query as input and determines one or more SNPs related to the user's text query based on the reference file (Group)]; for each of one or more expected genetic profile tests, determine one or more SNPs related to the user's text query with one of the expected genetic profile tests A plurality of matching between a set of SNP; and identifying one or more genetic profile is determined by the test based on the matching degree.

In another aspect, the present invention relates to a method for providing consumer feedback corresponding to one or more genetic profile tests through the interaction of a user with a voice assistant, the method comprising: (a) using an operation A processor of the device (e.g., via a network) receives a structured request including a text query from the voice assistant (e.g., a processor of the voice assistant), where the structured request is responded by the voice assistant to Generated by user voice [for example, by detecting the user voice and generating voice data corresponding to at least a portion (up to all) of the user voice and processing the user voice data to generate a text query (E.g., by generating text speech data (e.g., via a machine learning module) matching the text speech data with one or more identifiable text phrases stored in a database, and using such At least a portion (up to all) of the matched identifiable text phrases as items in the text query)], and wherein the text query of the structured request includes users associated with one or more genetic profiles ( (E.g., a subscribed user, one or more genetic profile tests have been performed on it and one or more genetic profiles have been established and stored in a database accessible by the processor) Identify; (b) use the text query of the structured request by the processor to identify one or more suggestions (e.g., purchase suggestions) based at least in part on one or more genetic profiles associated with the user; and (c) One or more structured responses including the identification of each of the one or more suggestions are provided to the voice assistant by a processor (e.g., via a network), where each structured response is provided by the voice assistant (e.g., When one of the voice assistants is executed, the voice assistant is caused to generate an audio output corresponding to the simulated voice based on the structured response, thereby providing users with audio feedback corresponding to the one or more suggestions.

In some embodiments, step (b) includes correlating one of the text queries (e.g., one or more terms of the text query) with each of at least one of the one or more suggestions (e.g., purchase suggestions) Or multiple stored keywords.

In some embodiments, each suggestion of one or more suggestions is associated with a set of one or more SNPs, and each SNP of the set of one or more stored keywords for the SNP set measured by the genetic profile test includes A name of a gene associated with the SNP (eg, a name of a gene in which the SNP occurs; for example, a name of a gene in which the SNP affects transcription).

In some embodiments, at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one or A measurement of a specific set of multiple SNPs (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test) and is related to the genetic profile The test-associated SNP set is a SNP measured by a genetic profile test.

In some embodiments, each suggestion is associated with a set of one or more genes, and the one or more stored keywords include one or more keys corresponding to the names of the genes associated with the suggestion word.

In some embodiments, the method includes identifying one or more suggestions based in part on information in one or more reference files stored in a database of reference files.

In some embodiments, identifying one or more suggestions includes: accessing, by a processor, a database including a plurality of reference documents (e.g., published literature; for example, a plurality of web pages of a public database), wherein Each reference file is associated with one or more SNPs and / or genes associated with one or more recommendations (e.g., each reference file includes specific health related tables regarding one or more SNPs and / or genes and their effects) Type information); use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user's text query [for example, by comparing the text query with the text data in the reference document (for example, written Input terms) (for example, by extracting keywords from a text query and searching for matches in a reference file; for example, using a received text query as input and determining one or more SNPs related to the user's text query based on the reference file A machine learning module)]; for each of one or more expected suggestions, determine one or more SNPs and / or genes associated with the user's text query with the SNP and / or genes associated with the expected suggestion and / Or a degree of matching between genes; and identifying one or more suggestions based on the determined degree of matching.

In some embodiments, the method includes automatically identifying one or more suggestions based on a mutation in one of the SNPs in one of the user's genomes (eg, identified by one or more genetic profiles of the user).

In some embodiments, at least one of the identified suggestions is associated with one or more SNPs, and for each of the one or more associated SNPs, the suggestion is associated with the SNP (e.g., via a qualifier) (Identified) is associated with a particular variation, and the identification of the recommendation is based at least in part on the specific variation of one or more SNPs associated with the recommendation and the user having one or more SNPs as in one of the users or Correspondence (e.g., relationship; e.g., correlation) between specific variations identified in multiple genetic profiles.

In some embodiments, the method includes: receiving (and / or accessing) mobile health data recorded by a mobile health device of one of the users; and automatically based on the user's genetic profile and the received mobile health data Identify one or more suggestions.

In some embodiments, the one or more suggestions include a suggested genetic profile test.

In some embodiments, the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; e.g., a blood test; e.g., all tablets an examination).

In certain embodiments, the one or more recommendations include purchase recommendations for one or more supplements (eg, nutritional supplements).

In some embodiments, the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, a brain wave feedback plan, a behavior plan (e.g., A focused program, an ADHD adjuvant program), and a somatization therapy.

In some embodiments, the one or more members are individualized protocols and / or treatments based on one or more genetic profiles of the user.

In some embodiments, the one or more suggestions include one or more suggested purchases of one or more mobile health devices (and / or one or more software apps operating on a mobile health device). In some embodiments, the structured response includes information corresponding to the location and name of one of the merchants from which the user can make a purchase suggestion, and the structured response causes the voice assistant to generate a provision when executed by the voice assistant Audio output of the location and name of the business.

In another aspect, the present invention relates to a system for providing purchase suggestions corresponding to genetic profile testing through interaction between a user and an artificial intelligence chat robot. The system includes: a processor; and instructions stored thereon. A memory in which the instructions, when executed by the processor, cause the processor to: (a) receive user input of a text query; (b) identify one or more related to the text query based on the text query Genetic profile tests (e.g., using a machine learning module), where each genetic profile test is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to a specific one of one or more SNPs A set of measurements (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test); and (c) for the one or more Identify each of the genetic profile tests and provide (e.g., for visualization and / or graphical display on a user's computing device) a graphical representation including one of the genetic profile tests (e.g., , The name of the test that appears as text; for example, an image associated with the test).

In some embodiments, the instructions, when executed by a processor, cause the processor to identify one or more genetic profile tests by: each of accessing a set of predefined genetic profile tests includes the following One of the databases (eg, a set of text files, such as an AIML file): (i) an identifier of the genetic profile test [eg, a text label (eg, representing a name of the genetic profile test)]; and ( ii) one or more keywords associated with the identifier of the genetic profile test; and for each identified genetic profile test, identifying one or more of the entries in the text query with (i) the genetic profile test Character and / or (ii) at least one of the one or more keywords.

In certain embodiments, for example, each SNP of a set of SNPs for which one or more keywords are measured for a genetic profile test includes a name of a gene associated with the SNP (e.g., a gene in which a SNP occurs). A name; for example, a name of a gene whose SNP affects transcription).

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more genetic profile tests based in part on information stored in one or more reference files in a database of reference files.

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more genetic profile tests by: accessing a plurality of reference documents (e.g., a published document; e.g., a public source) A plurality of web pages of the library), wherein each reference file is associated with one or more SNPs measured by the one or more genetic profiling tests (for example, each reference file includes information about one or more SNPs and Information related to a specific health-related phenotype that affects); use a text query and the information in the plurality of reference documents to determine one or more SNPs related to the user's text query [for example, by comparing a text query with a reference in a reference document. Textual data (e.g., written words) (e.g., by extracting keywords from a text query and searching for matches in a reference file; e.g., receiving a text query as input and determining a reference to the user's text query based on the reference file One or more machine learning modules of one or more SNPs)]; for each of one or more expected genetic profile tests, determine one or more related to user text query A degree of match between the SNP set with a genetic profile of the test measurements on the expected or more of the SNP; and identifying one or more genetic profile is determined by the test based on the matching degree.

In some embodiments, the graphical representation including identification of one of the genetic profile tests includes a selectable link that is selected by the user (e.g., by a user clicking with a mouse; for example, by the user Using a touch gesture such as a touch-sensitive interface of a touch screen to face the link) leads a user to a website that allows the user to purchase a genetic profile test.

In some embodiments, the instructions, when executed by the processor, cause the processor to cause (e.g., on a computing device of a user) to display a graphical user interface (GUI) corresponding to a chat window, wherein via the The chat window GUI receives a text query and appears in the chat window GUI with a graphical representation including an identification of one of the genetic contour tests as a response to the text query.

In some embodiments, the instructions, when executed by the processor, cause the processor to interact with an interactive website (e.g., a website used by a user to view genetic profile test results; for example, thereby allowing the user to identify and purchase their website). Chat window GUI displayed within additional genetic profile test).

In some embodiments, the instructions, when executed by the processor, cause the processor to execute in an interactive app [eg, an app used by a user to view genetic profile test results (eg, executed on a mobile phone such as a mobile phone) An app on the device); for example, thereby allowing the user to identify and (eg, via in-app purchases) purchase additional genetic profile tests for which they may be interested] to display a chat window GUI.

In some embodiments, the text query is provided by a voice assistant.

In another aspect, the present invention relates to a system for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and an artificial intelligence chat robot, the system including: a processor; and A memory of instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive user input of a text query, wherein the user is associated with one or more genetic profiles [ For example, the one or more genetic profiles represent the results of a genetic profile test performed on the user; for example, where the user is a subscribed (e.g., logged in) user who has been subjected to one or more genetic profiles Tested and have one or more genetic profiles established and stored in a database accessible by the processor] association; (b) text-based queries and at least in part based on the user's one or more Genetic profile recognition in response to one or more suggestions received by the user (e.g., a number of purchase suggestions) (e.g., using a machine learning module); and (c) providing (e.g., Of Displayed and / or graphically displayed on a computing device) includes one of one or more suggested graphical representations [eg, one that appears as one of the text and is recommended for purchase (eg, a nutritional supplement; for example, one of the purchases of a mobile health device) Name; for example, an image associated with a proposed purchase, a textual description in response to a query (eg, a suggested diet plan, a suggested fitness plan, etc.)].

In some embodiments, the instructions, when executed, cause the processor to identify one or more recommendations by: accessing by the processor each of a set of predefined recommendations including a database of one of the following (Eg, a set of text files, such as an AIML file): (i) an identifier of the suggestion [eg, a text label (eg, representing a name of the suggestion)]; and (ii) the identifier of the suggestion Associated one or more keywords; and for each identified suggestion, one or more items in the text query with (i) the proposed identifier and / or (ii) the one or more keywords At least one of them matches.

In some embodiments, each suggestion is associated with a set of one or more SNPs, and one or more keywords include, for each SNP of the SNP set associated with the suggestion, a gene of a gene associated with the SNP. A name (eg, a name of a gene in which SNP occurs; for example, a name of a gene in which SNP affects transcription).

In some embodiments, at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one or A measurement of a specific set of multiple SNPs (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test) and is related to the genetic profile The test-associated SNP set is a SNP measured by a genetic profile test.

In some embodiments, each suggestion is associated with a set of one or more genes, and the one or more keywords include one or more keywords corresponding to the names of the genes associated with the suggestion.

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more suggestions based in part on information in one or more reference files stored in a database of reference files.

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more suggestions by: accessing a plurality of reference documents (e.g., published literature; e.g., a public database) A plurality of web pages), wherein each reference file is associated with one or more SNPs and / or genes associated with one or more suggestions (e.g., each reference file includes information about one or more SNPs and / or Information about specific health-related phenotypes of genes and their effects); use text queries and information within the plurality of reference documents to determine one or more SNPs related to user text queries [for example, by comparing text queries and references Textual data within the document (for example, written words) (for example, by extracting keywords from a text query and searching for matches in a reference document; for example, using a received text query as input and determining a user based on the reference document Text query related to one or more SNPs, one of the machine learning modules)]; for each of one or more expected suggestions, determine one or more SNPs and / or related to the user's text query Recommended due to the associated expected that the SNP and / or the degree of match between a gene; and based on the determination of the degree of matching by identifying one or more recommendations.

In some embodiments, the instructions, when executed by the processor, cause the processor to automatically identify a variation based on a variation in a SNP in one of a user's genomes (e.g., identified by one or more genetic profiles of the user). Or more suggestions.

In some embodiments, at least one of the identified suggestions is associated with one or more SNPs, and for each of the one or more associated SNPs, the suggestion is associated with the SNP (e.g., via a qualifier) (Identified) is associated with a particular variation, and the identification of the suggestion is based at least in part on a particular variation of one or more SNPs associated with the suggestion that the user has one or more SNPs as if they were associated with the user Correspondence (e.g., relationship; e.g., correlation) between specific mutations identified in the genetic profile of the gene.

In some embodiments, the instructions, when executed by the processor, cause the processor to: receive (and / or access) mobile health data recorded by a mobile health device of one of the users; and based on the user's genetic profile and The received mobile health information automatically identifies one or more suggestions.

In some embodiments, the one or more suggestions include a suggested genetic profile test. In some embodiments, the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; e.g., a blood test; e.g., all tablets an examination). In certain embodiments, the one or more recommendations include purchase recommendations for one or more supplements (eg, nutritional supplements).

In some embodiments, the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, a brain wave feedback plan, a behavior plan (e.g., A focused program, an ADHD adjuvant program), and a somatization therapy. In some embodiments, the one or more members are individualized protocols and / or treatments based on one or more genetic profiles of the user.

In some embodiments, the one or more suggestions include one or more suggested purchases of one or more mobile health devices (and / or one or more software apps operating on a mobile health device).

In some embodiments, the graphical representation including identification of one of the genetic profile tests includes a selectable link that is selected by the user (e.g., by a user clicking with a mouse; for example, by the user Using a touch gesture such as a touch-sensitive interface of a touch screen to face the link) leads a user to a website that allows the user to purchase a genetic profile test.

In some embodiments, the instructions, when executed by the processor, cause the processor to cause (e.g., on a computing device of a user) to display a graphical user interface (GUI) corresponding to a chat window, wherein via the The chat window GUI receives a text query and appears in the chat window GUI with a graphical representation including an identification of one of the genetic contour tests as a response to the text query.

In some embodiments, the instructions, when executed by the processor, cause the processor to interact with an interactive website (e.g., a website used by a user to view genetic profile test results; for example, thereby allowing the user to identify and purchase their website). Chat window GUI displayed within additional genetic profile test).

In some embodiments, the instructions, when executed by the processor, cause the processor to execute in an interactive app [eg, an app used by a user to view genetic profile test results (eg, executed on a mobile phone such as a mobile phone) An app on the device); for example, thereby allowing the user to identify and (eg, via in-app purchases) purchase additional genetic profile tests for which they may be interested] to display a chat window GUI.

In some embodiments, the text query is provided by a voice assistant.

In another aspect, the present invention relates to a system for providing purchase advice corresponding to a genetic profile test through interaction between a user and a voice assistant. The system includes: a processor; and one of instructions stored thereon. Memory, where the instructions, when executed by the processor, cause the processor: (a) (e.g., via a network) to receive a text query from the voice assistant (e.g., one of the processors of the voice assistant) A structured request, where the structured request is generated by a voice assistant responding to a user's voice [for example, by detecting the user's voice and generating a word corresponding to at least a portion of the user's voice Voice data and processing the user voice data to generate a text query (e.g., by generating text voice data (e.g., via a machine learning module) and storing the text voice data in one of a database Or more recognizable text phrase matches, and using at least a portion of the identified recognizable text phrases as entries in the text query)]; (b) text query identification and The user's speech is related to one or more genetic profile tests [for example, by combining a text query (eg, one or more entries in a text query) with a genetic profile test (eg, stored in a database) One or more identifier matches (e.g., via a machine learning module; e.g., by identifying one or more subroutines based on a first part of a text query and passing a second part of a text query to the Identified subroutines as variables evaluated by those subroutines to identify one or more genetic profile tests)]; and (c) (e.g., via a network) will include one or more genetic profile tests Each of the identified one or more structured responses is provided to a voice assistant, where each structured response when executed by a voice assistant (e.g., one of the voice assistant processors) causes the voice assistant to generate a corresponding response based on the structured response. An audio output of the simulated voice is provided, thereby providing the user with audio feedback corresponding to the recommendations associated with the one or more identified genetic profile tests.

In some embodiments, the instructions, when executed by the processor, cause the processor to associate a text query (e.g., one or more entries of the text query) with each one or more genetic profile tests One or more stored keyword matches to identify one or more genetic profile tests related to the user's voice.

In certain embodiments, one or more keywords for each SNP of a set of SNPs measured by one or more genetic profiling tests include a name of a gene associated with the SNP (e.g., where the SNP occurs A name of a gene; for example, a name of a gene whose SNP affects transcription).

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more genetic profile tests based in part on information stored in one or more reference files in a database of reference files.

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more genetic profile tests by: accessing a plurality of reference documents (e.g., a published document; e.g., a public source) A plurality of web pages of the library), wherein each reference file is associated with one or more SNPs measured by the one or more genetic profiling tests (e.g., where each reference file includes information about one or more SNPs and Information about specific health-related phenotypes that have been affected); use text queries and the information in the plurality of reference documents to determine one or more SNPs related to text queries [for example, by comparing text queries with text in reference documents Data (e.g., written words) (e.g., by extracting keywords from a text query and searching for matches in a reference document; e.g., using a received text query as input and determining a reference to a user's text query based on the reference document One or more SNP machine learning modules)]; for each of one or more expected genetic profile tests, determine one or more S related to user text query A match between the NP and the set of one or more SNPs of the expected genetic profile test measurement; and identifying one or more genetic profile tests based on the determined match.

In another aspect, the invention relates to a system for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and a voice assistant. The system includes: a processor; and A memory storing instructions, wherein the instructions, when executed by the processor, cause the processor: (a) (e.g., via a network) from the voice assistant (e.g., the processor of the voice assistant) ) Receive a structured request that includes a text query, where the structured request is generated by a voice assistant responding to a user ’s voice [eg, by detecting the user ’s voice and generating a voice corresponding to the user ’s voice At least a portion (up to all) of the voice data and processing the user voice data to generate a text query (e.g., by generating the text voice data, (e.g., via a machine learning module) the text voice data Matches one or more identifiable text phrases stored in a database, and uses at least a portion (up to all) of the matched identifiable text phrases as items in the text query)], and its The textual query for the structured request includes users associated with one or more genetic profiles (e.g., a subscribed user who has been tested for one or more genetic profiles and has established one or more for them Genetic profiles and stored in a database accessible by the processor); (b) text queries using structured requests are identified based at least in part on one or more genetic profiles associated with the user One or more suggestions (e.g., a purchase suggestion); and (c) (e.g., via a network) providing one or more structured responses including an identification of each of the one or more suggestions to the voice assistant, Each structured response, when executed by a voice assistant (for example, a processor of a voice assistant), causes the voice assistant to generate an audio output corresponding to the simulated voice based on the structured response, thereby providing the user with a corresponding response Audio feedback for the one or more suggestions.

In some embodiments, the instructions, when executed by the processor, cause the processor to cause a text query (e.g., one or more items of the text query) and one or more suggestions (e.g., purchase At least one of the suggestions is associated with one or more stored keyword matches to identify the one or more suggestions.

In some embodiments, each suggestion is associated with a set of one or more SNPs, and each of the SNPs of the SNP set of the one or more stored keywords for the genetic profile test measurement includes an association with the SNP A name of a gene (eg, a name of a gene in which SNP occurs; for example, a name of a gene in which SNP affects transcription).

In some embodiments, at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one or A measurement of a specific set of multiple SNPs (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test) and is related to the genetic profile The test-associated SNP set is a SNP measured by a genetic profile test.

In some embodiments, each suggestion is associated with a set of one or more genes, and the one or more keywords include one or more keywords corresponding to the names of the genes associated with the suggestion.

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more suggestions based in part on information in one or more reference files stored in a database of reference files.

In some embodiments, the instructions, when executed by the processor, cause the processor to identify one or more suggestions by: accessing a plurality of reference documents (e.g., published literature; e.g., a public database) A plurality of web pages), wherein each reference file is associated with one or more SNPs and / or genes associated with one or more suggestions (e.g., each reference file includes information about one or more SNPs and / or Information about specific health-related phenotypes of genes and their effects); use text queries and information within the plurality of reference documents to determine one or more SNPs related to user text queries [for example, by comparing text queries and references Textual data within the document (for example, written words) (for example, by extracting keywords from a text query and searching for matches in a reference document; for example, using a received text query as input and determining a user based on the reference document Text query related to one or more SNPs, one of the machine learning modules)]; for each of one or more expected suggestions, determine one or more SNPs and / or related to the user's text query Recommended due to the associated expected that the SNP and / or the degree of match between a gene; and based on the determination of the degree of matching by identifying one or more recommendations.

In some embodiments, the instructions, when executed by the processor, cause the processor to automatically identify a variation based on a variation in a SNP in one of a user's genomes (e.g., identified by one or more genetic profiles of the user). Or more suggestions.

In some embodiments, at least one of the identified suggestions is associated with one or more SNPs, and for each of the one or more associated SNPs, the suggestion is associated with the SNP (e.g., via a qualifier) (Identified) is associated with a particular variation, and the identification of the recommendation is based at least in part on the specific variation of one or more SNPs associated with the recommendation and the user having one or more SNPs as in Correspondence (e.g., relationship; e.g., correlation) between specific variations identified in multiple genetic profiles.

In some embodiments, the instructions, when executed by the processor, cause the processor to: receive (and / or access) mobile health data recorded by a mobile health device of one of the users; and based on the user's genetic profile and The received mobile health information automatically identifies one or more suggestions.

In some embodiments, the one or more suggestions include a suggested genetic profile test. In some embodiments, the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; e.g., a blood test; e.g., all tablets an examination). In certain embodiments, the one or more recommendations include purchase recommendations for one or more supplements (eg, nutritional supplements). In some embodiments, the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, a brain wave feedback plan, a behavior plan (e.g., A focused program, an ADHD adjuvant program), and a somatization therapy.

In some embodiments, the one or more members are individualized protocols and / or treatments based on one or more genetic profiles of the user.

In some embodiments, the one or more suggestions include one or more suggested purchases of one or more mobile health devices (and / or one or more software apps operating on a mobile health device).

In some embodiments, the structured response includes information corresponding to the location and name of one of the merchants from which the user can make a purchase suggestion, and the structured response causes the voice assistant to generate a provision when executed by the voice assistant Audio output of the location and name of the business.

Cross-reference to related applications

This application claims priority and rights to US Provisional Patent Application No. 62 / 502,556, filed on May 5, 2017, the entire contents of which are incorporated herein by reference. definition

To make the present invention easier to understand, specific terms are first defined below. Additional definitions of the following terms and other terms may be set forth throughout this specification.

In this application, the use of "or" means "and / or" unless stated otherwise. As used in this application, the term "comprise" and variations of that term (such as "comprising and comprises") are not intended to exclude other additives, components, integers, or steps. As used in this application, the terms "about" and "approximately" are used as equivalents. Any numbers used or not used in this application are intended to cover any normal fluctuations as understood by those of ordinary skill in the relevant art. In some embodiments, the terms "approximately" or "approximately" refer to the stated reference value unless otherwise stated or unless it is clear from the context (except where the number will exceed 100% of the possible value) 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8 in any direction (greater or less) Value range within%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or less.

Genotyping data: As used herein, the term "genotyping data" refers to data obtained from measurements of genotypes and / or results obtained from a genetic profile test (eg, genetic profile test results). A genotype measurement performed on a biological sample identifies specific nucleotide (s) (also referred to as "bases") that are incorporated at one or more specific locations in the genetic material extracted from the biological sample. Therefore, genotyping measurements for a particular individual are measurements performed on biological samples from that individual, and these measurements identify specific nucleotides that exist at one or more specific locations within the genome of the individual.

In certain embodiments, the genotyping data describes the phenotype of an individual. The genotyping data may be a measurement of a particular gene (eg, an individual's genetic sequence (eg, a portion of a DNA sequence)), a SNP, or a variation of the SNP. For example, a genotyping measurement of a particular SNP in an individual identifies a particular variation of the SNP that the individual has. A genotyping measurement of a specific gene of an individual identifies a specific nucleotide present at one or more locations within and / or near the gene of the individual. For example, a genotyping measurement of a particular gene can identify a particular variation of one or more SNPs associated with a particular gene.

In certain embodiments, the genotyping data is obtained from a multi-gene panel. In some embodiments, the genotyping data is obtained from a test (eg, a TaqMan ™ test) that detects one or more specific mutations of a particular SNP. In certain embodiments, the genotyping data is obtained from genetic sequencing measurements. In certain embodiments, the genotyping data is generated in response to an individual purchase or request. In certain embodiments, the genotyping data includes data for a portion of (eg, an individual's) genotype. In certain embodiments, the genotyping data includes all available measurements of (e.g., individual) genotypes.

Supplement: As used herein, the term "supplement" refers to a product that is ingested, consumed, and / or administered by a user for at least one of the following: improving physical and mental health, improving performance or function, and counteracting chronic conditions Influence. Supplements can be vitamins, multivitamins, minerals, dietary supplements, herbs, botanicals, concentrates, metabolites, extracts, amino acids, over-the-counter medicines, prescription drugs, topical products, or health / treatment regimens or programs. In certain embodiments, a user takes the supplement regularly (eg, once a day or twice a day) over a period of time. A period of time may be the basis for continuing on for one of the unscheduled stop periods. In some embodiments, the supplement is a scheme or health care system that a user can register or purchase. For example, the supplement may be a behavioral program, such as a concentration program or a personalized fitness program (eg, for use in home fitness exercises).

Variation: As used herein, the term "variation" refers to a specific variation of a particular SNP that occurs in the genetic material of a population. In certain embodiments, the variant is the first allele of the first copy of the genetic material of the individual (eg, the DNA corresponding to the father of the individual) and the second allele of the second copy of the genetic material of the individual ( For example, a particular combination of DNA corresponding to an individual's mother) occurs in a diploid organism (eg, a human).

Qualifier: As used herein, the term "qualifier" refers to the classification (eg, label) of a particular variation of a given SNP. A qualifier associated with a given variation is a particular classification (e.g., label) of that variation. For example, a given variation may be associated with a particular qualifier of one of a set of predefined possible qualifiers. For example, a given variation may be associated with a qualifier (such as "adapt", "normal", and "feature") selected from a label group. In certain embodiments, for a given variation of a given SNP, the qualifier corresponds to (i) the prevalence of the given variation within the population (e.g., is the variation common; for example, is the variation rare) and / Or (ii) the classification of the given variation of health-related traits associated with the variation. For example, a common variation can be associated with the qualifier "normal". Rare variants that confer adverse phenotypes, such as high cholesterol susceptibility, can be associated with the qualifier "adapt" (eg, classified as rare and unfavorable). Rare variations that confer a favorable phenotype, such as lower cholesterol susceptibility, may be associated with the qualifier "feature" (eg, the variation is classified as rare and favorable, respectively).

Variant: As used herein, the term "variant" refers to a data structure that corresponds to (eg, used to represent) a particular mutation of a physical gene within a given genome (eg, the human genome).

Voice Assistant: As used herein, the term "voice assistant" refers to a device that provides audio interaction with an individual (eg, a user of the voice assistant). For example, as opposed to providing digital input (for example, by typing a text item into a GUI), a user can speak to a voice assistant and be provided with computer-generated speech as feedback. For example, as described herein, a structured request including a text query may be generated by a voice assistant in response to a user's voice. For example, a voice assistant may generate a voice query by detecting and / or recognizing a user's voice, generating voice data corresponding to at least a portion of the detected / recognized voice, and processing the voice data to generate a text query. Structured response. The text query may be provided (e.g., received by a computing device) to a computing device (e.g., via a network). Furthermore, in some embodiments, the voice assistant may deliver an audible analog voice response to one of the text queries (eg, in response to a suggestion or other information from a user query). In some embodiments, a voice assistant includes a hardware component near a user for detecting audible speech from the user. A voice assistant may include a processor and / or processing may be performed remotely (e.g., a signal corresponding to the detected voice may be transmitted electronically (e.g., via a network)).

SNP object: As used herein, the term "SNP object" refers to a data structure that corresponds to (eg, used to represent) a particular single nucleotide polymorphism (SNP). In some embodiments, the SNP object includes a SNP reference identifying a specific SNP corresponding to the SNP object. The SNP reference can be an alphanumeric code (such as the recognized name of the SNP) or other identification mark or label that can be stored electronically. The SNP reference can be an alphanumeric code, such as a National Biotechnology Information Center (NCBI) database reference number.

Genetic object; as used herein, the term "genetic object" refers to a data structure corresponding to (eg, used to represent) a particular physical gene within a given genome (eg, the human genome).

Category: As used herein, the term "category" refers to a data structure that corresponds to (eg, used to represent) a particular health-related trait or characteristic.

Product, genetic profile product, personal genetic profile product: As used herein, the terms "product", "genetic profile product" and "personal genetic profile product" refer to (e.g., used to represent) general health-related traits And / or characteristic data structure. In some embodiments, a product is associated with one or more categories that correspond to health-related traits and characteristics related to the health-related traits and characteristics of the general category corresponding to the product.

Genetic profile assessment: As used herein, the term "genetic profile assessment" refers to data that corresponds to (eg, used to represent) a phenotype for one or more users of one or more general health-related traits and / or characteristics Structure (for example, data structure hierarchy). In some embodiments, a user-to-user genetic profile assessment is generated by correlating the user's genotyping data with a pre-made (eg, stored) universal genetic profile product. In some embodiments, a user's genetic profile evaluation is viewed using an evaluation graphical user interface ("evaluation GUI") on a computing device (eg, a smartphone).

Developer: As used herein, the term "developer" refers to a person, company, or organization that uses a graphical user interface to create a data structure. In some embodiments, a developer also genotypes the biological sample in response to an evaluation corresponding to one of the products purchased or available to the individual.

User: As used herein, the term "user" refers to a person who uses an evaluation graphical user interface to view information about the genome. The user may provide one or more biological samples to be genotyped for evaluation of a genetic profile to be formed. The user may purchase or be allowed to use one or more products to view a genetic profile assessment. The user may purchase one or more supplements from one of the purchase advice lists provided in the graphical user interface based on the user's genetic profile assessment. The terms "user" and "individual" are used interchangeably herein.

Graphic control items: As used herein, the term "graphic control items" refers to items of graphical user interface items that can be used to provide user and / or individual input. Graphic control items can be text boxes, drop-down lists, option buttons, data fields, check boxes, buttons (for example, optional icons), list boxes, or sliders.

Associated with: As used herein, the terms "associated" and "associated with" in a first data structure associated with a second data structure refer to (eg, in computer memory A computer representation of the association between two data structures or data elements stored electronically.

Provide: As used herein, the term "provide" as used in "provide data" refers to a process for transferring data between different software applications, modules, systems, and / or databases. In some embodiments, providing data includes executing instructions by a procedure that transfers data between software applications or between different modules of the same software application. In some embodiments, a software application can provide data to another application in the form of a file. In some embodiments, an application can provide data to another application on the same processor. In some embodiments, standard protocols can be used to provide data to applications on different resources. In some embodiments, a module in a software application can provide data to a module by passing arguments to another module.

Mobile health device: As used herein, the term "mobile health device" refers to the various types of information that a user uses to record data such as biological / physical measurements and activities related to physical health-related activities performed by them Any of the mobile devices. Data recorded with a mobile health device is referred to herein as "mobile health data." In some embodiments, mobile health data includes measurements (such as weight, glycemic index, recorded calorie intake), and information about physical activity, such as the average number of steps taken during a given period of time Or aggregated data, recorded training (eg, recorded by a fitness monitoring software app running on a mobile health device), sleep quality data, and brain wave data (eg, EEG measurements). In some embodiments, the mobile health device is a network-connected device such that the mobile health data recorded by a given mobile health device can be passed through a network by a processor (e.g., of another computing device) Access and / or receive. In some embodiments, a mobile health device includes an activity tracking device (e.g., a device for monitoring fitness, step count, pulse rate, sleep, eating, or other activities), a mobile phone (e.g., a smart phone), a tablet Computers, brain activity monitoring devices (eg, devices for monitoring mental focus, alertness, stress, relaxation, sleep, or the like), connected home devices (eg, the scale of an internet connection).

The systems, architectures, devices, methods and procedures of the claimed invention are expected to cover changes and adaptations using information development from the embodiments described herein. The adaptations and / or modifications of the systems, architectures, devices, methods, and procedures described herein may be performed as described.

In the entire description in which articles, devices, systems, and architectures are described as having, including, or including specific components, or in which procedures and methods are described as having, including, or including specific steps, it is anticipated that the present invention may otherwise be substantially Articles, devices, systems, and architectures composed of, or composed of, the described components, and in addition, there is a program according to the present invention consisting essentially of, or composed of, the described processing steps. And methods.

It should be understood that as long as the invention remains operational, the order of the steps or the order used to perform a particular action is not important. In addition, two or more steps or actions can be performed simultaneously.

Any reference to a publication (eg, in the [Prior Art] paragraph) herein is not an admission that the publication is prior art relative to any of the claims made herein. [Prior Art] paragraphs are presented for clarity and are not intended to be a description of the prior art with respect to any claim. As mentioned, the documents are incorporated herein by reference. In the event of any discrepancy in the meaning of a particular term, the meaning provided in the [Definition] paragraph shall prevail.

Headers are provided for the convenience of the reader and are not intended to be restrictive on the subject matter claimed.

This article proposes to allow a user to interact with an artificial intelligence chat robot to automatically identify genetic profile tests of their interest, and based at least in part on the user's genetic profile test results (e.g., as stored in his / her / their (Genetic profile) A system and method for automatically identifying recommendations for health and fitness products and / or programs personalized to the user. These recommendations may include, for example, additional diagnostic tests (e.g., additional genetic profiling tests; e.g., tests for specific characteristics, traits, diseases, and / or conditions), nutritional supplement purchase recommendations, and recommendations that are well suited for the use Recommendations for specific programs (for example, diet programs, fitness programs, etc.) and similar.

In some embodiments, the systems and methods described herein allow a user to naturally chat with a chat robot by providing an input through a familiar messaging interface, as shown in FIG. 1A, FIG. 1B, FIG. 1C, Screen shots 100, 105, 110, 115, 120, 125, 130, 135, and 140 of FIGS. 1D, 1E, 1F, 1G, 1H, and 1I are shown. As shown in FIGS. 1A to 1I, a user can type a numeric input into a chat window as if she / he is talking to a human, and the user then receives a relevant response from the artificial intelligence chat robot. In some embodiments, the user input may be provided as voice rather than text typed into a GUI. The voice is detected by a voice assistant, which processes the detected voice and provides a text query to the chat robot.

In some embodiments, an artificial intelligence-enhanced chatbot: (i) decrypts what information a user requests (eg, from a written or spoken query); (ii) formulates products, services, and services based on the user's query And / or solutions; and (iii) provide textual and / or audio feedback to communicate these suggestions. In some embodiments, before providing a textual and / or audible feedback to transmit a suggestion, the suggestion is also formulated by the chatbot using information about the user's stored genetic profile.

In some embodiments, a chatbot allows individuals to easily purchase one or more of the suggested products, services, and / or programs. For example, as described herein, various genetic profiling tests can be purchased to provide information about an individual's genotype. A specific genetic profile test can measure (eg, one or more) a specific set of single nucleotide polymorphisms (SNPs) to determine which specific variation of an SNP an individual has. In some embodiments, the artificial intelligence chat robot may provide and purchase other products based on the SNP set and / or other information in the genetic profile (e.g., nutritional supplements, diet plans, (several) behavioral plans, ( Several) one or more recommendations related to the app, diagnostic tests for specific diseases and / or conditions, and additional genetic profiling tests). A. Chatbot for identifying genetic contour tests

In some embodiments, the artificial intelligence chatbot described herein responds to a text query to provide suggestions and / or other information related to personal genetic profile testing. For example, as shown in FIGS. 1A to 1I, a user types text input corresponding to various phrases used in (eg, with a human) a normal conversation. Chatbots provide relevant responses to mimic a conversational interaction. In the illustrative examples of FIGS. 1A to 1I, the chatbot identifies a genetic profile test that the user is interested in by analyzing a text query associated with the user ’s digital input. As shown in Figure ID, the chatbot causes a graphical representation of a genetic profile test (product) to appear on the messaging interface display. In this example, the genetic contour test is named "AURA", and the graphical representation that appears appears to include a picture associated with the test. The chatbot also provides one of the graphical user interfaces with the option to link to "BUY NOW". The user can select the link (for example, by clicking on it with a mouse or tapping on the link with a touch-sensitive interface) to transfer to a web page where the user can purchase one of the suggested tests . Ai receives user input

Referring now to FIG. 2, a method 200 is an exemplary method of providing purchase recommendations corresponding to a genetic profile test through the interaction of a user with an artificial intelligence chat robot according to an illustrative embodiment. In step 202, a processor of a computing device receives user input of a text query. The chatbot systems and methods described herein may use various inputs from a user (represented by a text query) to determine an appropriate genetic profile test to suggest to the user. For example, a user may use a graphical user interface (GUI) to enter text queries or ask questions with varying degrees of specificity relative to the hierarchical organization of products, categories, genes, and SNPs used to represent genetic profile tests and their results. , As described herein (eg, contained in paragraph C below). In some embodiments, the user input may be an audible voice from the user, and a voice assistant may process the voice input to generate a text query, as described in more detail below. A.ii. Recognition genetic profile test

Still referring to FIG. 2, at step 204 (eg, using a machine learning module), one or more genetic profile tests are identified based on the received text query. The identified genetic profile test is related to the text query. Each genetic profile test is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one of a specific set of one or more SNPs. For example, each corresponding SNP may affect a particular health-related trait associated with the general health-related phenotype associated with the genetic profile test.

In some embodiments, one or more genetic profile tests are identified according to step 204 shown in FIG. 2 by accessing a database (eg, a set of text files, such as an AIML file). The database (for each of a set of predefined genetic profile tests) includes an identifier of the genetic profile test and one or more keywords associated with the identifier of the genetic profile test. For example, the identifier may be a text label (eg, a name representing a genetic profile test). For each identified genetic profile test, one or more entries in the text query are then matched with (i) the identifier of the genetic profile test and / or (ii) at least one of the one or more keywords . In some embodiments, one or more keywords for each SNP of a set of SNPs measured by a genetic profile test include a name of a gene associated with the SNP (e.g., a name of a gene in which SNP occurs) ; For example, the name of one of the genes whose SNP affects transcription).

For example, a chatbot may analyze a user's input to match the words entered by the user with names that can then be presented for purchase by the user for one or more genetic profile tests. The name of the genetic profile test can be stored in a database of text files, such as artificial intelligence markup language (AIML) files. The text files in the database can be formatted to allow specific words and patterns to be identified in a user's input and match the response provided by the chatbot in the messaging interface. Examples of parts 300 and 350 of the AIML file are shown in Figures 3A and 3B, respectively.

As shown in FIG. 3A, the code <pattern> AURA </ pattern> provides the name of the genetic profile test identified in the user input. The matching response "The AURA skin assessment decodes information in your unique DNA, giving you unprecedented insights about your skin. Would you like to know more about ＜ set name =" topic "＞ aura ＜ / set ＞?" Prompts the user to confirm their wait Product interest. Part of the code shown in Figure 3B confirms a user's interest in the "AURA" genetic profile test and provides a response describing one of the tests and provides the test for purchase.

In some embodiments, the user input may also match keywords associated with a particular genetic profile test in order to identify one or more genetic profile tests of interest to the user and provide those genetic profile tests for purchase. One method similar to the method described with reference to FIG. 3A can be used to match various keywords to a specific genetic profile test.

In certain embodiments, each genetic profile test is associated with a general health-like phenotype and corresponds to a measurement of one of a particular set of one or more SNPs. For example, each SNP can affect a particular health-related trait associated with the general health-related phenotype associated with the genetic profile test. Thus, a keyword associated with a given genetic profile test may include the name of the gene associated with the SNP measured by the genetic profile test, and the name associated with a specific health-related phenotype associated with the genetic profile test. Keywords.

In some embodiments, the data architecture described herein (eg, in paragraph C below) may be used to associate a genetic profile test with a particular keyword. Specifically, as described herein, the product data structure can be used to represent general health-related phenotypes, and to store and affect various SNPs and specific traits within a given health-related phenotype represented by a given product Gene association. The product data structure can also contain additional information, such as descriptions of various SNPs and phenotypes of their effects. A given genetic profile test may be associated with a particular product profile structure (e.g., in fact, the product profile structure represents the genetic profile test). The names of genes and other keywords can be extracted accordingly from the product data structure associated with a given genetic profile test and used to (for example, automatically) populate a database of text files. In some embodiments, the chat robot also directly or alternatively accesses a database of a product data structure.

In addition, items in a user question (e.g., as contained in a text query) may match various data structure elements of a hierarchical structure (described in paragraph C herein). A list of specific items and associated elements of the hierarchical structure can be stored and used for direct matching. These lists can also be used in conjunction with evaluating a term in a text query to identify one of the machine learning modules of the proposed genetic profile test (for example, to train the machine learning module).

In some embodiments, a database of reference files is used to identify a specific genetic profile test (product) based on a user input that the user is interested in buying. For example, in some embodiments, a reference file associated with a particular SNP and / or gene is learned and compared to the user input to determine one or more related SNPs and / or that are of interest to the user gene. This method can be performed by a machine learning module that receives a text query as input and determines one or more SNPs related to the user's text query based on a reference file.

For example, one or more genetic profile tests may be identified based in part on information stored in one or more reference files in a database of reference files. For example, one or more genetic profile tests may be identified according to step 204 of FIG. 2 by accessing a database including a plurality of reference documents (eg, published documents; for example, a plurality of web pages of a public database). Each reference file is associated with one or more SNPs measured via the one or more genetic profile tests. For example, each reference includes information about one or more SNPs and their specific health-related phenotypes that affect them.

The text query and the information in the plurality of reference documents can then be used to identify one or more SNPs related to the user's text query [for example, by comparing the text query with the text data in the reference document (eg, written words) ]. For example, keywords can be extracted from a text query and matches in a reference file can be searched. For example, a machine learning module that receives one text query as input and determines one or more SNPs related to the user text query based on a reference file may be used. For each of the one or more expected genetic profile tests, a degree of match between one or more SNPs related to the user's text query and a set of one or more SNPs measured with the expected genetic profile test may then be determined . One or more genetic profile tests can then be identified based on the match. A.iii. Graphical representation of ( several ) identified genetic profile tests

Referring again to FIG. 2, at step 206, the processor (for each of the one or more identified genetic profile tests) provides (e.g., for visualization and / or graphical display on one of the user's computing devices) including the One of the genetic profiling tests identified a graphical representation. For example, the identification of the genetic contour test may include (eg, appear as text) the name of the test and / or an image associated with the genetic contour test. In some embodiments, the graphical representation including the identification of the genetic profile test includes a selectable link that is selected by the user (e.g., by a user using a mouse click; for example, by the user using A touch gesture such as a touch-sensitive interface of a touch screen facing the link) leads a user to a website that allows the user to purchase a genetic profile test.

In some embodiments, the processor causes (eg, on a computing device of a user) to display a graphical user interface (GUI) corresponding to a chat window. In some embodiments, instead of using a GUI, a voice assistant provides text queries in response to a user's audible voice. In both embodiments, a text query may be received (eg, via the chat window GUI or the voice assistant), and a graphical representation containing one of the recognitions of the genetic profile test is displayed within the chat window GUI as a response to one of the text queries. For example, the processor may cause a chat window GUI to be displayed in an interactive website (eg, a website used by a user to view one of the genetic profile test results). This may allow users to identify and purchase additional genetic profile tests that may be of interest. The processor may cause, for example, a chat window GUI to be displayed within an interactive app. The interactive app may be an app (eg, executed on a mobile device such as a mobile phone) used by a user to view the results of a genetic profile test. This may allow the user to identify and (e.g., via in-app purchases) purchase additional genetic profile tests that may be of interest to the user.

For example, a user may ask questions about which tests will inform them about general health-related traits and characteristics (such as skin, fitness, nutrition, and behavior). Therefore, a user question (and a text query included in a structured request received by a processor via a GUI) related to various general health-related traits and characteristics represented by different products. Terms can be used to identify associated genetic profile tests that are represented by specific products.

A user may ask more specific questions, for example, asking about the category of related genes as represented by the category in the hierarchical organization described herein. Therefore, user items associated with the category (such as skin moisturization, longevity, physical performance, and vitamins) can be used to identify genetic profile tests that measure SNPs included in the category associated with the item in the user's question.

Similarly, a user can ask about specific traits about specific genes, SNPs, and their effects. For example, a user may be interested in knowing whether they have a strong appetite for genetic susceptibility (for example) to help them manage their weight. This user can ask about which test will tell them about appetite. As described herein, appetite is affected by the rs17782313 SNP that occurs in the FTO gene. This SNP is, for example, measured in the FUEL ^™ (or nutrition) test and is associated with the test accordingly through the hierarchical organization shown in Figure 6 (described in paragraph C below). Therefore, the FUEL ^™ (or nutrition) test can be suggested to the user.

Therefore, by receiving a structured request via a GUI, the systems and methods described herein determine one or more suggested genetic profile tests. Once the one or more suggested genetic profile tests are determined, they can be included in a structured response displayed in the GUI. The structured response may include identification of one of the one or more suggested genetic profile tests, along with any additional information that the system will use to generate a response to one of the users. A.iv. Audio interaction with a voice assistant

In some embodiments, the systems and methods described herein provide, for example, audio-based user interaction through a voice assistant. A user can speak within the detection range of a voice assistant and be provided with computer-generated voice as feedback, instead of providing alphanumeric input in a GUI chat window by typing by typing. In this way, by interacting with a user through a voice assistant, the chatbot technology described herein allows a user (eg, via a voice assistant) to speak and simulate a conversation with the chatbot. Based on a user's voice input, the user is provided with one or more suggested genetic profile tests. The explanatory genetic profile test described in paragraph C below with reference to FIG. 4 may be referred to by an alternative name. For example, the AURA ^™ test is sometimes referred to herein as a Beauty test; the FITCODE ^™ test is sometimes referred to as a Fitness test; and the FUEL ^™ test is sometimes referred to as a Nutrition test.

Referring now to FIG. 4A, method 400 is an exemplary method of providing purchase recommendations corresponding to genetic profile testing through interaction between a user and a voice assistant. In step 402, a processor of a computing device (for example, via a network) receives a structured request including a text query from the voice assistant (for example, a processor of the voice assistant). The structured request is generated by a voice assistant responding to the user's voice. For example, a structured request can be generated by detecting the user's voice and generating voice data corresponding to at least a portion of the user's voice and processing the user's voice data to generate a text query. For example, text voice data can be generated (e.g., via a machine learning module) to match the text voice data with one or more identifiable text phrases stored in a database, and use such At least a part of the matched identifiable text phrase is used as an item in the text query to process user voice data.

At step 404, the processor uses the text query of the structured request to identify one or more genetic profile tests related to user input. For example, the processor may match a text query (e.g., one or more entries of the text query) with one or more identifiers (e.g., stored in a database) of a genetic profile test. Matching can be performed via a machine learning module. In some embodiments, one or more subroutines are identified based on a first part of a text query and then a second part of a text query is passed to the identified subroutines as by the subroutines Assessed variables to identify one or more genetic profile tests.

In some embodiments, step 404 according to FIG. 4A identifies one or more genetic profile tests by matching a text query (eg, one or more terms of the text query) with one or more stored keywords. , Where each keyword is associated with one or more genetic profile tests. In certain embodiments, one or more keywords for each SNP of the set of SNPs measured by the (and other) genetic profile test include a name of a gene associated with the SNP (e.g., one of the SNPs occurring within One name of a gene; for example, one name of a gene whose SNP affects transcription). In some embodiments, the genetic profile test (e.g., as above) is identified (e.g., as above) based in part on information stored in one or more reference files stored in a database of the reference file. Described in paragraph A.ii with reference to step 204 of method 200 shown in FIG. 2).

Still referring to FIG. 4A, in step 406, the processor provides one or more structured responses to the voice assistant (e.g., via a network). The one or more structured responses include identification of each of the one or more genetic profile tests identified in step 404. When each structured response is performed by a voice assistant (eg, by one of the voice assistant processors), the voice assistant generates an audio output corresponding to the simulated voice based on the structured response. This provides users with audio feedback corresponding to recommendations associated with the one or more identified genetic profile tests.

In some embodiments, in order to interact with a user through a voice assistant, the systems and methods described herein receive and send structured requests and structured responses, respectively. Specifically, in order to evaluate and provide a response based on the user's voice, a structured request is received from the voice assistant. The structured request includes a text query that can be analyzed by a processor to generate an appropriate response. The voice assistant may generate a structured request by detecting a user's voice and generating data (such as text) corresponding to the detected user's voice. The generated data may then be analyzed to generate a text query and / or various terms included therein. These concepts are explained in more detail below. In some embodiments, these concepts may be applied in the context of deriving a text query from a digital input received by a user (eg, via a graphical user interface GUI).

For example, the voice assistant can be provided with several sets of structured question phrases. Each set of structured question phrases corresponds to a specific question that the user intends to ask, that is, a user intention. For example, each structured question phrase of the set of structured questions may indicate a particular change in what a user wants to say when asking the specific question. In other words, a set of structured question phrases takes into account the fact that countless different specific questions can be asked for a single specific purpose or user intent.

For example, one of the users who is interested in which genetic profile test should be purchased to understand their skin health can ask "What test will tell me about my skin?", "What genetic profile test will tell me about my Skin health? "," What genetic profiling test should I buy to understand skin health? "And more. The voice assistant may use a change list in the form of a specific question to account for the variability of the exact words spoken by a user. The voice assistant may be provided with a list of phrases that are grouped together, such as in association with specific user intent. A voice assistant may include processing instructions and modules (e.g., artificial intelligence), such as a machine learning module, to allow for powerful evaluation of user issues. In particular, in some embodiments, the voice assistant does not need to rely on a one-to-one match between a word spoken by the user and a stored structured question phrase to accurately determine the user's intention.

The voice assistant can contain multiple sets of structured question phrases. These structured question phrases may be stored by the voice assistant (e.g., in the memory of the voice assistant) or may be accessed by the voice assistant (e.g., via a network).

Therefore, the voice assistant can use different sets of structured question phrases to detect and analyze the user's voice to determine one of the users' specific intentions. One of the user's intentions identified based on the user's voice may then be included in a text query of a structured request provided by a voice assistant. For example, if a user asks a question based on a structured question phrase that corresponds to knowing which genetic profile test will inform the user about one of their genotypes' intent to affect skin health, the voice assistant A pre-defined keyword (such as WhatTestSkinHealth) can be included in the text query of the structured request.

In some embodiments, the structured question phrase may include a constant, base portion, and a variable portion. For example, a user may be interested in various genetic profile tests that inform them about different parts of their genotype and the health-related traits and characteristics affected by those parts. Therefore, a structured question phrase can be used, such as What test will tell me about {test type}? Has a constant base part, What test will tell me about and a variable part, {test type} can be used by a voice assistant Evaluate the user's voice to determine if the user intends to connect to the same or more variable key entries. Therefore, the voice assistant can provide a structured request with a text query that includes one or more predefined keywords indicating the determined user's intent, with the same or multiple variable key entries.

For example, if a user asks "Which test will tell me about the skin?", A predefined intent keyword (such as WhatTest) with the same variable skin may be included in the text query of the structured request. If a user asks "Which test will tell me about fitness?", The same predefined intent keyword WhatTest is included in the text query together with the different variable fitness. As described herein, changes to the structured question phrase can be used to allow the voice assistant to determine user intent even when the user changes their way of asking a particular question. For example, another structured question phrase (such as Which test will help me with {test type}?) May be included in the list provided to the voice assistant. In this way, if the user asks a similar question (such as "Which test will help me with the skin?"), The appropriate intent keywords (eg, WhatTest) and variables (eg, skin) are still determined. In this way, multiple changes that include questions in the same form as the structured question phrases in a list accessible to the voice assistant allow the voice assistant to accurately determine the user's intentions and provide One of the variable parts of the question is a structured response to a text query. The structured response and the text query may be processed by the systems and methods described herein to determine one or more genetic profile tests that are suggested to the user and included in a structured response provided to the voice assistant.

In particular, in some embodiments, the systems and methods described herein receive a structured request from a voice assistant and process the structured request to determine one or more genetic profile tests to suggest to a user. The systems and methods described herein may use the user intent keywords included in the textual query of the structured request received from the voice assistant along with any variable key entries to determine the one or more suggested genetics Contour test.

For example, based on a structured request including a text query with an intent keyword WhatTest and a variable skin, the chatbot technology described herein may determine that the beauty test is a suggested genetic profile test. Evaluable includes a structured request with a text query having an intent keyword WhatTest and a variable fitness to determine the fitness test as a suggested test.

The chatbot system and method described herein may use various inputs from the user as represented in a text query in order to determine an appropriate genetic profile test to suggest to the user. For example, users can ask questions with varying degrees of specificity relative to the hierarchical organization of products, categories, genes, and SNPs used to represent genetic profiling tests and their results, as described herein.

For example, a user may ask questions about which tests will inform them about general health-related traits and characteristics (such as skin, fitness, nutrition, and behavior). Therefore, various items in a user question (and included in a text query of a structured request received from a voice assistant) that are associated with different general types of health-related traits and characteristics represented by different products can be used for Identify associated genetic profiling tests represented by specific products.

An item in a user question (eg, in a text query) can be matched with a structured response provided by a voice assistant. For example, the various data structure elements of the hierarchical architecture described herein (in paragraph C) may be associated with one or more structured responses. The list of specific items and associated elements of the hierarchical structure can be stored and used to directly match text queries and / or directly match structured responses. These lists may also or alternatively be used in conjunction with evaluating a term in a text query to identify one of the machine learning modules of the proposed genetic profile test (e.g., to train the machine learning module).

Therefore, by receiving a structured request from a voice assistant, the systems and methods described herein determine one or more suggested genetic profile tests. Once the one or more suggested genetic profile tests are determined, they can be included in a structured response provided to the voice assistant to allow the voice assistant to respond to the user. The structured response may include identification of one of the one or more suggested genetic profile tests, along with any additional information or speech that the voice assistant will use to respond to the user-generated speech.

For example, a user may ask a question orally, such as "What test will tell me about the skin?". The voice assistant can detect and process the user's voice to determine the intended keyword WhatTest and the variable skin. The intent keyword WhatTest and the variable skin are included in a text query provided to the chatbot system and method described in this article and receiving a structured request by them. The intent keyword WhatTest and the variable skin are evaluated to identify the cosmetic (ie, AURA ^™ ) test as a suggested genetic profile test. One of the structured responses containing text, such as the text box shown below, can then be provided to the voice assistant. Beauty DNA tests decode the information in your unique DNA and give you unprecedented insights into your skin. Discover how your genes affect your skin's moisture, aging, elasticity, and UV sensitivity with this 18-gene test, so you can better care for your skin. The structured response may also include other elements that the voice assistant uses to respond to the user (such as code that defines the nature of speech, such as intonation). The voice assistant evaluates the structured response and responds to the user via simulated voice.

In this manner, the systems and methods described herein can interact with a user to guide them to provide them with purchase recommendations for genetic profile testing. Examples of various structured question phrases, suggested genetic profile tests, and text of the structured responses provided are shown in Table 1 below. Other structured question phrases and structured responses that are not necessarily directly related to the recommendations of the genetic profile test can also be used. These structured question phrases and structured responses can be used to inform users about their tests, testing procedures, and to facilitate conversations with chatbots, such as those mediated by voice assistants. This conversation may help encourage a user to purchase one or more specific genetic profile tests. Examples of structured question phrases and the text of structured responses elicited from them are shown in Table 2 below. Table 1. Structured question phrases, identified genetic profile tests, and structured responses provided. The variable part of the structured question phrase is indicated in parentheses, where specific values of the variable part are shown (for example, the specific genetic profile test and structured response shown in the associated project for which it is determined). Question phrases are shown in bold text . The identified genetic profile test and corresponding response text are shown below. Table 2. Examples of structured question phrases and structured response text for additional conversations with users. Question phrases are shown in bold text , and the corresponding response text is shown below. B. Criteria for identification (s) of the product, the recommendation (s) of service and / or a (several) embodiment of the bot

In some embodiments, the systems and methods described herein allow for identification based on a user's genetic profile (e.g., the product (s), service (s), and / or ( (Several) of the proposals). For example, a recommendation may include purchasing a set of nutritional supplements, a diet plan, a behavioral plan, diagnostic tests for specific diseases and / or conditions, apps, and additional genetic profiling tests. For example, the proposal may include any of the purchase recommendations described in this document and detailed in PCT application no. One. Bi Identification and advice based on user genetic profile data

Suggestions may be identified and provided in a manner similar to the method used to identify the proposed genetic profile test described in paragraph A above, and may also include (e.g., by accessing a user's genetic profile assessment, (Described in paragraph C) uses the information in the user's genetic profile. For example, suggestions can be identified based on a variation of one of the SNPs in one of the user's genomes (eg, as identified by the user's genetic profile), or other genotyping data stored in the user's genetic profile. Various methods that utilize user genetic profile information to determine purchase recommendations, such as PCT application number PCT / US2017 / 067277 and the purchase recommendations described herein, can be used.

Once a user has performed a genetic profile test, genotyping data from the genetic profile test can be stored in a secure (eg, password-protected) user genetic profile. The genotyping data identifies a particular variation a user has for a particular set of SNPs associated with a genetic profile test. As described herein, variations in various SNPs possessed by a user affect various health-related traits and characteristics, such as joint health, metabolism, and ability to process specific foods.

Based on the various SNP variations (and / or other genotyping data) an individual has, specific supplements or combinations thereof may be useful to the individual. For example, if an individual has a specific variation of one of the SNPs that causes him or her to gain weight easily (for example, a specific variation of one of the SNPs of the ADIPOQ gene), taking supplements that help manage or prevent weight gain and obesity for that Individually very valuable. For example, an individual can benefit from taking a vitamin A supplement if the individual has a specific mutation that is one of the SNPs that reduces his or her ability to convert beta-carotene to retinol. Similarly, depending on whether an individual has specific SNP variations that affect longevity, joint health, muscle recovery, endurance and lean physique, and skin health, different supplements can be identified that are beneficial to the individual.

Based on genotyping measurements, such as obtained through a genetic profile test and stored in the individual's genetic profile assessment, various related supplements that are particularly beneficial to the individual can be identified. The identified supplements can be provided to the user via a user's interaction with an artificial intelligence chat robot as described herein.

Referring now to FIG. 4B, method 410 is an exemplary method of providing consumer feedback (eg, purchase suggestions) corresponding to one or more genetic profile tests through interaction between a user and an artificial intelligence chat robot. In step 412, a processor of a computing device receives user input of a text query. The text query may be provided as text input by a user by typing into a GUI or by a voice assistant in response to audible speech from the user. The user is associated with one or more genetic profiles (eg, one or more genetic profiles representing the results of a genetic profile test performed on the user). For example, the user may be a subscribed (e.g., logged in) user who has been tested for one or more genetic profiles and has one or more genetic profiles established and stored in the processor. Access one of the databases.

In response to the user input received in step 412, in step 414 (e.g., using a machine learning module) based on a text query and (e.g., automatically) identify one or more based on at least one of the user's genetic profiles or Multiple suggestions (for example, purchase suggestions).

At step 416, one or more suggestions are provided to the user via the artificial intelligence chat robot. Similar to the way in which the suggested genetic profile test can be provided to a user via a GUI (such as in a chat window) or via audio interaction through a voice assistant, or via a GUI and / or voice The assistant provides purchase suggestions to a user. In some embodiments, a graphical representation including an identification of a genetic profile test includes a selectable link that is selected by a user (e.g., by a user using a mouse click; for example, by using (Using a touch gesture such as a touch-sensitive interface of a touch screen to face the link), a user is then directed to a website that allows the user to purchase a genetic profile test. In some embodiments, a voice assistant can facilitate the purchase of a genetic profile test.

In some embodiments, identified by the processor's access to a database (e.g., a set of text files, such as an AIML file, such as a portion of the exemplary AIML file shown in Figures 3A and 3B) (e.g., At step 414) of method 410) one or more suggestions. The database contains, for each of a set of predefined recommendations, an identifier [eg, a text label (eg, representing a name of the recommendation)]. The database also includes one or more keywords associated with the identifier of the suggestion for each of a set of predefined suggestions. For each identified suggestion, the processor matches one or more entries in the text query with at least one of (i) the proposed identifier and / or (ii) the one or more keywords. In some embodiments, each suggestion is associated with a set of one or more SNPs, and one or more keywords include a gene associated with the SNP for each SNP of the SNP set associated with the suggestion A name (eg, a name of a gene in which SNP occurs; for example, a name of a gene in which SNP affects transcription).

In some embodiments, at least one of the one or more suggestions is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one of a particular set of one or more SNPs One of the measurements is the genetic profile test. For example, each corresponding SNP may affect a particular health-related trait associated with the general health-related phenotype associated with the genetic profile test. The set of SNPs associated with the genetic profile test is the SNP measured by the genetic profile test.

One or more suggestions may be identified based in part on information stored in one or more reference files stored in a database of reference files (eg, step 414 of method 410 shown in FIG. 4B). For example, one or more suggestions are identified by the processor accessing a database containing a plurality of reference documents (e.g., published literature; for example, a plurality of web pages of a public database). Each reference file is associated with one or more SNPs and / or genes to which one or more recommendations are associated. For example, each reference file contains information about one or more SNPs and / or genes and their specific health-related phenotypes that affect them.

Then use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user's text query [for example, by comparing the text query with the text data in the reference document (for example, the written word) ]. For example, keywords may be extracted from a text query and the processor may search for a match for these keywords in a reference file (e.g., using a received text query as input and based on the reference file to determine one or more related to the user's text query One of the machine learning modules of SNP). For each of the one or more expected suggestions, a degree of matching between one or more SNPs and / or genes related to the user's text query and the SNPs and / or genes associated with the expected suggestion is determined. One or more suggestions are identified based on the determined match.

In some embodiments, each suggestion is associated with a set of one or more genes, and the one or more keywords correspond to the name of a gene associated with the suggestion. In some embodiments, the one or more suggestions include a suggested genetic profile test. In some embodiments, the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; e.g., a blood test; e.g., all tablets an examination).

In some embodiments, the processor causes (eg, on a computing device of a user) to display a graphical user interface (GUI) corresponding to a chat window. A text query is received via the chat window GUI and a graphical representation containing a recognition of a genetic profile test appears in the chat window GUI as a response to the text query. In some embodiments, the processor causes a chat window GUI to be displayed within an interactive website (eg, a website used by a user to view one of the genetic profile test results). This thereby allows the user to identify and purchase additional genetic profile tests that may be of interest to the user. In some embodiments, the processor causes a chat window GUI to be displayed within an interactive app. The interactive app may be an app (eg, executed on a mobile device such as a mobile phone) used by a user to view the results of a genetic profile test. This may allow the user to identify and (e.g., via in-app purchases) purchase additional genetic profile tests that may be of interest to the user. Bia uses a voice assistant to provide a text query

Referring to FIG. 4C, method 420 is an exemplary method of providing consumer feedback corresponding to one or more genetic profile tests via interaction of a user with a voice assistant according to an illustrative embodiment. In step 422, a structured request is received from a voice assistant (e.g., a local or remote processor of the voice assistant) by a processor of a computing device (e.g., via a network). An illustrative example of a structured request is described in paragraph A above. Generally, a structured request includes a text query and is generated by a voice assistant in response to a user's voice. For example, the structure can be generated by detecting the user's voice and generating voice data corresponding to at least a part (up to all) of the user's voice and processing the user's voice data to generate a text query. request. For example, text voice data can be generated (e.g., via a machine learning module) to match the text voice data with one or more identifiable text phrases stored in a database, and use such At least a part (up to all) of the matched identifiable text phrases are used as items in the text query to process user voice data. The structured request text query includes identification of one of the users associated with one or more genetic profiles. For example, a user associated with a genetic profile may be a subscribed user, one or more genetic profile tests have been performed on it, and one or more genetic profiles have been established and stored in the processor. Access one of the databases.

At step 424, the processor uses the text query of the structured request to identify one or more suggestions (eg, purchase suggestions). For example, the one or more suggestions may include a suggested genetic profile test. The one or more suggestions are identified based at least in part on one or more genetic profiles associated with the user. In some embodiments, step 424 includes associating one or more text queries (e.g., one or more entries of the text query) with at least one each of one or more suggestions (e.g., purchase suggestions). Stored keywords match.

In some embodiments, each suggestion is associated with a set of one or more SNPs, and each of the SNPs of the SNP set of the one or more stored keywords for the genetic profile test measurement includes an association with the SNP A name of a gene (eg, a name of a gene in which SNP occurs; for example, a name of a gene in which SNP affects transcription). Automatically identify one or more suggestions based on a mutation in one of the SNPs in one of the user's genomes (eg, identified by one or more genetic profiles of the user). In some embodiments, one or more suggestions are identified based in part on information stored in one or more reference files in a database of reference files (e.g., as described above with reference to method 410 in FIG. 4B Described in step 414).

In some embodiments, at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one or A measurement of a specific set of multiple SNPs (eg, where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test). The set of SNPs associated with the genetic profile test is the SNP measured by the genetic profile test. For example, each suggestion may be associated with a set of one or more genes, and one or more keywords may correspond to the name of a gene associated with the suggestion.

At step 426, the processor provides (eg, via a network) one or more structured responses to the voice assistant. The one or more structured responses include identification of each of the one or more suggestions, and each structured response causes the voice assistant when executed by a voice assistant (e.g., a local or remote processor of the voice assistant) An audio output corresponding to the simulated voice is generated based on the structured response. Therefore, audio feedback corresponding to the one or more suggestions is provided to the user. In some embodiments, the structured response includes information corresponding to the location and name of one of the merchants from which the user can make a purchase suggestion, and the structured response causes the voice assistant to generate a provision when executed by the voice assistant Audio output of the location and name of the business.

In some embodiments, at least one of the identified suggestions is associated with one or more SNPs, and for each of the one or more associated SNPs, the suggestion is associated with the SNP (e.g., via a qualifier) Identify) a particular mutation. The suggestion is based, at least in part, on the specific variation of one or more SNPs associated with the suggestion and the user having one or more SNPs (e.g., as identified in one or more genetic profiles of the user) Correspondence between variations (e.g., relationship; e.g., correlation).

As described herein, for example, one or more of the recommendations may include a suggested diagnostic test (eg, a test for a particular disease and / or condition, such as a blood test for cholesterol index; for example, a blood test; for example, One slice check). The one or more recommendations include purchase recommendations for one or more supplements (eg, nutritional supplements). In some embodiments, one or more suggestions may include: a diet plan, an all-around fitness plan, an electroencephalogram feedback plan, a behavior plan (eg, a concentration plan, an ADHD assisted plan), and / or a body plan Chemical treatment. These suggestions may be individualized protocols and / or treatments based on one or more genetic profiles of the user. For example, one or more suggestions include one or more suggested purchases of one or more mobile health devices (and / or one or more software apps operating on a mobile health device). B.ii. Storage recommendations

Proposals (eg, suggested purchases) may be stored and associated with various genetic profile items such as the genetic profile items described herein (eg, in paragraph A) in order to utilize the individual's genetic profile assessment to identify related purchases. In some embodiments, the proposed purchase item (e.g., corresponding to the data structure of the proposed purchase) is established by a developer and based on the corresponding suggested purchase and corresponding genome components (e.g., genes, SNPs, and / Or mutations) are associated with the data structure (eg, genetic objects, SNP objects, and / or mutation objects) in a user's genetic profile assessment. Accordingly, automatic identification may include invoking or identifying one or more of the stored associations. In some embodiments, a suggested purchase is identified by searching through a database of all possible suggested purchases using one of the data including a genetic profile assessment from a user.

For example, a purchase suggestion item indicating a specific possible purchase recommendation may be stored in a purchase recommendation database. Each purchase suggestion item stored in the purchase suggestion database is associated with one or more stored variation items. The stored variation object associated with a specific purchase suggestion object represents a specific variation of various SNPs, and for these specific variation suggestions, a possible suggestion purchase indicated by the specific purchase suggestion object is suggested. For example, a purchase suggestion item that represents one of the vitamin A supplements may be associated with a stored variation item that represents a specific variation of one of the SNPs that causes a reduction in an individual's ability to convert β-carotene to retinol.

A user's genotyping data (e.g., as stored in their genetic profile assessment) can then be used to query a purchase suggestion database to identify specific suggested purchases that would be of benefit to them. In particular, the user's genotyping data indicates the results of genotyping measurements performed on a biological sample from the user to determine specific variations of various SNPs present in their genome. These results can be represented in the genotyping data through a plurality of user-specific mutation objects, and each user-specific mutation object indicates that the user has a specific mutation of a specific SNP in his or her genome.

Therefore, user-specific variants can be matched with stored variants. Mutated objects can be matched based on the measurement results and / or qualifiers associated with them. Purchase suggestion objects associated with stored variants of user-specific variants that match genotyping data can thus be identified to determine a set of possible recommendations for purchase. One or more suggested purchases may then be selected from the set of possible proposed purchases determined. In some embodiments, all possible suggested purchases may be selected. In some embodiments, additional criteria (such as user assessment results, cost, availability for users, whether a particular suggested purchase conflicts with others) may be used to determine one or more possible suggested purchases from the set Select one or more suggested purchases.

In some embodiments, suggestions are provided via a GUI, such as a chat window, by displaying a graphical representation of a suggested purchase within the chat window. The graphical representation may include one or more icons and / or text for display within an evaluation GUI, or it may include a link that a user chooses to access a separate GUI for viewing purchase suggestions (e.g., , One button, hyperlink, selectable icon). In some embodiments, a user may directly purchase suggestions using the chat window GUI.

For example, in some embodiments, the purchase of a particular suggested purchase by a user is performed by displaying a selectable button corresponding to the particular suggested purchase and bringing the selectable button to a predefined website of a particular merchant. A link (eg, a link to a web page) is associated and promoted. In this manner, a user of one of the selectable buttons selects a purchase initiated by the user for a particular suggested purchase corresponding to the selectable button. In some embodiments, users can store other sets of information that can be automatically provided to merchant sites. For example, they may store addresses and payment information (eg, credit card information) in a secure database. After selecting selectable buttons for purchasing a particular suggested purchase, the systems and methods described herein access user information and automatically provide it to the merchant site. In some embodiments, all the information required for the purchase is stored and automatically provided to the merchant site so that the user can complete the purchase with one of the selectable buttons (eg, without any further user interaction required).

In some embodiments, the suggestions provided to the user via a voice assistant may also include prompting the user to type the number of products purchased, payment information, and the like, so that the user can directly communicate with the voice via them The assistant's interaction confirms the purchase and purchase suggestions. In some embodiments, the user payment is stored and a user may purchase a specific suggested purchase only through affirmative orders to one of the voice assistants.

In some embodiments, one or more of the identified suggestions are personalized based on the user's genetic profile. In some embodiments, at least some of the suggestions as suggested purchases provided to a user are directed to a plan and / or treatment that the user can personalize (eg, personalize). These suggested purchases can be personalized based on text queries received by the user and / or genetic profile of the user. For example, a fitness plan that is generally suggested to a user based on the genotype (s) of a user may be further personalized for the user based on one or more specific genotypes. More specifically, once again, for example, an all-in-one fitness plan may be proposed based on several traits of a user, but certain specifics in this fitness plan make the user more likely to experience arthritis and / or pain. Fitness exercises can be replaced based on the particular phenotype of the user. As an additional example, a diet plan suggested to a user based on a health-related phenotype suggesting that the user has a glucose sensitivity may be based on the user's lactose intolerance (e.g., from a text query and / or the user's Genetic profiling) and modified to exclude dairy products from this protocol. B.iii. Example purchase recommendations

Additional illustrative examples of suggestions that can be provided by chatbots in accordance with the systems and methods described herein are described below.

In some embodiments, a diet profile can be determined for a user using a diet profile based on a user's textual query and / or genetic profile (e.g., their genotyping data) . The diet profile for the user indicates guidelines and / or taste preferences for the user and includes a set of user-specific diet labels (eg, alphanumeric strings) identifying common diets and / or allergens. For example, diet labels (such as "vegan", "vegan", "fish vegetarian", "low cholesterol", "dairy-free", "lactose-free", "gluten-free", "raw diet", "low sugar" "And the like) can be used to identify various diets suggested based on user genotyping data. For example, diet labels (such as "dairy products", "peanuts", "nuts", "gluten", and the like) can be used to identify a user's genotyping data. Results indicate that the user is allergic to it and / or textual query instructions Allergens that the user does not want to consume. Diet labels can be determined from user genotyping data based on their association with specific variations of various different SNPs and / or qualifiers that classify these variations.

For example, SNPs associated with FADS1, KCTDIO, and PPARg affect cholesterol and fat storage indexes. Therefore, based on the presence of a mutation and / or qualifier of any of the SNPs associated with these genes in a user's genotyping data, a label such as "low cholesterol" can be added to A diet outline. Can store various diet labels and their associations with variants and / or qualifiers that identify and / or classify specific possible variants of various SNPs, respectively, so that a diet profile can be derived from (i) genotyping data Automatic matching between user-specific variants and / or user-specific qualifiers and (ii) stored variants and / or stored qualifiers is filled in with diet labels.

Once determined, the user's diet profile can be used to identify dietary recommendations and specific recipes suggested to the user. For example, in some embodiments, a dietary database includes a plurality of predefined dietary plans each associated with one or more plan specific diet labels. The user-specific diet labels of the user's diet profile can be matched with the program-specific diet labels to identify the user-recommended diet plans stored in the diet database. The identified meal plans can include multiple recipes from which users can choose to follow a diet that will be beneficial to their health.

In some embodiments, the dietary database includes a plurality of recipes each associated with one or more recipe-specific diet labels. The user-specific diet labels of the user's diet profile can be matched with the recipe-specific diet labels to identify user-recommended recipes stored in the diet database. One or more of these suggested recipes can be automatically selected and combined to create a customized meal plan for the user.

In some embodiments, the dietary database includes a list of ingredients for various recipes that can be queried. Based on the list of ingredients for a particular recipe, the systems and methods described herein can determine whether the particular recipe conforms to one or more of the diets identified by the user's specific diet label and / or does not include the use of the one or more Any allergen identified by a user-specific diet label. This method of querying the ingredients list of a recipe can be used instead of or in conjunction with querying diet-specific diet labels.

In some embodiments, the customized meal plan contains information about various recipes it includes, such as the title of the recipe and pictures of it, and the like. In some embodiments, the title of the recipe and / or pictures thereof are shown graphically. In some embodiments, the customized meal plan includes a user may subscribe to obtain a list of ingredients and / or one of the cooking processes of one or more recipes included in the customized meal plan. In some embodiments, graphics and / or text corresponding to the ingredient list and / or cooking process of one or more recipes are graphically presented for presentation to a user.

In some embodiments, the customized meal plan includes one or more specific restaurants and / or food delivery services (e.g., participating restaurants and / or participating food delivery services that provide recipe information stored in a meal database) An identification, a user may obtain at least one of the suggested recipes through such restaurants and / or food delivery services.

In some embodiments, a customized fitness plan is identified and suggested to the user. In some embodiments, the customized fitness program includes one or more suggested training categories identified based on the user's genotyping data and / or a text query thereof as suggested to the user ( For example, provided in a user's area; for example, provided by a participating merchant (e.g., a gym). The training category identification can be stored in a training category database. Each training category may be associated with one or more mutation objects and / or qualifiers that respectively represent a specific variation of a specific SNP and / or classify a specific variation of a specific SNP. User-specific mutation objects and / or qualifiers in the user's genotyping data and / or other text queries can be matched with the stored mutation objects and / or qualifiers to identify relevant training categories. For example, SNPs associated with the COL5a1 gene affect joint strength and flexibility. Specific variants of the SNPs associated with the COL5a1 gene make individuals susceptible to reduced flexibility, high blood pressure, and risk of injury during certain types of fitness exercises. Thus, for example, specific training categories that provide low-impact stretching and flexibility fitness exercises may be associated with mutated objects and / or qualifiers that correspond to such variations, making it possible to suggest such specifics to users who would benefit from them Training category. Similarly, a text query may include an indication of a user's preference for a given fitness activity. For example, a measurement based on the text query to determine a user's health status (eg, injured) can be used to suggest low-impact training to the user.

In some embodiments, a body fitness profile similar to one of the diet profiles described above may be determined for the user based on the user's genotyping data and / or a text query thereof. The physical fitness profile may include identification based on the user's genotyping data that is determined by the processor to be associated with the user (e.g., beneficial to the user) (e.g., suggested to the user; e.g., the user should Avoided) A set of user-specific fitness labels (e.g., alphanumeric strings such as "HIIT", "aerobic", "aerobic", "high-intensity", "flexibility") Similar). The user-specific fitness tags can then be used to query a training category database including one of a plurality of training categories, each training category being associated with one or more program-specific fitness tags. By matching the user-specific fitness tag with the program-specific fitness tag, the relevant training categories can be identified by their association with the matching program-specific fitness tag.

Once identified, one or more suggested training categories can be provided for presentation to the user. In some embodiments, graphics and / or text corresponding to a suggested training category are graphically presented for presentation to a user. In some embodiments, indicates additional information associated with the suggested training category [e.g., provides one or more times in the category; for example, provides one or more locations in which the category is located (e.g., a particular gym Location); for example, the cost of the category; for example, the graphics and / or text used to register a link to the category] are graphically presented to the user.

In some embodiments, for example, a suggestion is provided based on the identification of location data (e.g., GPS coordinates) provided by a user (e.g., via their mobile computing device (e.g., a cellular phone; a smart watch)). The location of the training category in the gym near the user. In some embodiments, the user's location data is used in conjunction with providing identified locations for gyms with suggested training categories to provide the user with a map showing locations of nearby gyms, directions to nearby gyms, and the like. For example, a list of nearby gyms, maps, directions, and the like can be displayed on the user's mobile computing device.

Where the genetic profile is based on SNP variations associated with the identified traits, one of the products or the A combination. The following are exemplary genetic traits that can be used as part of a genetic profile (e.g., notified by an associated, identified SNP variation determined from a biological sample of a user). B.iii.a. Weight management

Genetic traits associated with weight management that can (for example, at least in part) be identified based on SNP mutations include, for example: weight gain, food reward, satiety, appetite, obesity, hunger, preference for sweets, fatty acid sensitivity, age Related metabolism, lipid metabolism, fat processing capacity, monounsaturated fat and sugar sensitivity. In some embodiments, based on genetic profile results of a user relative to one or more of these traits, automatic identification may be provided to the user as an optional recommendation (e.g., purchase recommendations; e.g., in-app purchases). One or more of the following supplements presented. For example, customized supplement packs (e.g. garcinia cambogia, CLA, raspberry ketone, green tea extract, green coffee bean extract, carbohydrate and fat blockers, safflower seed oil, subgenus Phalaenopsis, and / Or meal replacement). B.iii.b. Daily support

Genetic traits that may be (e.g., at least in part) identified based on SNP mutations that are associated with an individual's need for vitamins and / or that individual's ability to effectively use vitamins include, for example, beta-carotene (vitamin A), vitamin B ₁₂ , Vitamin D, Folic acid index, Vitamin B ₆ , Vitamin E and Vitamin C. In some embodiments, based on the genetic profile results of a user relative to one or more of these traits, the system automatically identifies the use as an optional suggestion (e.g., purchase suggestion; e.g., in-app purchase) to the use Present one or more of the following supplements. For example, customized supplement packs (eg, multivitamins, multivitamin B, folic acid and adenosylmethionine (Sam-E), vitamin A, vitamin C, vitamin D, and / or vitamin E) may be suggested. B.iii.c. Longevity

For example, genetic traits associated with longevity can be identified based on individual SNP variations. In some embodiments, based on a user's genetic profile results, the system automatically identifies one or more of the following supplements that can be presented to the user as an optional suggestion (e.g., purchase suggestion; e.g., in-app purchase). . For example, customized supplement packs (e.g., sedge acetate, curcumin, turmeric, rhodiola, carnitine, and / or N-acetamidine cysteine) may be suggested. B.iii.d. Joint health and sports recovery

Genetic traits associated with an individual's joint health and ability to recover from exercise include (for example): joint strength and flexibility, joint health and injury, muscle strength, muscle strength, cardiopulmonary capacity, exercise recovery, intensity training, and blood flow regulation . In some embodiments, based on the genetic profile results of a user relative to one or more of these traits, the system automatically identifies the use as an optional suggestion (e.g., purchase suggestion; e.g., in-app purchase) to the use Present one or more of the following supplements. For example, customized supplement packs [eg, joint health supplements (glucosamine chondroitin sulfate, fish oil, MSM and / or collagen), and / or muscle recovery supplements (branched chain amino acids (BCAA), bran Amino acid and / or whey protein powder)]. B.iii.d. Endurance and lean constitution

Genetic traits associated with an individual's endurance and lean constitution include, for example: cardiac output, oxygen capacity, maximum oxygen uptake, muscle function, energy output, muscle efficiency, cardiopulmonary capacity, blood flow regulation, lean physique and muscle mass . In some embodiments, based on the genetic profile results of a user relative to one or more of these traits, the system automatically identifies the use as an optional suggestion (e.g., purchase suggestion; e.g., in-app purchase) to the use Present one or more of the following supplements. For example, a customized supplement pack may be suggested [eg, creatine, caffeine, β-alanine, sodium phosphate, NO ₂ (arginine), and / or pre-training supplements]. B.iii.e. Skin health

Genetic traits associated with an individual's skin health include, for example ,: sun sensitivity, skin protection, skin renewal, complexion, skin health, photoaging, and skin moisturization. In some embodiments, based on the genetic profile results of a user relative to one or more of these traits, the system automatically identifies the use as an optional suggestion (e.g., purchase suggestion; e.g., in-app purchase) Present one or more of the following supplements. For example, a customized supplement pack may be suggested [eg, biotin, vitamin E, fern extract (sun protection), evening primrose, black currant oil, collagen, and / or phytoceramide].

For any of the above examples, in some embodiments, a specific formulation of a suggested supplement may also be automatically identified and presented to a user based on the user's genetic profile and / or textual query. In some embodiments, (e.g., via a food delivery service) based on a user's genetic profile results, one or more suggested meal plans are automatically identified for presentation and presentation to the user. In some embodiments, one or more suggested fitness strategies, brainwave feedback schemes (e.g., for stress relief), and / or behavioral schemes (e.g., concentration schemes) are automatically identified based on a user's genetic profile results , ADHD assistance, improved mental acuity scheme, MCI prevention scheme and / or Alzheimer prevention scheme) to appear and present to the user. B.iv. Automated identification back end for recommendations related to personal genetic profile

In certain embodiments, (eg, as a result of presenting a user's genetic profile) a personal genetic profile product identifies and presents recommendations for other products, services, and / or protocols associated with the user's specific genetic profile. For example, in some embodiments, since a developer uses a suggestion to build a back end (e.g., to create a graphical user interface), a set of stored suggestions is used to determine a nutritional supplement suggestion and present it to a user. A developer can manually or automatically upload a set of purchase suggestion objects (ie, a data structure corresponding to the suggestion) in order to make the purchase suggestion available to a user. For example, a developer may upload a set of supplement purchase recommendations for a variation range of SNPs corresponding to weight management. When a user views other genetic profile evaluations that include (in this example) a weight management genetic profile product, they may then proceed to view the group corresponding to the particular variation the user has from the set uploaded by the developer. Buying advice.

In some embodiments, the set of suggestions is indexed and stored so that it can be queried based on a user's genetic profile assessment. In some embodiments, the additional information associated with an item in a genetic contour product includes a suggestion for one of the plurality of items (eg, where the purchase suggestion item defines an optional link). In some embodiments, the proposed object is associated with a hierarchy of genetic data structure so that when a user's genetic profile assessment is formed from the user's genotyping data and a hierarchy of genetic data structure, the relevant associated suggestions are automatically ( Indirectly) associated with the genetic profile assessment of the user.

In some embodiments, a developer uses a graphical user interface to create new suggested objects and associate them with existing objects in a genetic profile product. Referring now to FIG. 5, method 500 is an exemplary method for establishing a proposed article associated with a stored article in a genetic profile product. At step 502, a developer presents a graphical user interface item for creating a suggestion object. The developer enters data into the graphical user interface item for inclusion in the suggestion object. The portion of the graphical user interface item allows a developer to select one or more stored genomic objects associated with the established suggested objects. Once a genomic object is stored, any data structure in the base of a data structure that defines a genetic profile product (eg, as described in paragraph C below). At step 504, a processor of a computing device receives a purchase suggestion object containing one of the data entered by the developer and a selection of one or more stored genomic objects by the developer. At step 506, the processor associates the one or more stored genomic items with the purchase suggestion item. At step 508, the processor stores the purchase suggestion object and the association for further update or retrieval (eg, to populate an evaluation GUI with the purchase suggestion).

One of the graphical user interface items provided to a developer for creating a purchase suggestion object includes one or more graphic control items for entering information related to the purchase suggestion corresponding to the purchase suggestion object. For example, graphical controls can be provided to type a name or title of a purchase suggestion, descriptive text and information, a hyperlink (if the purchase suggestion is provided to the user on a separate website interface or GUI), and a The buyer displays an icon of the purchase suggestion. In some embodiments, a graphical user interface item provides a developer with one or more graphical control items (e.g., drop-down lists) to select a previously created purchase suggestion object and associate it with a stored genomic object (E.g., for updating purchase recommendations for a particular genotype or health-related phenotype based on new research or guidelines).

A purchase recommendation item may be associated with any stored item of a genetic contour product. In some embodiments, a purchase suggestion item is most often associated with a mutated item because a specific purchase suggestion is only applicable to users who have a specific variation of an SNP. For example, a user with a neutral variation corresponding to one of the SNPs of joint pain will not experience increased joint pain or an increased likelihood of joint pain. Therefore, associating a purchase recommendation article for one of the anti-inflammatory supplements with this joint pain SNP article will cause this particular user to receive an unnecessary purchase recommendation for one of the anti-inflammatory supplements. In contrast, a user with a "fit" defined variation (eg, having a higher susceptibility to joint pain or increased joint pain) would benefit from this supplement recommendation. In some embodiments, if a suggested supplement is considered to be beneficial to all or most users (without a particular variation of any user), then a suggested item and a SNP item, a genetic item, a category, or a product are purchased Associated.

In some embodiments, the purchase suggestion object may include data input from a developer that causes a purchase suggestion that is usually displayed to a user with a variation of the SNP to be hidden from the view of a user (if the The user has a specific variation of another SNP). A user may receive a purchase suggestion based on the possession of a particular health-related trait in the absence of all other genotyping information. However, due to a different health-related trait, the user may not receive the same purchase suggestion because the suggested supplement will confer or increase the likelihood of conferring negative effects based on the different health-related trait. For example, if a user's phenotype makes it easy for the user to build muscle mass, but the user's phenotype also makes the user sensitive to sugar, based on data entered by the developer, it may not be shown to the user One of the recommendations for building a muscle mass supplement is usually provided because the supplement is high in sugar and the user has a sugar allergy. Interaction between Bv and mobile health devices

In some embodiments, the systems and methods described herein provide interaction with one or more mobile health devices of a user. Mobile health devices can be used to record health information about a user. Data recorded by a mobile health device (e.g., mobile health data) includes one of the user's biological / physical measurement ranges (such as their weight, glycemic index, brain activity (e.g., as measured by an EEG), and the like ), And information about activities performed by users (such as physical activity index and diet). Biological / physical measurements can be performed via devices such as a network connection scale and wearable brain activity monitoring devices (e.g., wearable devices capable of recording an EEG signal). Physical activity can be measured by mobile health devices (such as activity tracking devices and smartphones) that allow a user to record and track data about activities (such as training, sleep, and meals) through various apps. A given mobile health device may record one or more biological / physical measurements and / or activity measurements. Mobile health data may be recorded in an automated manner and / or in conjunction with a user's interaction with a mobile health device.

Mobile health information about a user may be received and / or accessed by the systems and methods described herein and used in conjunction with the user's genotyping data (e.g., genetic profile assessment) to provide purchases to the user Suggest and / or update purchase suggestions and / or provide feedback on their activities to users.

For example, any of the methods described above for identifying purchase recommendations to a user based on genotyping data can be extended by incorporating mobile health data in addition to genotyping data in the identification process . For example, if a user's genotyping data indicates that they are prone to obesity, and their mobile health data (e.g., via an activity monitor or a smartphone record) shows that they have a low physical activity index, then A recommendation corresponding to one of the all-in-one solutions can be identified.

In some embodiments, a suggested purchase for one user identification includes one or more mobile health devices related to another suggested purchase. For example, if, for a given user, identifying one of the integrated fitness plans is recommended for purchase, one or more mobile health devices (such as activity trackers) that promote the user's ability to adhere to the fitness plan are also identified. Or a specific smartphone app). Similarly, in some embodiments, a suggested purchase corresponding to one of an electroencephalogram feedback scheme may be linked to one or more suggested purchases corresponding to a wearable electroencephalogram monitoring and / or meditation aid. It is recommended to purchase products that can be in the form of hardware, software, or a combination of hardware and software. C. Storage and presentation of genetic profile assessment

The data architecture utilized by the chatbot has a relationship and hierarchical structure that provides one of several benefits to the systems and methods described herein. For example, each of the various genetic profile tests is linked to a particular category and / or characteristic of an individual, and these categories and / or characteristics are in turn linked to specific measurable genotypes (e.g., related to those categories and / or characteristics) Specific SNPs and / or genes). The structure of this data allows to reliably determine an individual's genetic profile assessment and to reliably store the results of that determination in an efficient search (for example, in a database) to allow the chatbot identification described in this article to target users Related recommendations. The relationship between Ci products, categories, SNP objects and genetic objects

In some embodiments, to not only provide individuals with an assessment of their genetic profile, but also to convey in an organized and intuitive manner information about specific traits and characteristics affected by specific SNP mutations present in their genetic material, The systems and methods described herein include a data architecture that includes an intuitive hierarchical organization of data structures. This architecture provides the storage (e.g., association) of specific SNPs, biological traits, and characteristics with these traits and characteristics of the general class based on specific traits affected by each specific SNP.

In some embodiments, the first (eg, top-level) data structure (referred to herein as a product) is used to represent health-related traits and characteristics of different general classes. In some embodiments, a product data structure corresponds to a specific evaluation that was ordered (e.g., purchased by an individual), where (e.g., via genotyping) identifies the specific generic category that the individual has an impact on the corresponding product representation Unique versions of genes and / or SNPs for health-related traits and traits.

Referring now to FIG. 6, in some embodiments, each product has a name that provides a convenient and memorable way to refer to the product [eg, a product data structure includes a name (eg, textual data representing the name) ]. For example, a particular product 612 (eg, named "FUEL ™" or "Nutrition") is used to indicate a type of trait that corresponds to the way an individual's body handles different foods and nutrients. Another product 614 (for example, named "AURA ™" or "Beauty") is used to indicate a trait corresponding to skin health. Another product 616 (for example, named "FITCODE ™" or "Physical Fitness") is used to indicate a type of trait corresponding to physical fitness. Another product 618 (for example, named "SUPERHERO ™") is used to indicate a type of trait corresponding to physical and intellectual performance. In some embodiments, the name of a product is the same as the name used to sell a particular evaluation. For example, Evaluation FUEL ™, FITCODE ™, AURA ™, and SUPERHERO ™ are sold by Org3n Corporation of Boston, Massachusetts.

In some embodiments, each product is then associated with one or more of a second type of data structure (referred to as a category). In some embodiments, each category corresponds to a particular health-related trait or characteristic (e.g., food allergies, food breakdown, hunger and weight, vitamins, skin ultraviolet (uv) sensitivity, endurance, metabolism, joint health, muscles Strength, wisdom). In some embodiments, each category associated with a particular product corresponds to a difference related to a general health-related trait or characteristic (e.g., a general health-related trait or characteristic represented by the product) corresponding to the specific product. Health-related traits or characteristics. As with products, in some embodiments, each category has a name that provides a convenient and memorable way to refer to one of the categories [eg, a category data structure includes a name (eg, textual data representing the name)].

Each class is then associated with one or more SNP objects, and each SNP object corresponds to a specific SNP. Each SNP object associated with a specific category corresponds to a specific SNP that affects a specific health-related trait related to the trait or characteristic corresponding to that specific category. Each SNP object can identify its corresponding SNP via the SNP object including an SNP reference. The SNP reference can be an alphanumeric code (such as one of the recognized names of the SNP) or other identification mark or label that can be stored electronically. The SNP reference can be an alphanumeric code, such as a National Biotechnology Information Center (NCBI) database reference number.

For example, the schematic diagram of FIG. 6 shows a block diagram 600 of an example of a series of products, categories, and SNP objects associated with each other. Associated genetic objects that will be described below are also shown. Different products and categories are identified by their specific names, and SNP objects are each identified by a respective SNP reference including one. In the example of FIG. 6, the SNP reference is the NCBI database reference number.

"FUEL ™" products 612 and certain categories (such as "Food Sensitivity" 622, "Food Breakdown" 624, "Hunger and Weight" 626 and "Vitamins" 628) Association. A number of SNP objects corresponding to specific SNPs that affect characteristics related to an individual's sensitivity to different types of food and correspondingly associated with the "food allergy" category 622 are shown. In FIG. 6, lines connecting SNP objects to different classes indicate the association of each particular SNP object with one or more different classes. Such associations may be direct or indirect (eg, through an interconnection with an intermediate data structure not shown).

For example, SNP object 642 corresponds to an rs671 SNP that affects the way an individual handles alcohol. In particular, depending on the particular variation of the rs671 SNP that the individual has, the individual's ability to process alcohol or its ability to handle alcohol is impaired and may suffer from adverse reactions such as flushing, headache, fatigue, and disease. Thus, providing individuals with knowledge of specific variations of the rs671 SNPs they possess may allow them, for example, to change their behavior accordingly by paying attention to their alcohol consumption (eg, on a regular basis; for example, in a social setting).

Other SNP objects are displayed that correspond to SNPs that affect characteristics related to food allergy and are accordingly associated with "Food Allergy" category 622. For example, SNP object 644 corresponds to rs762551 SNP that affects caffeine metabolism, SNP object 646 corresponds to rs4988235 SNP that affects lactose intolerance and SNP object 648 corresponds to rs72921001 SNP that affects aversion to herb coriander (for example, depending on the individual having For this particular variation of SNP, individuals may consider the fragrant cuisine to be delicious or taste bitter like soap).

In some embodiments, multiple SNPs are associated with a particular characteristic, and accordingly, SNP objects corresponding to the SNPs can be combined together. For example, three SNPs—rs713598 (corresponding to SNP object 650a), rs10246939 (corresponding to SNP object 650b), and rs1726866 (corresponding to SNP object 650c), affect individuals' bitter taste foods (for example, cabbage, broccoli, cauliflower, kale, Brussels sprouts and kale) are sensitive and accordingly they like or dislike these foods.

SNPs correspond to specific locations within or near genes in an individual's genetic material (for example, SNPs can occur in a promoter region that affects the transcription of a particular gene; for example, SNPs can be upstream or downstream of a particular gene5 occurs within 100 kb; for example, SNP can occur within 100 kb upstream or downstream of a specific gene; for example, SNP can occur within 500 kb upstream or downstream of a specific gene; for example, SNP can occur upstream or downstream of a specific gene Occurs within 1 Mb). Therefore, in some embodiments, as shown in FIG. 6, each SNP object is associated with a genetic object that is one of the specific genes corresponding to the SNP in or near which the SNP object appears. For example, the rs671 SNP corresponds to a position within the ALDH2 gene; the rs762551 SNP corresponds to a position within the CYP1A2 gene, the rs4988235 SNP occurs within the MCM6 gene, and the rs72921001 SNP occurs within the OR10A2 gene. Therefore, the SNP object 642 (corresponding to the rs671 SNP) is associated with the gene object 662 (corresponding to the ALDH2 gene). Similarly, SNP object 644 (corresponding to rs762551 SNP) is associated with genetic object 664 (corresponding to CYP1A2 gene), SNP object 646 (corresponding to rs4988235 SNP) is associated with gene object 666 (corresponding to MCM6 gene), and SNP object 648 (corresponding to rs72921001 SNP) is associated with genetic object 668 (corresponding to OR10A2 gene).

Other SNP objects correspond to SNPs that are near a particular gene of interest and thereby affect the characteristics associated with the representation of the gene. For example, rs12696306 is a 1.5 kb SNP downstream of the TERC gene and affects the biological aging associated with the TERC gene. Thus, in one example, a SNP object corresponding to an rs12696306 SNP is associated with a genetic object corresponding to a TERC gene.

In certain embodiments, multiple SNPs of interest occur within a single gene. For example, three SNPs related to bitterness—rs713598, rs10246939, and rs1726866 occur within the TAS2R38 gene. Therefore, the SNP objects 650a, 650b, and 650c corresponding to the rs713598 SNP, rs10246939 SNP, and rs1726866 SNP, respectively, are all associated with the gene object 670 corresponding to one of the TAS2R38 genes.

In some embodiments, different products correspond to different general classes of health-related traits and properties. For example, the product may be based on a specific organ (eg, product 614 named "AURA ™" is related to skin health) or specific habits, activities or physical functions. For example, food-related biological characteristics and traits can be covered by a single product or multiple products. A single product or multiple products can be based on learning and brain function characteristics and traits. A single product or multiple products may be based on physical fitness (eg, cardiovascular strength, agility, flexibility, and / or muscle strength).

For example, as shown in FIG. 6, another product 616 (eg, named "FITCODE ™") is related to general fitness-related physical fitness traits and correspondingly includes endurance 630 ("Endurance"), metabolism 632 (`` Metabolism ''), the individual's ability to recover effectively after exercise 634 (`` Exercise Recovery '') and cardiovascular fitness and skeletal muscle composition 636 (`` Power Performance '') Associated category.

In some embodiments, a particular SNP object is associated with two or more categories. For example, the rs17782313 SNP that occurs in the FTO gene affects an individual's appetite. Therefore, as shown in FIG. 6, the SNP object 652 corresponding to the rs17782313 SNP is associated with both the "hunger and weight" category 626 of the "FUEL ™" product 612 and the "metabolism" category 632 of the "FITCODE ™" product 616. SNP object 652 is also associated with genetic object 672, reflecting the fact that rs17782313 SNP occurs in the FTO gene. In some embodiments, like the rs17782313 SNP object, each of the first category and the second category associated with a particular SNP object is associated with a different product. In some embodiments, a specific SNP object is associated with a first category and a second category, and both the first category and the second category are associated with the same product.

For example, the SNP object 654 corresponding to the rs1800795 SNP of the IL-6 gene (therefore, the SNP object 654 is associated with the gene object 674 corresponding to the IL-6 gene) and the "exercise recovery" category 634 and the "physical performance" category 636 (Both are associated with the "FITCODE ™" product 616). In addition, in some embodiments, a category is associated with two or more products. For example, the "Physical Performance" category 636 is associated with a "FITCODE ™" product 616 and a "SUPERHERO ™" product 618 that provides an assessment of one of the common traits related to physical and mental performance.

In some embodiments, a hierarchical organization of data structures of products, categories, SNP objects, genetic objects, and variant objects is used as a flexible template that not only facilitates the rapid establishment of individual genetics from genotyping measurements obtained from multiple individuals Contour evaluation also facilitates the evaluation of the genetic profile of an individual. In particular, individuals can purchase assessments corresponding to different products to gain a deeper understanding of the ways in which their individual genomes affect the health-related traits and characteristics of different general categories corresponding to different products. Thus, the assessment of the genetic profile of an individual corresponding to one or more products for each particular SNP associated with each category (which is associated with each of the one or more products) includes a particular variation of the particular SNP that the individual has One identification. Typically, obtained by performing one or more genotyping measurements on a biological sample (eg, a blood sample; for example, a cheek swab sample; for example, a saliva sample; for example, a hair sample; for example, a hair follicle sample) obtained from an individual The identification.

In some embodiments, an individual may purchase a first evaluation corresponding to one of a first product and provide a biological sample for genotyping. A biological sample of the individual can be stored (e.g., cryopreserved). After a period of time, the individual may choose to purchase additional evaluations corresponding to other products, and may obtain previously stored biological samples of the individual from the storage for additional genotyping measurements of additional SNPs associated with the new product . Further, in some embodiments, additional new products may be created over time and new evaluations corresponding to the new products may be provided to or purchased by the individual. In certain embodiments, when clarifying new information related to the impact of new and / or existing SNPs on different specific health-related characteristics, new SNP objects and genetic objects may be created and established in association with new or existing categories and / Or new associations between products. In some embodiments, an individual's existing genetic profile assessment is automatically updated to reflect new information. C.ii. Data Structure Hierarchy

In certain embodiments, to facilitate the establishment and presentation (e.g., corresponding to one or more different products) of an individual genetic profile based on the architecture described above, the products, categories, SNP objects, and The genetic object data structure is associated with it as a generic data structure hierarchy for later association with the individual's genotyping data. FIG. 7 is a block diagram of one level of the data structure 700 of an exemplary genetic profile product. In some embodiments, a developer creates and stores one or more common data structure hierarchies that define an individual's ability to purchase and / or access one or more products according to FIG. 7. These data structure hierarchies are common because they do not contain personal information for each individual, but instead define a collection of genes, SNPs, and variations related to the biological characteristics and / or traits covered by a product.

An exemplary data structure of each type is shown in association with the child data structure in FIG. 7 to simplify the presentation of the schema. It should be understood that the data structure may be associated with any number of other data structures in the hierarchy if the association is consistent with the association shown in the block diagram 700 of FIG. 7. For example, category 720b is shown as associated with genetic objects 730a-730b, while category 720c may be associated with one or more genetic objects and / or SNP objects, but none of these associations are shown. In some embodiments, the data structure may be established without additionally forming associations between other types of related types. For example, an unrelated or partially related data structure may be created for planning purposes such as during product or category development (e.g., category 720a is not yet associated because its scope has not yet been determined by the user). For example, non-associated or partially associated data structures can be established to allow genotyping data to be associated with related genetic or SNP objects so that the genetic and / or SNP objects are later associated with one or more categories Where relevant, the material is kept ready for use.

Still referring to FIG. 7, the product 710 includes three categories 720 a to 720 c and additional information 722. The additional information 722 may be a name of the product, an icon associated with the product, and / or a description of the product. The category 720b includes two genetic objects 730a to 730b, one SNP object 740, and additional information 732. The additional information 732 may include a name of the category, a background image associated with the category, an icon associated with the category, a category sequence identifier, and / or a description of the category. SNP object 740 is associated with genetic object 770. The genetic object 730a is associated with three SNP objects 742a to 742c. Categories may be directly associated with SNP objects (such as category 720b is associated with SNP object 740), or they may be indirectly associated (such as SNP objects 742a through 742c are associated with category 720b via genetic object 730a). The ability to form associations indirectly allows all SNP objects associated with a particular genetic object to be associated with a class by forming a single association in the case where all SNP objects of a particular gene are associated with a specific class. The ability to form associations directly allows a particular SNP object to be associated with a class without the need to form all other SNP objects in the case where only one or a subset of the SNP objects of a particular genetic object are associated with a class ( They are associated with the genetic object to which the particular SNP object is associated).

The genetic object 730a is also associated with the additional information 744. The additional information 744 may include one or more data structures including information such as a genetic object 730a corresponding to a unique genetic identifier of a particular physical gene and descriptive information about the corresponding gene. The gene identifier may be an alphanumeric code, such as a recognized name for a gene, or other identification mark or tag that can be stored electronically. Extensions can be stored as a single data structure or as multiple data structures.

SNP object 742b is associated with SNP reference 750 and additional information 754. SNP Reference 750 is a unique identifier for a SNP object that corresponds to a specific physical SNP. The SNP reference may be an alphanumeric code (such as a recognized name for a gene) or other identification mark or tag that can be stored electronically. The SNP reference can be an alphanumeric code, such as a National Biotechnology Information Center (NCBI) database reference number. The additional information 754 may include one or more data structures having other descriptive information about the corresponding SNP.

Various combinations of data elements such as measurement results and qualifiers can be used to represent a particular SNP variation within a corresponding SNP object. For example, a specific variation of an SNP can be identified by a measurement result that identifies an identifier (such as an alphanumeric code) of a specific allele corresponding to the specific variation. For example, a measurement (such as the string "CC") identifies the first variation in which the individual has an rs762551 SNP with cytosine (C) at the rs762551 position in each copy of their genetic material. A measurement (such as the string "AC") identifies a second variation of the rs762551 SNP in which the individual has a C at position rs762551 in one copy and an adenine (A) at position rs762551 in another copy. A measurement (such as the string "AA") identifies a second variation in which an individual has an rs762551 SNP at position rs762551 in each copy of their genetic material.

A qualifier identifies an identifier (such as an alphanumeric code) of a category of a variation, where the classification may be based on the prevalence of the variation in a group of people, a health-related trait associated with the variation, and / or other related Classification basis. Qualifiers can be words or short phrases that characterize variations. For example, "adaptation" can be used to characterize unusual and / or unfavorable variations; "normal" can be used to characterize common and / or unfavorable and unfavorable variations; and "excellent" can be used to characterize uncommon and / or unfavorable variations Or favorable variation. You can also include additional information in a SNP object to describe a particular mutation.

In some embodiments, the measurement results and qualifiers that separately identify and classify the same variation are associated with each other to form a variation object associated with the SNP object. For example, the variant object 752a includes a measurement result 760 and a qualifier 762. The variant object 752a also includes additional information 764. Additional information 764 includes a description of the mutation. For example, the additional information includes a description of a particular health-related phenotype exhibited by an individual having the mutation represented by the mutation object 752a or an explanation of the prevalence of the mutation. An SNP object can be associated with a mutation object to represent each variation of its corresponding specific SNP. For example, the SNP object is associated with three variant objects 752a to 752c.

In some embodiments, the data structure described above is stored as a general hierarchy for generating genetic profile assessments of individuals. A collection of data structures corresponding to genes, SNPs, and mutations can be organized into one or more categories within a product (eg, as visualized in Figure 7). The product can be personalized for the individual by filling in the universal product with the genotyping data of a particular individual or by associating the universal product with the genotyping information of the individual in order to provide the individual with specific information about their specific genome. In some embodiments, a genetic profile evaluation is used to populate an evaluation graphical user interface ("evaluation GUI") through which an individual views an evaluation of one of his / her genetic profiles. In this way, an individual can view an evaluation GUI that visualizes one of his / her genetic profile assessments by showing the individual a particular variation of the SNP that the individual has (eg, organized in a hierarchy of products and categories). C.iii. Add genotyping data to the genetic profile of the individual

In some embodiments, in order to fill in an assessment GUI provided to an individual, genotyping data must be added to the genetic profile assessment of the individual or the genotyping data must be associated with the genetic profile assessment of the individual. FIG. 8 is a block diagram of an exemplary method 800 for adding genotyping data to an individual's genetic profile assessment. In step 810, one of the processors of a computing device receives genotyping data. In step 820, the processor identifies a genetic object corresponding to a gene measured in the genotyping data and a SNP object corresponding to a SNP in or near the gene [eg, the SNP is in the gene Occurs within or near the gene (for example, within a promoter region that affects transcription of the gene; for example, within 5 kb upstream or downstream of the gene; for example, within 100 kb upstream or downstream of the gene; For example, within 500 kb upstream or downstream of the gene; for example, within 1 Mb upstream or downstream of the gene)]. In some embodiments, the genotyping data is stored as a data table in a text file, where each row corresponds to a unique SNP.

At step 830, a specific variation of the identified SNP object and its associated qualifier are determined based on the data from the genotyping measurement. For example, the data corresponding to the measurement results of a particular variation may be stored as one or more rows at the end of each column. In step 840, this data is stored in the genetic profile of the individual. According to method 800, at step 840, the data may be stored in a (previously common) data structure hierarchy or the data may be stored separately along with the association of the data with the identified genetic and SNP objects. In any case, the stored data (and any generated and stored associations) is defined for the genetic profile assessment of the individual. In step 850, the processor determines whether all data of the genotyping data have been stored. If not all data are stored in the genetic profile assessment of the individual, the method returns to step 820. If all data have been stored, the method ends 860. In some embodiments, the processor determines whether there is unsaved data by determining whether a data row exists in the genotyping data below the just processed row.

FIG. 9 shows exemplary genotyping data 900 that can be added to an individual's genetic profile assessment according to the method 800. The genotyping data may take the form of a text file stored by the user, where the text file is generated manually or as an output from a device used to perform genotyping measurements (e.g., TaqMan ^TM SNP genotyping test) produce. Figure 9 includes six genotyping data columns from a single biological sample ("RONEN147"). Each column corresponds to data for a different SNP. Each SNP of the genotyping data 900 is identified by at least one genetic identifier 910 and an SNP reference 920. The gene identifier identifies a gene associated with the SNP. In some embodiments, multiple (e.g., two or more) genes are associated with the SNP (e.g., the SNP can occur near two or more genes and affect the association with the associated genes). Each is associated with a phenotype), and therefore two or more corresponding gene identifiers are listed. Each SNP in the genotyping data has a corresponding mutation identified by allele measurement 930. The measurement results associated with the "Allele 1" and "Allele 2" for a given SNP and the variation of one SNP object corresponding to the given SNP can be compared to fill in the Genetic profile assessment.

The genotyping data used to fill in an individual's genetic profile assessment in Figure 9 was generated from one or more biological samples of the individual. However, the one or more biological samples may also be obtained from different human or non-human animals for use in filling in an individual's genetic profile assessment. In some embodiments, the genotyping data is generated from one or more biological samples from a non-human animal. For example, an individual may provide a biological sample of his or her pet to understand information about the pet's phenotype to help provide better care. The animal can be a pet or an animal that can be taken care of by the individual. For example, the individual may be a veterinarian or a caretaker responsible for caring for the animals at the zoo. In some embodiments, the genotyping data is generated from one or more biological samples of a protected person (a system's guardian). For example, a parent may provide one or more biological samples to the genotyping data for their children to improve his / her way of raising children. D. Computer system and network architecture

One implementation of a network environment 1000 for one of the systems, methods, and architectures described herein is shown in FIG. 10. In a brief overview, referring now to FIG. 10, a block diagram of an exemplary cloud computing environment 1000 is shown and described. The cloud computing environment 1000 may include one or more resource providers 1002a, 1002b, and 1002c (collectively referred to as 1002). Each resource provider 1002 may include computing resources. In some implementations, the computing resources may include any hardware and / or software used to process the data. For example, computing resources may include hardware and / or software capable of executing algorithms, computer programs, and / or computer applications. In some implementations, exemplary computing resources may include an application server and / or database with storage and retrieval capabilities. Each resource provider 1002 may be connected to any other resource provider 1002 in the cloud computing environment 1000. In some embodiments, the resource providers 1002 can be connected via a computer network 1008. Each resource provider 1002 can connect to one or more computing devices 1004a, 1004b, 1004c (collectively referred to as 1004) via the computer network 1008.

The cloud computing environment 1000 may include a resource manager 1006. The resource manager 1006 can be connected to the resource provider 1002 and the computing device 1004 via the computer network 1008. In some implementations, the resource manager 1006 may facilitate the supply of computing resources to one or more computing devices 1004 through one or more resource providers 1002. The resource manager 1006 may receive a request for a computing resource from a specific computing device 1004. The resource manager 1006 may identify one or more resource providers 1002 capable of providing the computing resources requested by the computing device 1004. The resource manager 1006 may select a resource provider 1002 that provides one of the computing resources. The resource manager 1006 can facilitate a connection between the resource provider 1002 and a specific computing device 1004. In some implementations, the resource manager 1006 may establish a connection between a specific resource provider 1002 and a specific computing device 1004. In some implementations, the resource manager 1006 may redirect a specific computing device 1004 to a specific resource provider 1002 having the requested computing resource.

FIG. 11 shows an example of a computing device 1100 and a mobile computing device 1150 that can be used in the method and system described in the present invention. The computing device 1100 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The mobile computing device 1150 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, and other similar computing devices. The functions of these components, their connections and relationships, and their functions shown herein are intended for illustration only and are not intended to be limiting.

The computing device 1100 includes a processor 1102, a memory 1104, a storage device 1106, a high-speed interface 1108 connected to the memory 1104 and a plurality of high-speed expansion ports 1110, and a low-speed expansion port 1114 and the storage device. 1106 is a low-speed interface 1112. Each of the processor 1102, the memory 1104, the storage device 1106, the high-speed interface 1108, the high-speed expansion ports 1110, and the low-speed interface 1112 are interconnected using various buses and can be installed on a common motherboard Or otherwise installed as appropriate. The processor 1102 can process instructions executed in the computing device 1100, including one stored in the memory 1104 or the storage device 1106 to display an external input / output device (such as a display 1116 coupled to a high-speed interface 1108). GUI graphics information instructions. In other implementations, multiple processors and / or multiple buses can be used as appropriate along with multiple memories and multiple types of memory. In addition, multiple computing devices may be connected to each device (for example, a server bank, a blade server group, or a multi-processor system) that provides some of the required operations. Therefore, when terminology is used herein, where multiple functions are described as being performed by "a processor", this encompasses any number of processors in which any number of computing devices (one or more) are used (One or more) embodiments that perform the plurality of functions. In addition, where a function is described as being performed by a "processor," this encompasses any number in which (e.g., in a decentralized computing system) any number of computing devices (one or more) An embodiment in which one or more processors perform this function.

The memory 1104 stores information in the computing device 1100. In some embodiments, the memory 1104 is one (or several) volatile memory units. In some embodiments, the memory 1104 is a (or several) non-volatile memory unit. Memory 1104 may also be another form of computer-readable medium, such as a magnetic disk or optical disk.

The storage device 1106 can provide large-capacity storage for the computing device 1100. In some embodiments, the storage device 1106 may be a computer-readable medium or include a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device or a magnetic tape device, a flash memory, or other Similar to solid state memory devices, or a device array that is one of the devices contained in a storage area network or other configuration. The instructions can be stored in an information carrier. The instructions execute one or more methods (such as the methods described above) when executed by one or more processing devices (e.g., the processor 1102). The instructions may also be stored by one or more storage devices, such as a computer-readable or machine-readable medium (e.g., memory 1104, storage device 1106, or memory on processor 1102).

The high-speed interface 1108 manages the bandwidth-intensive operation for the computing device 1100, and the low-speed interface 1112 manages the lower bandwidth-intensive operation. These function assignments are for illustration only. In some implementations, the high-speed interface 1108 is coupled to the memory 1104, the display 1116 (eg, via a graphics processor or accelerator), and is coupled to a high-speed expansion port 1110 that can accept various expansion cards (not shown). In an embodiment, the low-speed interface 1112 is coupled to the storage device 1106 and the low-speed expansion port 1114. Low-speed expansion port 1114, which can include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) can be coupled to one or more input / output devices (such as a keyboard, a pointing device, a Scanner), or (e.g., through a network adapter) coupled to a network connection device (such as a switch or router).

The computing device 1100 may be implemented in many different forms, as shown in the figure. For example, it may be implemented as a standard server 1120 or multiple times in a group of such servers. In addition, it may be implemented in a personal computer, such as a laptop computer 1122. It may also be implemented as part of a rack server system 1124. Alternatively, the components from the computing device 1100 may be combined with other components (not shown) in a mobile device, such as a mobile computing device 1150. Each of these devices may include one or more of a computing device 1100 and the mobile computing device 1150, and the entire system may be composed of a plurality of computing devices communicating with each other.

The mobile computing device 1150 includes a processor 1152, a memory 1164, an input / output device (such as a display 1154), a communication interface 1166, a transceiver 1168, and other components. The mobile computing device 1150 may also have a storage device (such as a microdrive or other device) to provide additional storage. Each of the processor 1152, the memory 1164, the display 1154, the communication interface 1166, and the transceiver 1168 are interconnected using various buses, and several of these components can be installed on a common motherboard or Install otherwise, as appropriate.

The processor 1152 can execute the instructions in the mobile computing device 1150, including the instructions stored in the memory 1164. The processor 1152 may be implemented as one chipset including discrete and multiple analog and digital processors. The processor 1152 may provide, for example, coordination of other components of the mobile computing device 1150, such as control of a user interface, applications running through the mobile computing device 1150, and wireless communication through the mobile computing device 1150.

The processor 1152 may communicate with a user through a control interface 1158 and a display interface 1156 coupled to the display 1154. The display 1154 may be, for example, a TFT (thin film transistor liquid crystal display) display or an OLED (organic light emitting diode) display or other suitable display technology. The display interface 1156 may include appropriate circuitry for driving the display 1154 to present graphics and other information to a user. The control interface 1158 may receive commands from a user and convert the commands for submission to the processor 1152. In addition, an external interface 1162 can provide communication with the processor 1152, so as to implement near-field communication between the mobile computing device 1150 and other devices. The external interface 1162 may provide, for example, wired communication in some embodiments, or wireless communication in other embodiments, and multiple interfaces may also be used.

The memory 1164 stores information in the mobile computing device 1150. The memory 1164 may be implemented as one or more of one (or several) computer-readable media, one (or several) volatile memory units, or one (or several) non-volatile memory units. An expansion memory 1174 can also be provided and connected to the mobile computing device 1150 through an expansion interface 1172. The expansion interface 1172 can include, for example, a SIMM (Single-Row Memory Module) card interface or a DIMM ( Dual-line in-line memory module) card interface. The extended memory 1174 can provide additional storage space for the mobile computing device 1150, or can also store applications or other information for the mobile computing device 1150. Specifically, the extended memory 1174 may include instructions to execute or supplement the procedures described above, and may also include security information. Therefore, for example, the extended memory 1174 can be provided as a security module of the mobile computing device 1150 and can be programmed with instructions that allow the mobile computing device 1150 to be used safely. In addition, security applications can be provided via the DIMM card along with additional information (such as placing identification information on the DIMM card in an unattackable manner).

The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, the instructions are stored in an information carrier and perform one or more methods (such as the methods described above) when executed by one or more processing devices (e.g., processor 1152). The instructions may also be stored by one or more storage devices, such as one or more computer-readable or machine-readable media (e.g., memory 1164, expansion memory 1174, or memory on the processor 1152). In some implementations, the instructions may be received in a propagated signal, for example, via the transceiver 1168 or the external interface 1162.

The mobile computing device 1150 can communicate wirelessly through a communication interface 1166, and the communication interface 1166 can include a digital signal processing circuit if necessary. The communication interface 1166 can provide communication in various modes or protocols such as GSM voice telephony (Global System for Mobile Communications), SMS (SMS), EMS (Enhanced Messaging Service) or MMS messaging (Multimedia messaging Delivery Services), CDMA (Divided Code Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular Telephone), WCDMA (Broadband Division Multiple Access), CDMA2000 or GPRS (General Packet Radio Service) Wait. This communication may occur, for example, using radio frequency through the transceiver 1168. In addition, short-range communications can occur, such as using Bluetooth®, Wi-Fi ™, or other such transceivers (not shown). In addition, a GPS (Global Positioning System) receiver module 1170 can provide additional navigation-related and location-related wireless data to the mobile computing device 1150 that can be appropriately used by applications running on the mobile computing device 1150.

The mobile computing device 1150 can also audibly communicate using an audio codec 1160. The audio codec 1160 can receive spoken information from a user and convert it into usable digital information. The audio codec 1160 may also produce an audible sound to a user, such as through a speaker in an earpiece of the mobile computing device 1150, for example. The sound may include a sound from a voice call, may include a recorded sound (eg, a voice message, a music file, etc.) and may also include a sound generated by an application operating on the mobile computing device 1150.

The mobile computing device 1150 may be implemented in many different forms, as shown in the figure. For example, it may be implemented as a cellular phone 1180. It may also be implemented as part of a smart phone 1182, personal digital assistant or other similar mobile device.

Various implementations of the systems and technologies described herein can be implemented in digital electronic circuits, integrated circuits, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software and / or combinations thereof in. These different implementations may include implementations in one or more computer programs, which may include at least one programmable processor (which may be dedicated or general purpose, coupled to store from one The system receives and transmits data and instructions to the storage system), one of the at least one input device and at least one output device can be executed and / or interpreted on a programmable system.

These computer programs (also known as programs, software, software applications, and code) contain machine instructions for a programmable processor and can be a high-level procedural and / or object-oriented programming language, and / or Implemented in combined language / machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to a machine-readable medium used to provide machine instructions and / or information to a programmable processor (which includes receiver machine instructions as a machine-readable signal). Any computer program product, device, and / or device (eg, magnetic disk, optical disk, memory, programmable logic device (PLD)). The term machine-readable signal refers to any signal used to provide machine instructions and / or information to a programmable processor.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer that has a display device (e.g., a CRT (cathode ray tube) or LCD) for displaying information to the user. (Liquid crystal display) monitor) and the user can provide input to a keyboard and a pointing device of the computer (for example, a mouse or a trackball). Other types of devices can also be used to provide interaction with a user; for example, the feedback provided to the user can be any form of sensory feedback (for example, visual feedback, auditory feedback, or tactile feedback); and from the user Input can be received in any form, including sound, voice, or tactile input.

The systems and techniques described herein may be implemented in a computing system that includes a back-end component (for example, as a data server) or an intermediate software component (for example, an application server), or Contains a front-end component (for example, a graphical user interface through which a user can interact with one of the systems and technologies described herein or a client computer of a web browser), or such , Middleware, or any combination of front-end components. The components of the system can be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

The computing system may include a client and a server. The client and server are generally remote from each other and usually interact through a communication network. The client-server relationship occurs through computer programs running on the respective computers and having a client-server relationship to each other.

The systems and methods described herein may include one or more artificial intelligence modules (e.g., machine learning modules). For example, an artificial intelligence module (e.g., a machine learning module) can be configured to perform various machine learning techniques, including, for example, linear and nonlinear regression, principal component analysis, k-nearest neighbor method, and support vector machine regression. , Clusters, and the like. To perform one or more of these techniques, an artificial intelligence module can be trained, for example, using example voice data (e.g., pre-recorded questions) and / or alphanumeric data (e.g., a list of specific items) (E.g., machine learning modules) to identify a user input related to other types of data described in this document (e.g., related to text queries, structured responses, suggested genetic profile testing, other suggestions related to purchases, and the like (E.g., relevance). In some embodiments, the artificial intelligence module (for example, a machine learning module) includes a neural network (for example, a convolutional neural network).

Although the invention has been shown and described with particular reference to specific preferred embodiments, those skilled in the art will understand that the invention can be used without departing from the spirit and scope of the invention, as defined by the scope of the accompanying patent application Various changes in form and detail have been made in the preferred embodiments.

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135‧‧‧Screenshots

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200‧‧‧ Method

202‧‧‧step

204‧‧‧step

206‧‧‧step

300‧‧‧ Part of AIML File

350‧‧‧ Part of AIML File

400‧‧‧Method

402‧‧‧step

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410‧‧‧Method

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416‧‧‧step

420‧‧‧Method

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500‧‧‧method

502‧‧‧step

504‧‧‧step

506‧‧‧step

508‧‧‧step

612‧‧‧Products / "FUEL ™" Products

614‧‧‧Products

616‧‧‧Products / "FITCODE ™" Products

618‧‧‧Products / "SUPERHERO ™" Products

622‧‧‧ Food Allergy / "Food Allergy" Category

624‧‧‧Food breakdown

626‧‧‧ Hunger and Weight / "Hunger and Weight" category

628‧‧‧ Vitamin

630‧‧‧Endurance

632‧‧‧ Metabolism / "metabolism" category

634‧‧‧ Ability to recover effectively after exercise / "Exercise Recovery" category

636‧‧‧Cardiovascular fitness and skeletal muscle composition / "Physical performance" category

642‧‧‧Single Nucleotide Polymorphism (SNP) Object

644‧‧‧Single Nucleotide Polymorphism (SNP) Object

646‧‧‧Single Nucleotide Polymorphism (SNP) Object

648‧‧‧Single Nucleotide Polymorphism (SNP) Object

650a‧‧‧Single Nucleotide Polymorphism (SNP) Object

650b‧‧‧Single Nucleotide Polymorphism (SNP) Object

650c‧‧‧Single Nucleotide Polymorphism (SNP) Object

652‧‧‧Single Nucleotide Polymorphism (SNP) Object

654‧‧‧Single Nucleotide Polymorphism (SNP) Object

662‧‧‧Genetic Object

664‧‧‧Genetic Object

666‧‧‧Genetic Object

668‧‧‧Genetic Object

670‧‧‧Genetic Object

672‧‧‧Genetic Object

674‧‧‧Genetic Object

700‧‧‧Data Structure / Block Diagram

710‧‧‧Products

720a‧‧‧Category

720b‧‧‧ Category

720c‧‧‧ Category

722‧‧‧ Additional Information

730a‧‧‧Genetic Object

730b‧Genetic Object

732‧‧‧ Additional Information

740‧‧‧Single Nucleotide Polymorphism (SNP) Object

742a‧‧‧Single Nucleotide Polymorphism (SNP) Object

742b‧‧‧Single Nucleotide Polymorphism (SNP) Object

742c‧‧‧Single Nucleotide Polymorphism (SNP) Object

744‧‧‧ Additional Information

750‧‧‧Single Nucleotide Polymorphism (SNP) Reference

752a‧‧‧ Mutant Object

752b‧‧‧ Mutant Object

752c‧‧‧ Mutant Object

754‧‧‧ Additional Information

760‧‧‧Measurement results

762‧‧‧ qualifier

764‧‧‧ Additional Information

770‧‧‧Genetic Object

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900‧‧‧ Genotyping Information

910‧‧‧Gene identifier

920‧‧‧Single Nucleotide Polymorphism (SNP) Reference

930‧‧‧Allele measurement

1000‧‧‧ network environment / cloud computing environment

1002a‧‧‧ Data Provider

1002b‧‧‧ Resource Provider

1002c‧‧‧ Resource Provider

1004a‧‧‧ Computing Device

1004b‧‧‧ Computing Device

1004c‧‧‧ Computing Device

1006‧‧‧Explorer

1008‧‧‧Computer Network

1100‧‧‧ Computing Device

1102‧‧‧Processor

1104‧‧‧Memory

1106‧‧‧Storage Device

1108‧‧‧High-speed interface

1110‧‧‧High-speed expansion port

1112‧‧‧ Low Speed Interface

1114‧‧‧low speed expansion port

1116‧‧‧Display

1120‧‧‧Standard Server

1124‧‧‧ Rack Server System

1150‧‧‧Mobile Computing Device

1152‧‧‧Processor

1154‧‧‧Display

1156‧‧‧Display interface

1158‧‧‧Control Interface

1160‧‧‧Audio codec

1162‧‧‧External interface

1164‧‧‧Memory

1166‧‧‧ communication interface

1168‧‧‧ Transceiver

1170‧‧‧Global Positioning System (GPS) Receiver Module

1172‧‧‧Expansion interface

1174‧‧‧Expand Memory

1182‧‧‧Smartphone

The drawings are presented herein for purposes of illustration only and not for limitation. The foregoing and other objects, aspects, features, and advantages of the present invention will become more and better understood by referring to the following description in conjunction with the accompanying drawings, among which:

FIG. 1A is a screen shot of an interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

FIG. 1B is a screen shot of interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

FIG. 1C is a screen shot of interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

FIG. 1D is a screen shot of interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

FIG. 1E is a screen shot of an interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

FIG. 1F is a screen shot of interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

1G is a screenshot of the interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention;

1H is a screen shot of the interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention;

FIG. 1I is a screen shot of interaction between a user and an artificial intelligence chat robot via a messaging interface according to an illustrative embodiment of the present invention; FIG.

2 is a block flow diagram of a method for providing purchase suggestions corresponding to a genetic profile test through interaction between a user and an artificial intelligence chat robot according to an illustrative embodiment of the present invention;

3A is a part of a code (an AIML file) for matching a user input with a genetic profile test according to an illustrative embodiment of the present invention;

FIG. 3B is a portion of an identification code (an AIML file) that provides a genetic profile test that can be purchased to a user according to an illustrative embodiment of the present invention; FIG.

4A is a block flow diagram of a method for providing purchase advice corresponding to a genetic profile test through interaction between a user and a voice assistant according to an illustrative embodiment of the present invention;

4B is a block flow diagram of a method for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and an artificial intelligence chat robot according to an illustrative embodiment of the present invention;

4C is a block flow diagram of a method for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and a voice assistant according to an illustrative embodiment of the present invention;

5 is a block diagram of one method of linking supplement purchase advice to personal genetic profile products according to an illustrative embodiment of the present invention;

6 is a block diagram illustrating an association between different data structures of a genetic profile product according to an illustrative embodiment of the present invention;

7 is a block diagram showing an organizational hierarchy of a personal genetic profile product according to an illustrative embodiment of the present invention;

8 is a block diagram showing a procedure for establishing a genetic profile evaluation according to an illustrative embodiment of the present invention;

9 is a part of a text file including genotyping data according to an illustrative embodiment of the present invention;

FIG. 10 is a block diagram of an exemplary network environment for use in the methods and systems described herein in accordance with an illustrative embodiment of the present invention; and

11 is a block diagram of an exemplary computing device and an exemplary mobile computing device used in an illustrative embodiment of the present invention.

[Embodiments], which will be explained hereinafter, will become more apparent when combining the drawings with features and advantages of the present invention, in which the same element symbols always identify corresponding elements. In the drawings, the same element symbols generally indicate the same, functionally similar, and / or structurally similar elements.

Claims (106)

A method for providing purchase advice corresponding to a genetic profile test through interaction between a user and an artificial intelligence chat robot, the method comprising: (a) receiving user input of a text query through a processor of a computing device; ( b) identifying one or more genetic profile tests (e.g., using a machine learning module) related to the text query by the processor based on the text query, wherein each genetic profile test (e.g., by a product (Represented by) a general health-related phenotype that is associated with and corresponds to one of a specific set of one or more SNPs (e.g., where each corresponding SNP affects the general health-related association associated with the genetic profile test) Phenotype-associated with a specific health-related trait); and (c) provided by the processor for each of the one or more identified genetic profile tests (e.g., for use on a computing device of the user) (Visually and / or graphically) includes a graphical representation of one of the genetic profile tests (eg, the name of the test that appears as text; for example, a shadow associated with the test) image). The method of claim 1, wherein identifying the one or more genetic profile tests comprises: accessing, by the processor, a database for a set of predefined genetic profile tests including one of the following (e.g., a group of Text files, such as AIML files): (i) an identifier of the genetic profile test [eg, a text label (eg, representing a name of the genetic profile test)]; and (ii) the identification with the genetic profile test Associated with one or more keywords; and for each identified genetic profile test, one or more entries in the text query are associated with (i) the identifier of the genetic profile test and / or (ii) At least one of the one or more keywords matches. The method of claim 2, wherein the one or more keywords for each SNP of the set of SNPs measured by the genetic profile test include a name of a gene associated with the SNP (e.g., where the SNP occurs A name of a gene; for example, a name of a gene whose SNP affects its transcription). The method of any one of claims 1 to 3, comprising identifying the one or more genetic profile tests based in part on information stored in one or more reference files in a database of reference files. The method of claim 4, wherein identifying the one or more genetic profile tests includes: accessing, by the processor, a plurality of reference documents (e.g., published literature; e.g., a plurality of web pages of a public database) A database in which each reference file is associated with one or more SNPs measured via the one or more genetic profile tests (e.g., each reference file includes the specific health information regarding one or more SNPs and their effects) Information about related phenotypes); using the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user text query [for example, by comparing the text query with the text in the reference document Data (e.g., words written) (e.g., by extracting keywords from the text query and searching for matches in the reference documents; for example, using receiving the text query as input and determining One of the one or more SNPs related to the user text query machine learning module)]; for each of the one or more expected genetic profile tests, determine the text query related to the user A matching degree between the one or more SNPs in question and the set of one or more SNPs of the expected genetic profile test measurement; and identifying the one or more genetic profile tests based on the determined matching degree. The method of any one of claims 1 to 5, wherein the graphical representation including identification by one of the genetic profile tests includes a selectable link that is selected by the user (e.g., by a user A mouse click; for example, after the user uses a touch gesture such as a touch-sensitive interface of a touch screen to face the link) a user is directed to a website that allows the user to purchase the genetic profile test. The method of any one of claims 1 to 6, comprising causing (e.g., on a computing device of the user) to display a graphical user interface (GUI) corresponding to a chat window, wherein via the chat window The GUI receives the text query and appears in the chat window GUI including the graphical representation identified by one of the genetic profile tests as a response to the text query. A method as claimed in item 7, which includes an interactive website (e.g., a website used by the user to view genetic profile test results; for example, thereby allowing the user to identify and purchase additional genetic resources that may be of interest to them) Contour Test) displays the chat window GUI. The method of claim 7, comprising an interactive app [for example, an app used by the user to view genetic profile test results (for example, an app executed on a mobile device such as a mobile phone); for example , Thereby allowing the user to identify and (e.g., via in-app purchases) purchase additional genetic profile tests that may be of interest to them] to display the chat window GUI. The method of any one of claims 1 to 9, wherein the text query is provided by a voice assistant. A method for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and an artificial intelligence chat robot. The method includes: (a) receiving a text query by a processor of a computing device; User input, where the user and one or more genetic profiles [for example, the one or more genetic profiles represent the results of a genetic profile test performed on the user; for example, where the user is a subscribed (such as , (Registered) users, have performed one or more genetic profile tests and have established one or more genetic profiles and stored them in a database accessible by the processor] associated; (b) by the processor based on the text query and based at least in part on the user's one or more genetic contours to identify one or more suggestions in response to the received user input (e.g., (several) purchases (Recommended) (e.g., using a machine learning module); and (c) provided by the processor (e.g., for display and / or graphical display on a computing device of the user) including the one or more Each A graphic representation [for example, a name that appears as text on a suggested purchase (eg, a nutritional supplement; for example, purchase of a mobile health device); for example, an image associated with a proposed purchase, in response to A textual description of the query (eg, a suggested diet plan, a suggested fitness plan, etc.)]. The method of claim 11, wherein identifying the one or more suggestions includes: accessing, by the processor, a database for each of a set of predefined suggestions including one of the following (e.g., a set of text files, such as AIML file): (i) an identifier of the proposal [for example, a text label (for example, representing a name of the proposal)]; and (ii) one or more keywords associated with the identifier of the proposal ; And for each identified suggestion, match one or more items in the text query with (i) the identifier of the suggestion and / or (ii) at least one of the one or more keywords. The method of claim 12, wherein each suggestion is associated with a set of one or more SNPs, and the one or more keywords for each SNP of the SNP set associated with the suggestion include the ones associated with the SNP A name of a gene (eg, a name of a gene in which the SNP occurs; for example, a name of a gene in which the SNP affects transcription). The method of claim 12, wherein at least one of the one or more recommendations is a genetic profile test that is associated with and corresponds to (for example, represented by a product) a general class of health-related phenotypes Measured on a specific set of one or more SNPs (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test), and The set of SNPs associated with the genetic profile test is the SNPs measured by the genetic profile test. The method of any one of claims 12 to 14, wherein each suggestion is associated with a set of one or more genes, and the one or more keywords include names corresponding to the genes associated with the suggestion One or more keywords. The method of any one of claims 11 to 14, comprising identifying the one or more suggestions based in part on information in one or more reference documents stored in a database of reference documents. The method of claim 16, wherein identifying the one or more suggestions includes: accessing, by the processor, one of a plurality of reference documents (e.g., published literature; e.g., a plurality of web pages of a public database) Library, where each reference file is associated with one or more SNPs and / or genes associated with one or more recommendations (e.g., each reference file includes information about one or more SNPs and / or genes and their effects) Information such as specific health-related phenotypes); use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user text query [for example, by comparing the text query with the reference document Text data (e.g., words written) (e.g., by extracting keywords from the text query and searching for matches in those reference documents; for example, using receiving the text query as input and based on the references The document determines one of the one or more SNP machine learning modules related to the user text query)]; for each of one or more expected suggestions, determines the one related to the user text query A degree of matching between the one or more SNPs and / or genes and the SNPs and / or genes associated with the expected recommendation; and identifying the one or more recommendations based on the determined degree of matching. The method of any one of claims 11 to 17, comprising automatically identifying the one based on a variation in a SNP in one of the user's genomes (e.g., identified by one or more genetic profiles of the user). Or more suggestions. The method of any one of claims 11 to 18, wherein: at least one of the identified recommendations is associated with one or more SNPs, and for each of the one or more associated SNPs, the recommendation is related to A specific variation of the SNP (e.g., identified by a qualifier) is associated, and the identification of the suggestion is based at least in part on the specific variation of one or more SNPs associated with the suggestion and the user has the Correspondence (e.g., relationship; e.g., correlation) between specific variations of one or more SNPs as identified in the genetic profiles associated with the user. The method of any one of claims 11 to 19, comprising: receiving (and / or accessing) mobile health data recorded by a mobile health device of one of the users; and based on the genetic profile of the user And the received mobile health information to automatically identify the one or more recommendations. The method of any one of claims 11 to 20, wherein the one or more suggestions include a suggested genetic profile test. The method of any one of claims 11 to 21, wherein the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as blood measurement of a cholesterol index; for example, A blood test; for example, a slice test). The method of any one of claims 11 to 22, wherein the one or more recommendations include purchase recommendations for one or more supplements (e.g., nutritional supplements). The method of any one of claims 11 to 23, wherein the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, and an electroencephalogram feedback A protocol, a behavioral protocol (eg, a focused protocol, an ADHD adjuvant protocol), and a somatization therapy. The method of claim 24, wherein the one or more members are individualized protocols and / or treatments based on the one or more genetic profiles of the user. The method of any one of claims 11 to 25, wherein the one or more suggestions include one or more mobile health devices (and / or one or more software apps operating on a mobile health device) or Multiple recommended purchases. The method of any one of claims 11 to 26, wherein the graphical representation including identification by one of the genetic profile tests includes a selectable link that is selected by the user (e.g., by a user A mouse click; for example, after the user uses a touch gesture such as a touch-sensitive interface of a touch screen to face the link) a user is directed to a website that allows the user to purchase the genetic profile test. A method as claimed in any one of claims 11 to 27, comprising causing (e.g., on a computing device of the user) to display a graphical user interface (GUI) corresponding to a chat window, wherein via the chat window The GUI receives the text query and appears in the chat window GUI including the graphical representation identified by one of the genetic profile tests as a response to the text query. A method as claimed in item 28, which includes an interactive website (e.g., a website used by the user to view genetic profile test results; for example, thereby allowing the user to identify and purchase additional genetic resources that may be of interest to them) Contour Test) displays the chat window GUI. The method of claim 28, comprising an interactive app [for example, an app used by the user to view genetic profile test results (for example, an app executed on a mobile device such as a mobile phone); for example , Thereby allowing the user to identify and (e.g., via in-app purchases) purchase additional genetic profile tests that may be of interest to them] to display the chat window GUI. The method of any one of claims 11 to 30, wherein the text query is provided by a voice assistant. A method for providing purchase suggestions corresponding to genetic profile testing through interaction between a user and a voice assistant, the method comprising: (a) using a processor of a computing device (e.g., via a network) from the voice An assistant (eg, a processor of the voice assistant) receives a structured request including a text query, wherein the structured request is generated by the voice assistant in response to a user's voice [for example, by detecting the use The user's voice and generating voice data corresponding to at least a portion of the user's voice and processing the user's voice data to generate the text query (e.g., by generating text voice data, (e.g., via a machine learning Module) matches the text speech data with one or more identifiable text phrases stored in a database, and uses at least a portion of the matched identifiable text phrases as entries in the text query )]; (B) the text query using the structured request by the processor to identify one or more genetic profile tests related to the user's voice [for example, by making the text query (eg For example, the one or more terms of the text query) match (for example, stored in a database) one or more identifiers of a genetic profile test (for example, via a machine learning module; for example, by Identify one or more subroutines based on a first part of the text query and pass a second part of the text query to the identified subroutines as a variable evaluated by the subroutines to identify the One or more genetic profile tests)]; and (c) one or more structured responses including identification of each of the one or more genetic profile tests by the processor (e.g., via a network) Provided to the voice assistant, wherein each structured response, when executed by the voice assistant (e.g., a processor of the voice assistant), causes the voice assistant to generate an audio output corresponding to the simulated voice based on the structured response , Thereby providing the user with audio feedback corresponding to the recommendations associated with the one or more identified genetic profile tests. The method of claim 32, wherein step (b) comprises associating the text query (e.g., one or more entries of the text query) with one or more stored data each associated with one or more genetic profile tests Keyword match. The method of claim 33, wherein the one or more keywords for each SNP of a set of SNPs measured by the one or more genetic profile tests include a name of a gene associated with the SNP (e.g., within A name of a gene of the SNP occurs; for example, a name of a gene whose SNP affects transcription). The method of any one of claims 32 to 34, comprising identifying the one or more genetic profile tests based in part on information stored in one or more reference files in a database of reference files. The method of claim 35, wherein identifying the one or more genetic profile tests comprises: accessing, by the processor, a plurality of reference documents (e.g., published literature; e.g., a plurality of web pages of a public database) A database in which each reference file is associated with one or more SNPs measured by the one or more genetic profile tests (e.g., where each reference file includes specific health-related information about one or more SNPs and their effects) Phenotypic information); use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the text query [for example, by comparing the text query with the text data in the reference document (such as , Written terms) (for example, by extracting keywords from the text query and searching for matches in the reference documents; for example, using receiving the text query as input and determining a relationship with the user based on the reference documents One of the one or more SNPs related to the text query machine learning module)]; for each of the one or more expected genetic profile tests, determined to be relevant to the user text query A degree of match between the one or more SNP set with a genetic profile of the test measurements on the expected or more of the SNP; and identifying the one or more genetic profile is determined by the test based on the matching degree. A method for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and a voice assistant, the method comprising: (a) a processor through a computing device (e.g., via a network Path) receives a structured request including a text query from the voice assistant (e.g., a processor of the voice assistant), where the structured request is generated by the voice assistant in response to the user's voice [for example, by borrowing By detecting the user voice and generating voice data corresponding to at least a portion (up to all) of the user voice and processing the user voice data to generate the text query (e.g., by generating a text voice Data (e.g., via a machine learning module) to match the text speech data with one or more identifiable text phrases stored in a database, and at least use the matching identifiable text phrases A part (up to all) as items in the text query)], and wherein the text query of the structured request includes the user (e.g., a subscribed A user who has performed one or more genetic profile tests and has established them and one or more genetic profiles and stored them in a database accessible by the processor); (b ) Identifying the one or more suggestions (e.g., purchase suggestions) based on the text query of the structured request by the processor based at least in part on the one or more genetic profiles associated with the user; and (c) One or more structured responses including the identification of each of the one or more suggestions are provided to the voice assistant by the processor (e.g., via a network), where each structured response is transmitted through the voice When the assistant (for example, a processor of the voice assistant) executes, the voice assistant is caused to generate an audio output corresponding to the simulated voice based on the structured response, thereby providing the user with suggestions corresponding to the one or more Audio feedback. The method of claim 37, wherein step (b) includes at least one of the text query (e.g., one or more items of the text query) and each of the one or more suggestions (e.g., purchase suggestions) The associated one or more stored keyword matches. The method of claim 38, wherein each suggestion is associated with a set of one or more SNPs, and each SNP of the set of SNPs of the one or more stored keywords for the genetic profile test includes the SNP An associated name of a gene (eg, a name of a gene in which the SNP occurs; for example, a name of a gene in which the SNP affects transcription). The method of claim 39, wherein at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds Measured on a specific set of one or more SNPs (e.g., where each corresponding SNP affects a specific health-related trait associated with the general health-related phenotype associated with the genetic profile test), and The set of SNPs associated with the genetic profile test is the SNPs measured by the genetic profile test. The method of any one of claims 38 to 40, wherein each suggestion is associated with a set of one or more genes, and the one or more stored keywords include corresponding genes corresponding to the suggestion One or more keywords. The method of any one of claims 37 to 41, comprising identifying the one or more suggestions based in part on information in one or more reference documents stored in a database of reference documents. The method of claim 42, wherein identifying the one or more suggestions includes: accessing, by the processor, a piece of information including a plurality of reference documents (e.g., published literature; e.g., a plurality of web pages of a public database) Library, where each reference file is associated with one or more SNPs and / or genes associated with one or more recommendations (e.g., each reference file includes information about one or more SNPs and / or genes and their effects) Information such as specific health-related phenotypes); use the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user text query [for example, by comparing the text query with the reference document Text data (e.g., words written) (e.g., by extracting keywords from the text query and searching for matches in those reference documents; for example, using receiving the text query as input and based on the references The document determines one of the one or more SNP machine learning modules related to the user text query)]; for each of one or more expected suggestions, determines the one related to the user text query A degree of matching between the one or more SNPs and / or genes and the SNPs and / or genes associated with the expected recommendation; and identifying the one or more recommendations based on the determined degree of matching. The method of any one of claims 37 to 43, comprising automatically identifying the one based on a variation in a SNP in one of the user's genomes (e.g., identified by one or more genetic profiles of the user). Or more suggestions. The method of any one of claims 37 to 44, wherein: at least one of the identified recommendations is associated with one or more SNPs, and for each of the one or more associated SNPs, the recommendation is associated with A specific variation of the SNP (e.g., identified by a qualifier) is associated, and the identification of the suggestion is based at least in part on the specific variation of one or more SNPs associated with the suggestion and the user has the Correspondence (e.g., relationship; e.g., correlation) between specific variations of one or more SNPs as identified in one or more genetic profiles of the user. The method of any one of claims 37 to 45, comprising: receiving (and / or accessing) mobile health data recorded by a mobile health device of the user; and based on the genetic profile of the user And the received mobile health information to automatically identify the one or more recommendations. The method of any one of claims 37 to 46, wherein the one or more suggestions include a suggested genetic profile test. The method of any one of claims 37 to 47, wherein the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as blood measurement of a cholesterol index; for example, A blood test; for example, a slice test). The method of any of claims 37 to 48, wherein the one or more recommendations include purchase recommendations for one or more supplements (e.g., nutritional supplements). The method of any one of claims 37 to 49, wherein the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, and an electroencephalogram feedback A protocol, a behavioral protocol (eg, a focused protocol, an ADHD adjuvant protocol), and a somatization therapy. The method of claim 50, wherein the one or more members are individualized protocols and / or treatments based on the one or more genetic profiles of the user. The method of any one of claims 37 to 51, wherein the one or more suggestions include one or more mobile health devices (and / or one or more software apps operating on a mobile health device) or Multiple recommended purchases. The method of any one of claims 37 to 52, wherein the structured response includes data corresponding to the location and name of one of the merchants from which the user can purchase the proposal, and the structured response is in When the voice assistant is executed, the voice assistant is caused to generate an audio output that provides the location and name of the business. A system for providing purchase advice corresponding to genetic profile testing through interaction between a user and an artificial intelligence chat robot. The system includes: a processor; and a memory storing instructions thereon, where the instructions are borrowed When executed by the processor, the processor is caused to: (a) receive user input of a text query; (b) identify one or more genetic profile tests related to the text query based on the text query (e.g., using a machine learning Module), where each genetic profile test is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to one of a specific set of one or more SNPs (e.g., each corresponding SNP affects a specific health-related trait associated with the general class of health-related phenotypes associated with the genetic profile test); and (c) for each of the one or more identified genetic profile tests, provide (e.g., For displaying and / or graphical display on a computing device of the user) includes a graphical representation (for example, the The name of the test; for example, one associated with the test image correlation). The system of claim 54 wherein the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests by accessing each of a set of predefined genetic profile tests. Includes a database of one of the following (eg, a set of text files, such as an AIML file): (i) an identifier of the genetic profile test [eg, a text label (eg, a name representing the genetic profile test)] ; And (ii) one or more keywords associated with the identifier of the genetic profile test; and for each identified genetic profile test, one or more entries in the text query are related to (i) the The identifier of the genetic profile test and / or (ii) at least one of the one or more keywords matches. The system of claim 55, wherein the one or more keywords for each SNP of the set of SNPs measured by the genetic profile test include a name of a gene associated with the SNP (e.g., where the SNP occurs A name of a gene; for example, a name of a gene whose SNP affects its transcription). The system of any one of claims 54 to 56, wherein the instructions, when executed by the processor, cause the processor to be based in part on one or more reference files stored in a database of reference files. The information identifies the one or more genetic profile tests. The system of claim 57 wherein the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests by: accessing a plurality of reference documents (e.g., published literature) (E.g., a plurality of web pages of a public database) a database in which each reference file is associated with one or more SNPs measured by the one or more genetic profile tests (e.g., each reference file includes information about a Or multiple SNPs and information about those specific health-related phenotypes that they affect); use the text query and the information in the multiple reference documents to determine one or more SNPs related to the user text query [for example, By comparing the text query with text data (eg, written words) within the reference document (eg, by extracting keywords from the text query and searching for matches in those reference documents; for example, using receive The text query is taken as an input and one of the one or more SNPs related to the user text query is determined based on the reference documents to a machine learning module of the one or more SNPs)]; for one or more expected genetics Each of the profile tests to determine a degree of match between the one or more SNPs related to the user text query and the set of one or more SNPs of the expected genetic profile test measurement; and based on the determined The matching degree identifies the one or more genetic profile tests. If the system of any one of claims 54 to 58 including the graphical representation identified by one of the genetic profile tests includes an optional link, the optional link is selected by the user (e.g., by a user A mouse click; for example, after the user uses a touch gesture such as a touch-sensitive interface of a touch screen to face the link) a user is directed to a website that allows the user to purchase the genetic profile test. The system of any one of claims 54 to 59, wherein the instructions, when executed by the processor, cause the processor to cause (e.g., on a computing device of the user) to display a corresponding chat window A graphical user interface (GUI), in which the text query is received via the chat window GUI and the graphical representation including an identification of the genetic profile test is displayed in the chat window GUI as a response to the text query. The system of claim 60, wherein the instructions, when executed by the processor, cause the processor to be on an interactive website (e.g., a website used by the user to view genetic profile test results; for example, by allowing The user recognizes and purchases additional genetic profiling tests that may be of interest to him) and displays the chat window GUI. The system of claim 60, wherein the instructions, when executed by the processor, cause the processor to be in an interactive app [for example, the user is an app for viewing genetic profile test results (for example, executed in A mobile phone, an app on a mobile device); for example, thereby allowing the user to identify and (eg, via in-app purchases) purchase additional genetic profile tests that may be of interest to them] to display the chat window GUI. The system of any one of claims 54 to 62, wherein the text query is provided by a voice assistant. A system for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and an artificial intelligence chat robot. The system includes: a processor; and a memory storing instructions thereon, wherein The instructions, when executed by the processor, cause the processor to: (a) receive user input from a text query, where the user is associated with one or more genetic profiles [eg, the one or more genetic profiles represent a pair The results of the genetic profile test performed by the user; for example, where the user is a subscribed (e.g., logged in) user, one or more genetic profile tests have been performed on it, and one or more have been established on them Genetic profiles and stored in a database accessible by the processor] associated; (b) based on the text query and based at least in part on the user's genetic profile or genetic profile identification response to the classic One or more suggestions received by the user (e.g. (several) purchase suggestions) (e.g. using a machine learning module); and (c) providing (e.g. Appearing on a piece and / or graphically) including one of the one or more recommendations [eg, appearing as one of the texts and suggested to purchase (eg, a nutritional supplement; for example, purchase one of mobile health devices) Name; for example, an image associated with a proposed purchase, a textual description in response to the query (eg, a suggested diet plan, a suggested fitness plan, etc.)]. The system of claim 64, wherein the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by the following steps: Each of the accesses to a set of predefined recommendations includes the following A database (eg, a set of text files, such as an AIML file): (i) an identifier of the suggestion [eg, a text label (eg, representing a name of the suggestion)]; and (ii) with the suggestion One or more keywords associated with the identifier; and for each identified suggestion, one or more of the items in the text query are related to (i) the identifier of the suggestion and / or (ii) the At least one of the one or more keywords matches. If the system of claim 65, wherein each suggestion is associated with one of one or more sets of SNPs, and the one or more keywords for each SNP of the SNP set associated with the suggestion include the associated with the SNP A name of a gene (eg, a name of a gene in which the SNP occurs; for example, a name of a gene in which the SNP affects transcription). The system of claim 66, wherein at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to A measurement of a particular set of one or more SNPs (e.g., where each corresponding SNP affects a particular health-related trait associated with the general health-related phenotype associated with the genetic profile test) and is associated with the The set of SNPs associated with the genetic profile test is the SNPs measured by the genetic profile test. The system of any one of claims 65 to 67, wherein each suggestion is associated with a set of one or more genes, and the one or more keywords include names corresponding to the genes associated with the suggestion One or more keywords. The system of any one of claims 64 to 68, wherein the instructions, when executed by the processor, cause the processor to be based in part on one or more reference files stored in a database of reference files. The information identifies the one or more suggestions. The system of claim 69, wherein the instructions, when executed by the processor, cause the processor to identify the one or more suggestions by: accessing a plurality of reference documents (e.g., published literature; e.g., , A plurality of web pages of a public database), a database in which each reference file is associated with one or more SNPs and / or genes associated with one or more suggestions (for example, each reference file includes information about one or more Information about the specific health-related phenotypes of multiple SNPs and / or genes and their effects); using the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user text query [E.g., by comparing the text query with textual data (e.g., written terms) within the reference document (e.g., by extracting keywords from the text query and searching for matches in those reference documents; for example, Using a machine learning module that receives the text query as input and determines the one or more SNPs related to the user text query based on the reference documents)]; for one or more expected suggestions Each, determine a degree of match between the one or more SNPs and / or genes associated with the user text query and the SNPs and / or genes associated with the prospective suggestion; and based on the determined The matching degree identifies the one or more suggestions. If the system of any one of items 64 to 70 is requested, the instructions, when executed by the processor, cause the processor to be based on a SNP in one of the genomes of the user (e.g., via one of the users or One of the multiple genetic contour recognition) a mutation automatically identifies the one or more suggestions. The system of any one of claims 64 to 71, wherein: at least one of the identified recommendations is associated with one or more SNPs, and for each of the one or more associated SNPs, the recommendation is related to A specific variation of the SNP (e.g., identified by a qualifier) is associated, and the identification of the suggestion is based at least in part on the specific variation of one or more SNPs associated with the suggestion and the user has the Correspondence (e.g., relationship; e.g., correlation) between specific variations of one or more SNPs as identified in the genetic profiles associated with the user. The system of any one of claims 64 to 72, wherein the instructions, when executed by the processor, cause the processor to: receive (and / or access) the information recorded by one of the user's mobile health devices Mobile health data; and automatically identifying the one or more recommendations based on the genetic profile of the user and the received mobile health data. The system of any one of claims 64 to 73, wherein the one or more recommendations include a suggested genetic profile test. The system of any one of claims 64 to 74, wherein the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; for example, A blood test; for example, a slice test). The system of any one of claims 64 to 75, wherein the one or more recommendations include purchase recommendations for one or more supplements (e.g., nutritional supplements). The system of any one of claims 64 to 76, wherein the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around plan, and an electroencephalogram feedback A protocol, a behavioral protocol (eg, a focused protocol, an ADHD adjuvant protocol), and a somatization therapy. The system of claim 77, wherein the one or more members are individualized protocols and / or treatments based on the one or more genetic profiles of the user. The system of any one of claims 64 to 78, wherein the one or more recommendations include one or more mobile health devices (and / or one or more software apps operating on a mobile health device) or Multiple recommended purchases. If the system of any one of claims 64 to 79, wherein the graphical representation including the identification of one of the genetic profile tests includes an optional link, the optional link is selected by the user (e.g., by a user A mouse click; for example, after the user uses a touch gesture such as a touch-sensitive interface of a touch screen to face the link) a user is directed to a website that allows the user to purchase the genetic profile test. The system of any one of claims 64 to 80, wherein the instructions, when executed by the processor, cause the processor to cause (e.g., on a computing device of the user) to display a corresponding chat window A graphical user interface (GUI), in which the text query is received via the chat window GUI and the graphical representation including an identification of the genetic profile test is displayed in the chat window GUI as a response to the text query. The system of claim 81, wherein the instructions, when executed by the processor, cause the processor to be on an interactive website (e.g., a website used by the user to view genetic profile test results; for example, by allowing The user recognizes and purchases additional genetic profiling tests that may be of interest to him) and displays the chat window GUI. The system of claim 81, wherein the instructions, when executed by the processor, cause the processor to be in an interactive app [for example, an app used by the user to view genetic profile test results (for example, A mobile phone, an app on a mobile device); for example, thereby allowing the user to identify and (eg, via in-app purchases) purchase additional genetic profile tests that may be of interest to them] to display the chat window GUI. The system of any one of claims 64 to 83, wherein the text query is provided by a voice assistant. A system for providing purchase suggestions corresponding to genetic profile testing through interaction between a user and a voice assistant. The system includes: a processor; and a memory storing instructions thereon, wherein the instructions are used by the The processor causes the processor to execute: (a) (e.g., via a network) receiving a structured request including a text query from the voice assistant (e.g., a processor of the voice assistant), wherein the structured request Generated by the voice assistant in response to the user's voice [for example, by detecting the user's voice and generating voice data corresponding to at least a portion of the user's voice and processing the user's voice data To generate the text query (e.g., by generating text speech data (e.g., via a machine learning module) to match the text speech data with one or more identifiable text phrases stored in a database And using at least a portion of the matching identifiable text phrases as items in the text query)]; (b) using the structured request to identify the text query and the user's voice With respect to one or more genetic profile tests [for example, by combining the text query (e.g., one or more terms of the text query) with (e.g., stored in a database) one of the genetic profile tests or Multiple identifier matches (e.g., via a machine learning module; e.g., by identifying one or more subroutines based on a first part of the text query and passing a second part of the text query to the Identified subroutines as variables evaluated by the subroutines to identify the one or more genetic profile tests)]; and (c) (eg, via a network) will include the one or more genetic profiles The identified one or more structured responses of each of the tests are provided to the voice assistant, where each structured response when executed by the voice assistant (e.g., one of the voice assistant processors) causes the voice assistant to be based on the voice assistant The structured response generates an audio output corresponding to one of the simulated voices, thereby providing the user with audio feedback corresponding to a suggestion associated with the one or more identified genetic profile tests. The system of claim 85, wherein the instructions, when executed by the processor, cause the processor to make the text query (e.g., one or more items of the text query) and each one or more The genetic profile test is associated with one or more stored keyword matches to identify the one or more genetic profile tests related to the user's voice. The system of claim 86, wherein the one or more keywords for each SNP of a set of SNPs measured by the one or more genetic profile tests include a name of a gene associated with the SNP (e.g., within A name of a gene of the SNP occurs; for example, a name of a gene whose SNP affects transcription). The system of any one of claims 85 to 87, wherein the instructions, when executed by the processor, cause the processor to be based in part on one or more reference files stored in a database of reference files. The information identifies the one or more genetic profile tests. The system of claim 88, wherein the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests by: accessing a plurality of reference documents (e.g., published literature) ; For example, a plurality of web pages of a public database) a database in which each reference file is associated with one or more SNPs measured by the one or more genetic profile tests (for example, where each reference file includes information about Information about one or more SNPs and their specific health-related phenotypes); using the text query and the information in the plurality of reference documents to determine one or more SNPs related to the text query [for example, by comparing The text query and the textual data (e.g., written words) in the reference document (e.g., by extracting keywords from the text query and searching for matches in the reference documents; e.g., receiving the text query using One of the one or more SNPs related to the user's text query as a machine learning module based on the reference documents as a reference)]; for one or more expected genetic profile measurements Each of which determines a match between one or more SNPs related to the user's text query and the set of one or more SNPs measured by the expected genetic profile test; and based on the determined match Identify the one or more genetic profile tests. A system for providing consumer feedback corresponding to one or more genetic profile tests through interaction between a user and a voice assistant. The system includes: a processor; and a memory storing instructions thereon, where The instructions, when executed by the processor, cause the processor: (a) (e.g., via a network) to receive a structured request including a text query from the voice assistant (e.g., a processor of the voice assistant), The structured request is generated by the voice assistant in response to the user's voice [for example, by detecting the user's voice and generating a voice corresponding to at least a part (up to all) of the user's voice Data and processing the user voice data to generate the text query (eg, by generating text voice data (e.g., via a machine learning module) and storing the text voice data in one of a database Or more recognizable text phrases match, and using at least a portion (up to all) of the recognizable text phrases of those matches as items in the text query)], and wherein the structured request The text query includes the user associated with one or more genetic profiles (e.g., a subscribed user who has been tested with one or more genetic profiles and has been established with one or more genetic profiles Contours and stored in a database accessible by the processor); (b) the text query using the structured request is based at least in part on the one or more genetics associated with the user Outline (e.g., a purchase suggestion); and (c) (e.g., via a network) provide one or more structured responses including the identification of each of the one or more suggestions to The voice assistant, wherein each structured response, when executed by the voice assistant (for example, a processor of the voice assistant), causes the voice assistant to generate an audio output corresponding to the simulated voice based on the structured response, by This provides the user with audio feedback corresponding to the one or more suggestions. The system of claim 90, wherein the instructions, when executed by the processor, cause the processor to cause the text query (e.g., one or more items of the text query) and each of the one or more At least one of the recommendations (eg, purchase recommendations) is associated with one or more stored keyword matches to identify the one or more recommendations. The system of claim 91, wherein each suggestion is associated with a set of one or more SNPs, and each of the SNPs of the SNP set of the one or more stored keywords for the genetic profile test includes the SNP An associated name of a gene (eg, a name of a gene in which the SNP occurs; for example, a name of a gene in which the SNP affects transcription). The system of claim 92, wherein at least one of the one or more recommendations is a genetic profile test that is associated with (e.g., represented by a product) a general health-related phenotype and corresponds to A measurement of a particular set of one or more SNPs (e.g., where each corresponding SNP affects a particular health-related trait associated with the general health-related phenotype associated with the genetic profile test) and is associated with the The set of SNPs associated with the genetic profile test is the SNPs measured by the genetic profile test. The system of any one of claims 91 to 93, wherein each suggestion is associated with a set of one or more genes, and the one or more keywords include names corresponding to the genes associated with the suggestion One or more keywords. The system of any one of claims 90 to 94, wherein the instructions, when executed by the processor, cause the processor to be based in part on one or more reference files stored in a database of reference files. The information identifies the one or more suggestions. The system of claim 95, wherein the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by: accessing a plurality of reference documents (e.g., published literature; e.g., , A plurality of web pages of a public database), a database in which each reference file is associated with one or more SNPs and / or genes associated with one or more suggestions (for example, each reference file includes information about one or more Information about the specific health-related phenotypes of multiple SNPs and / or genes and their effects); using the text query and the information in the plurality of reference documents to determine one or more SNPs related to the user text query [E.g., by comparing the text query with textual data (e.g., written terms) within the reference document (e.g., by extracting keywords from the text query and searching for matches in those reference documents; for example, Using a machine learning module that receives the text query as input and determines the one or more SNPs related to the user text query based on the reference documents)]; for one or more expected suggestions Each, determine a degree of match between the one or more SNPs and / or genes associated with the user text query and the SNPs and / or genes associated with the prospective suggestion; and based on the determined The matching degree identifies the one or more suggestions. The system of any one of claims 90 to 96, wherein the instructions, when executed by the processor, cause the processor to be based on a SNP in one of the genomes of the user (e.g., via one of the users (One or more genetic profile recognition) a mutation automatically identifies the one or more suggestions. The system of any one of claims 90 to 97, wherein: at least one of the identified recommendations is associated with one or more SNPs, and for each of the one or more associated SNPs, the recommendation is associated with A specific variation of the SNP (e.g., identified by a qualifier) is associated, and the identification of the suggestion is based at least in part on the specific variation of one or more SNPs associated with the suggestion and the user has the Correspondence (e.g., relationship; e.g., correlation) between specific variations of one or more SNPs as identified in one or more genetic profiles of the user. The system of any one of claims 90 to 98, wherein the instructions, when executed by the processor, cause the processor to: receive (and / or access) the information recorded by one of the user's mobile health devices Mobile health data; and automatically identifying the one or more recommendations based on the genetic profile of the user and the received mobile health data. The system of any one of claims 90 to 99, wherein the one or more suggestions include a suggested genetic profile test. The system of any one of claims 90 to 100, wherein the one or more recommendations include a suggested diagnostic test (e.g., a test for a particular disease and / or condition, such as a blood test for cholesterol index; for example, A blood test; for example, a slice test). The system of any one of claims 90 to 101, wherein the one or more recommendations include purchase recommendations for one or more supplements (e.g., nutritional supplements). The system of any one of claims 90 to 102, wherein the one or more suggestions include one or more members selected from the group consisting of: a diet plan, an all-around fitness plan, and an electroencephalogram feedback A protocol, a behavioral protocol (eg, a focused protocol, an ADHD adjuvant protocol), and a somatization therapy. The system of claim 103, wherein the one or more members are individualized protocols and / or treatments based on the one or more genetic profiles of the user. The system of any one of claims 90 to 104, wherein the one or more suggestions include one or more mobile health devices (and / or one or more software apps operating on a mobile health device) or Multiple recommended purchases. If the system of any one of claims 90 to 105, the structured response includes data corresponding to the location and name of one of the merchants from which the user can purchase the proposal, and the structured response is in When the voice assistant is executed, the voice assistant is caused to generate an audio output that provides the location and name of the business.