WO2018204763A2

WO2018204763A2 - Systems and methods for generating genetic profile test and related purchase recommendations via an artificial intelligence-enhanced chatbot

Info

Publication number: WO2018204763A2
Application number: PCT/US2018/031055
Authority: WO
Inventors: Robin Y. SMITH; Marcie A. Glicksman; Sunil Anant GUPTA
Original assignee: Orig3N, Inc.
Priority date: 2017-05-05
Filing date: 2018-05-04
Publication date: 2018-11-08
Also published as: TW201909001A; WO2018204763A4; WO2018204763A3

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

SYSTEMS AND METHODS FOR GENERATING GENETIC PROFILE TEST AND RELATED PURCHASE RECOMMENDATIONS VIA AN ARTIFICIAL INTELLIGENCE-ENHANCED CHATBOT

CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to and the benefit of U.S. Provisional Patent

Application Serial No. 62/502,556 filed on May 5, 2017, the content of which are hereby incorporated by reference herein in its entirety.

FIELD OF THE INVENTION [0002] This invention is related generally to systems and methods for facilitating purchase recommendations to users of genetic profile products.

BACKGROUND OF THE INVENTION [0003] Genomes hold valuable information that can be used to better understand characteristics of living things. Much research is being conducted to establish relationships between the human genome and biological characteristics and traits, in particular. For example, single nucleotide polymorphisms (SNPs) are specific sites identified in particular genes that influence biological characteristics and traits depending on the particular polymorphism of an individual. Different polymorphisms of the nucleotides at a specific site influence the relevant characteristic or trait differently. Relationships between the variants of SNPs and their corresponding biological characteristics and traits have been established and many more possible relationships are currently undiscovered and under investigation. [0004] Personalized genetic profiles, such as LifeProfile offered by Orig3n, Inc. of

Boston, Massachusetts, provide SNP -based assessments of various characteristics and traits using simple cheek swab samples, providing secure, user-friendly, smartphone accessible test results. Individuals provide a biological sample and receive an assessment of their genetic profile that is accessible for review on their smartphones. Individuals can learn from their genetic profile how their genome impacts their personal health

characteristics, fitness characteristics, and dietary characteristics, for example. The number of personalized genetic profile products available to consumers will continue to increase as more links between genotyping data and various traits and characteristics are identified. The amount of such information available to consumers will expand, and it will be important that there are ways of organizing and presenting the information in an

understandable, consumer-friendly manner.

[0005] Many individuals take vitamins, supplements, and other over-the-counter or prescription medications on a recurring basis in order to enhance their wellbeing. Often, supplements and medications are taken to relieve chronic conditions. For example, some individuals take glucosamine to deal with joint pain. Supplements may also be taken to boost performance or function. For example, some individuals take supplements when weightlifting, such as nitric oxide, in order to boost their improvements in physique and strength. Individuals frequently self-prescribe such supplements based on personal research or physician recommendation. Thus, the decisions of individuals are made largely on qualitative information about how an individual feels or how the patient's condition, as described, sounds to a physician. Similarly, individuals choose fitness programs, meal plans, and other health and fitness-related regimens based on such qualitative information. Information available via personalized genetic profile products can better inform a specific consumer's decisions regarding which vitamins, supplements, and/or over-the-counter medications to take, as well as decisions regarding what fitness programs, meal programs, stress management programs, and/or other health and wellbeing-related programs are best suited for the specific consumer. However, there remains a need to automatically identify and present information about such recommendations to a consumer in an understandable, consumer-friendly manner.

[0006] Thus, there is a need for automated systems and methods to assist individuals in the selection of relevant genetic profile tests to provide personalized information of interest to them, and there is a need for automated systems and methods that provide recommendations about supplements, health and fitness programs, meal plans, stress management plans, and the like, that are suitable for them based on their genetic profile.

SUMMARY

[0007] 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 and/or to automatically identify recommendations (e.g., 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, recommendations of nutritional supplements to purchase, recommendations about specific programs (e.g., meal programs, fitness programs, etc.) that are well-suited for the user, recommendations of additional diagnostic tests (e.g., additional genetic profile tests, e.g., tests for particular characteristics, traits, diseases, and/or conditions), and the like.

[0008] In certain embodiments, the artificial intelligence chatbot described herein identifies recommendations for purchases (e.g., purchase recommendations) based on a user question and how a user's personal genome affects his/her biological traits (e.g., health-related phenotypes). For example, recommendations for supplements may be identified based on genetic profile test results (e.g., genotyping data) for the user, which indicate (e.g., are correlated with), for example, a need for a particular supplement.

Genetic profile test results are obtained from biological samples provided by individuals, and the genetic profile test results include genotyping data associated with a range of biological characteristics. Genotyping data may be stored as a genetic profile, and the artificial intelligence chatbot may, in response to a user question, access the user's genetic profile to identify appropriate recommendations for the individual. These

recommendations can be associated with various characteristics of the user, which are determined based on his/her genetic profile. For example, the user's genetic profile can reveal (i) nutritional characteristics (e.g., the way in which an individual's body processes different foods and nutrients), (ii) skin health, (iii) physical fitness, and (iv) personal behavior tendencies (e.g., empathy, risk of addiction, and tolerance for stress and pain), and these characteristics can be used to identify recommendations for the user.

[0009] For example, based on an individual's particular biological traits, the chatbot may, in response to a user question, identify one or more supplements a user may wish to purchase. For example, in response to a user question about nutrition, the chatbot may recommend one or more nutritional supplements that are identified based on the user's genetic profile. For example, if the user's genetic profile indicates that the user has a decreased ability to process certain foods, the chatbot may recommend nutritional supplements which aid in processing foods. One or more purchase recommendations for these supplements or links to purchase recommendations can be presented to the user (e.g., in a graphical user interface presented on a personal computing device). Thus, in certain embodiments, users can easily view recommendations for supplements to purchase based on a question that is asked and information in the user's genetic profile. The user may also have the ability to directly purchase or be redirected to purchase the supplements.

[0010] In one aspect, the invention is directed to a method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with an artificial intelligence chatbot, the method comprising: (a) receiving, by a processor of a computing device, user input of a textual query; (b) identifying, by the processor, based on the textual query, one or more genetic profile tests related to the textual query (e.g., using a machine learning module), wherein each genetic profile test is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more S Ps (e.g., wherein each corresponding S P influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with); and (c) providing, by the processor, for each of the one or more identified genetic profile tests, a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising an identification of the genetic profile test (e.g., a name of the test, rendered as text; e.g., an image associated with the test). [0011] In certain embodiments, identifying the one or more genetic profile tests comprises: accessing, by the processor, a database (e.g., a set of text files such as AIML files) comprising, for each of a predefined set of genetic profile tests: (i) an identifier [e.g., a textual label (e.g., representing a name of the genetic profile test)] 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, matching one or more terms in the textual query to (i) the identifier of the genetic profile test and/or (ii) at least one of the one or more keywords.

[0012] In certain embodiments, the one or more keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0013] In certain embodiments, the method comprises identifying the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

[0014] In certain embodiments, identifying the one or more genetic profile tests comprises: accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., each reference document comprises information regarding one or more SNPs and the specific health-related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and identifying the one or more genetic profile tests based on the determined degree of matching.

[0015] In certain embodiments, the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

[0016] In certain embodiments, the method comprises causing display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

[0017] In certain embodiments, the method comprises causing display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in). [0018] In certain embodiments, the method comprises causing display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

[0019] In certain embodiments, the textual query is provided by a voice assistant.

[0020] In another aspect, the invention is directed to a method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with an artificial intelligence chatbot, the method comprising: (a) receiving, by a processor of a computing device, user input of a textual query, wherein the user is associated with one or more genetic profiles [e.g., the one or more genetic profiles representing results of genetic profile tests performed for the user; e.g., wherein the user is a subscribed (e.g., logged in) user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor]; (b) identifying, by the processor, based on the textual query, one or more recommendations (e.g., purchase recommendation(s)) responsive to the received user input and based at least in part on the one or more genetic profiles for the user (e.g., using a machine learning module); and (c) providing, by the processor, a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising the one or more recommendations [e.g., a name of a recommended purchase (e.g., a nutritional supplement; e.g., a mobile health device to purchase), rendered as text; e.g., an image associated with a recommended purchase, a textual description responsive to the query (e.g., a recommended meal plan, a recommended exercise plan, etc.)].

[0021] In certain embodiments, identifying the one or more recommendations comprises: accessing, by the processor, a database (e.g., a set of text files such as AIML files) comprising, for each of a predefined set of recommendations: (i) an identifier [e.g., a textual label (e.g., representing a name of the recommendation)] of the recommendation; and (ii) one or more keywords associated with the identifier of the recommendation; and for each identified recommendation, matching one or more terms in the textual query to (i) the identifier of the recommendation and/or (ii) at least one of the one or more keywords.

[0022] In certain embodiments, each recommendation is associated with a set of one or more S Ps, and the one or more keywords comprise, for each S P of the set of S Ps associated with the recommendation, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0023] In certain embodiments, at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

[0024] In certain embodiments, each recommendation of the one or more recommendations is associated with a set of one or more genes, and the one or more keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

[0025] In certain embodiments, the method comprises identifying the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

[0026] In certain embodiments, identifying the one or more recommendations comprises: accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations (e.g., each reference document comprises information regarding one or more SNPs and/or genes and the specific health- related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective recommendations, determining a degree of matching between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation; and identifying the one or more recommendations based on the determined degree of matching. [0027] In certain embodiments, the method comprises automatically identifying the one or more recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

[0028] In certain embodiments, 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 particular variant of the SNP (e.g., identified via a qualifier), and the identification of the recommendation is based at least in part on a correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation, and particular variants of the one more SNPs that the user has, as identified in the genetic profiles associated with the user.

[0029] In certain embodiments, the method comprises: receiving (and/or accessing) mobile health data recorded by a mobile health device of the user; and automatically identifying the one or more recommendations based on the genetic profiles of the user and the received the mobile health data.

[0030] In certain embodiments, the one or more recommendations comprise a recommended genetic profile test.

[0031] In certain embodiments, the one or more recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy).

[0032] In certain embodiments, the one or more recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase.

[0033] In certain embodiments, the one or more recommendations comprise one or more members selected from the group consisting of a meal program, a fitness program, a brain wave feedback program, a behavioral program (e.g., a focus program, an ADHD assistance program), and an individualized therapy. In certain embodiments, the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

[0034] In certain embodiments, the one or more recommendations comprise one or more recommended purchases of one or more mobile health devices (and/or one or more software apps operating on a mobile health device).

[0035] In certain embodiments, the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

[0036] In certain embodiments, the method comprises causing display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

[0037] In certain embodiments, the method comprises causing display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

[0038] In certain embodiments, the method comprises causing display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

[0039] In certain embodiments, the textual query is provided by a voice assistant.

[0040] In another aspect, the invention is directed to a method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with a voice assistant, the method comprising: (a) receiving (e.g., via a network), by a processor of a computing device, from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion of the matching recognizable textual phrases as terms in the textual query)]; (b) identifying, by the processor, using the textual query of the structured request, one or more genetic profile tests related to the user speech [e.g., by matching the textual query (e.g., one or more terms of the textual query) to one or more identifiers of genetic profile tests (e.g., stored in a database) (e.g., via a machine learning module; e.g., by identifying one or more subroutines based on a first portion of the textual query and passing a second portion of the textual query to the identified sub-routines as variables evaluated by the sub-routines to identify the one or more genetic profile tests)]; and (c) providing (e.g., via a network), by the processor, to the voice assistant, one or more structured responses comprising identifications of each of the one or more genetic profile tests, 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 simulated speech based on the structured response, thereby providing to the user audio feedback

corresponding to recommendations associated with the one or more identified genetic profile tests.

[0041] In certain embodiments, step (b) comprises matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with one or more genetic profile tests.

[0042] In certain embodiments, the one or more keywords comprise, for each SNP of a set of SNPs that the one or more genetic profile tests measure, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0043] In certain embodiments, the method comprises identifying the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

[0044] In certain embodiments, identifying the one or more genetic profile tests comprises: accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., wherein each reference document comprises information regarding one or more SNPs and the specific health related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and identifying the one or more genetic profile tests based on the determined degree of matching.

[0045] In another aspect, the invention is directed to a method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with a voice assistant, the method comprising: (a) receiving (e.g., via a network), by a processor of a computing device, from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion (up to all) of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion (up to all) of the matching recognizable textual phrases as terms in the textual query)], and wherein the textual query of the structured request comprises an identification of the user (e.g., a subscribed user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor) associated with one or more genetic profiles; (b) identifying, by the processor, using the textual query of the structured request, one or more recommendations (e.g., purchase recommendations) based at least in part on the one or more genetic profiles associated with the user; and (c) providing (e.g., via a network), by the processor, to the voice assistant, one or more structured responses comprising identifications of each of the one or more recommendations, 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 simulated speech based on the structured response, thereby providing to the user audio feedback corresponding to the one or more recommendations.

[0046] In certain embodiments, step (b) comprises matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with at least one of the one or more recommendations (e.g., purchase recommendations).

[0047] In certain embodiments, each recommendation of the one or more recommendations is associated with a set of one or more SNPs, and the one or more stored keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0048] In certain embodiments, at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

[0049] In certain embodiments, each recommendation is associated with a set of one or more genes, and the one or more stored keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

[0050] In certain embodiments, the method comprises identifying the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

[0051] In certain embodiments, identifying the one or more recommendations comprises: accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations (e.g., each reference document comprises information regarding one or more SNPs and/or genes and the specific health- related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective recommendations, determining a degree of matching between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation; and identifying the one or more recommendations based on the determined degree of matching.

[0052] In certain embodiments, the method comprises automatically identifying the one or more recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

[0053] In certain embodiments, 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 particular variant of the SNP (e.g., identified via a qualifier), and the identification of the recommendation is based at least in part on a correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation, and particular variants of the one more SNPs that the user has, as identified in the user's one or more genetic profiles.

[0054] In certain embodiments, the method comprises receiving (and/or accessing) mobile health data recorded by a mobile health device of the user; and automatically identifying the one or more recommendations based on the genetic profiles of the user and the received the mobile health data.

[0055] In certain embodiments, the one or more recommendations comprise a recommended genetic profile test.

[0056] In certain embodiments, the one or more recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy). [0057] In certain embodiments, the one or more recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase.

[0058] In certain embodiments, the one or more recommendations comprise one or more members selected from the group consisting of a meal program, a fitness program, a brain wave feedback program, a behavioral program (e.g., a focus program, an ADHD assistance program), and an individualized therapy.

[0059] In certain embodiments, the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

[0060] In certain embodiments, the one or more recommendations comprise one or more recommended purchases of one or more mobile health devices (and/or one or more software apps operating on a mobile health device). In certain embodiments, the structured response comprises data corresponding to an identification of a location and name of a vendor from which the user can purchase the recommendation and, the structure response, when executed by the voice assistant, causes the voice assistant to generate audio output providing the location and name of the vendor.

[0061] In another aspect, the invention is directed to a system for providing purchase recommendations corresponding to genetic profile tests via a user interaction with an artificial intelligence chatbot, the system comprising: a processor; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive user input of a textual query; (b) identify, based on the textual query, one or more genetic profile tests related to the textual query (e.g., using a machine learning module), wherein each genetic profile test is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with); and (c) provide, for each of the one or more identified genetic profile tests, a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising an identification of the genetic profile test (e.g., a name of the test, rendered as text; e.g., an image associated with the test).

[0062] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests by: accessing a database (e.g., a set of text files such as AIML files) comprising, for each of a predefined set of genetic profile tests: (i) an identifier [e.g., a textual label (e.g., representing a name of the genetic profile test)] 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, matching one or more terms in the textual query to (i) the identifier of the genetic profile test and/or (ii) at least one of the one or more keywords.

[0063] In certain embodiments, the one or more keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0064] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents. [0065] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests by: accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database), wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., each reference document comprises information regarding one or more SNPs and the specific health-related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and identifying the one or more genetic profile tests based on the determined degree of matching.

[0066] In certain embodiments, the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test. [0067] In certain embodiments, the instructions, when executed by the processor, cause the processor to cause display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

[0068] In certain embodiments, the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

[0069] In certain embodiments, the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

[0070] In certain embodiments, the textual query is provided by a voice assistant.

[0071] In another aspect, the invention is directed to a system for providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with an artificial intelligence chatbot, the system comprising: a processor; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive user input of a textual query, wherein the user is associated with one or more genetic profiles [e.g., the one or more genetic profiles representing results of genetic profile tests performed for the user; e.g., wherein the user is a subscribed (e.g., logged in) user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor]; (b)identify, based on the textual query, one or more recommendations (e.g., purchase recommendation(s)) responsive to the received user input and based at least in part on the one or more genetic profiles for the user (e.g., using a machine learning module); and (c) provide a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising the one or more recommendations [e.g., a name of a recommended purchase (e.g., a nutritional supplement; e.g., a mobile health device to purchase), rendered as text; e.g., an image associated with a recommended purchase, a textual description responsive to the query (e.g., a recommended meal plan, a recommended exercise plan, etc.)].

[0072] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by: accessing, by the processor, a database (e.g., a set of text files such as AIML files) comprising, for each of a predefined set of recommendations: (i) an identifier [e.g., a textual label (e.g., representing a name of the recommendation)] of the recommendation; and (ii) one or more keywords associated with the identifier of the recommendation; and for each identified

recommendation, matching one or more terms in the textual query to (i) the identifier of the recommendation and/or (ii) at least one of the one or more keywords.

[0073] In certain embodiments, each recommendation is associated with a set of one or more S Ps, and the one or more keywords comprise, for each S P of the set of S Ps associated with the recommendation, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the S P occurs; e.g., a name of a gene whose transcription the SNP influences).

[0074] In certain embodiments, at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

[0075] In certain embodiments, each recommendation is associated with a set of one or more genes, and the one or more keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

[0076] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

[0077] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by: accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations (e.g., each reference document comprises information regarding one or more SNPs and/or genes and the specific health-related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective recommendations, determining a degree of matching between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation; and identifying the one or more recommendations based on the determined degree of matching.

[0078] In certain embodiments, the instructions, when executed by the processor, cause the processor to automatically identify the one or more recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

[0079] In certain embodiments, 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 particular variant of the SNP (e.g., identified via a qualifier), and the identification of the recommendation is based at least in part on a correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation, and particular variants of the one more SNPs that the user has, as identified in the genetic profiles associated with the user.

[0080] In certain 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 the user; and automatically identify the one or more recommendations based on the genetic profiles of the user and the received the mobile health data.

[0081] In certain embodiments, the one or more recommendations comprise a recommended genetic profile test. In certain embodiments, the one or more

recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy). In certain embodiments, the one or more recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase.

[0082] In certain embodiments, the one or more recommendations comprise one or more members selected from the group consisting of a meal program, a fitness program, a brain wave feedback program, a behavioral program (e.g., a focus program, an ADHD assistance program), and an individualized therapy. In certain embodiments, the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

[0083] In certain embodiments, the one or more recommendations comprise one or more recommended purchases of one or more mobile health devices (and/or one or more software apps operating on a mobile health device).

[0084] In certain embodiments, the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test. [0085] In certain embodiments, the instructions, when executed by the processor, cause the processor to cause display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

[0086] In certain embodiments, the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

[0087] In certain embodiments, the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

[0088] In certain embodiments, the textual query is provided by a voice assistant.

[0089] In another aspect, the invention is directed to a system for providing purchase recommendations corresponding to genetic profile tests via a user interaction with a voice assistant, the system comprising: a processor; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive (e.g., via a network), from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion of the matching recognizable textual phrases as terms in the textual query)]; (b) identify, using the textual query of the structured request, one or more genetic profile tests related to the user speech [e.g., by matching the textual query (e.g., one or more terms of the textual query) to one or more identifiers of genetic profile tests (e.g., stored in a database) (e.g., via a machine learning module; e.g., by identifying one or more subroutines based on a first portion of the textual query and passing a second portion of the textual query to the identified sub-routines as variables evaluated by the sub-routines to identify the one or more genetic profile tests)]; and (c) provide (e.g., via a network), to the voice assistant, one or more structured responses comprising identifications of each of the one or more genetic profile tests, 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 simulated speech based on the structured response, thereby providing to the user audio feedback corresponding to recommendations associated with the one or more identified genetic profile tests.

[0090] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests related to the user speech by matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with one or more genetic profile tests. [0091] In certain embodiments, the one or more keywords comprise, for each SNP of a set of SNPs that the one or more genetic profile tests measure, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0092] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

[0093] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests by: accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., wherein each reference document comprises information regarding one or more SNPs and the specific health related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and identifying the one or more genetic profile tests based on the determined degree of matching.

[0094] In another aspect, the invention is directed to a system for providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with a voice assistant, the system comprising: a processor; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) receive (e.g., via a network), from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion (up to all) of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion (up to all) of the matching recognizable textual phrases as terms in the textual query)], and wherein the textual query of the structured request comprises an identification of the user (e.g., a subscribed user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor) associated with one or more genetic profiles; (b)identify, using the textual query of the structured request, one or more recommendations (e.g., purchase recommendations) based at least in part on the one or more genetic profiles associated with the user; and (c) provide (e.g., via a network), to the voice assistant, one or more structured responses comprising

identifications of each of the one or more recommendations, 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 simulated speech based on the structured response, thereby providing to the user audio feedback

corresponding to the one or more recommendations.

[0095] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with at least one of the one or more recommendations (e.g., purchase recommendations).

[0096] In certain embodiments, each recommendation is associated with a set of one or more SNPs, and the one or more stored keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0097] In certain embodiments, at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures. [0098] In certain embodiments, each recommendation is associated with a set of one or more genes, and the one or more keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

[0099] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

[0100] In certain embodiments, the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by: accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations (e.g., each reference document comprises information regarding one or more SNPs and/or genes and the specific health-related phenotypes that they influence); determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective recommendations, determining a degree of matching between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation; and identifying the one or more recommendations based on the determined degree of matching.

[0101] In certain embodiments, the instructions, when executed by the processor, cause the processor to automatically identify the one or more recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

[0102] In certain embodiments, 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 particular variant of the SNP (e.g., identified via a qualifier), and the identification of the recommendation is based at least in part on a correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation, and particular variants of the one more SNPs that the user has, as identified in the user's one or more genetic profiles.

[0103] In certain 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 the user; and automatically identify the one or more recommendations based on the genetic profiles of the user and the received the mobile health data.

[0104] In certain embodiments, the one or more recommendations comprise a recommended genetic profile test. In certain embodiments, the one or more

recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy). In certain embodiments, one or more recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase. In certain embodiments, the one or more recommendations comprise one or more members selected from the group consisting of a meal program, a fitness program, a brain wave feedback program, a behavioral program (e.g., a focus program, an ADHD assistance program), and an individualized therapy.

[0105] In certain embodiments, the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

[0106] In certain embodiments, the one or more recommendations comprise one or more recommended purchases of one or more mobile health devices (and/or one or more software apps operating on a mobile health device).

[0107] In certain embodiments, the structured response comprises data

corresponding to an identification of a location and name of a vendor from which the user can purchase the recommendation and, the structure response, when executed by the voice assistant, causes the voice assistant to generate audio output providing the location and name of the vendor.

DEFINITIONS

[0108] In order for the present disclosure to be more readily understood, certain terms used herein are defined below. Additional definitions for the following terms and other terms may be set forth throughout the specification.

[0109] In this application, the use of "or" means "and/or" unless stated otherwise.

As used in this application, the term "comprise" and variations of the 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 numerals used in this application with or without about/approximately are meant to cover any normal fluctuations appreciated by one of ordinary skill in the relevant art. In certain embodiments, the term "approximately" or "about" refers to a range of values that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of the stated reference value unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value).

[0110] Genotyping data: As used herein, the term "genotyping data" refers to data obtained from measurements of a genotype and/or to results obtained from a genetic profile test (e.g., genetic profile test results). Measurements of a genotype performed on a biological sample identify the particular nucleotide(s) (also referred to as "bases") that is/are incorporated at one or more particular positions in genetic material extracted from the biological sample. Accordingly, genotyping measurements for a particular individual are measurements performed on a biological sample of from the individual, and which identify the particular nucleotides present at one or more specific positions within their genome.

[0111] In certain embodiments, genotyping data describes an individual's phenotype. Genotyping data may be measurements of particular genes (e.g., portions of an individual's genetic sequence, e.g., DNA sequence), S Ps, or variants of SNPs. For example, a genotyping measurement of a particular SNP for an individual identifies the particular variant of that SNP that the individual has. A genotyping measurement of a particular gene for an individual identifies the particular nucleotides that are present at one or more locations within and/or in proximity to the gene for the individual. For example, genotyping measurements of a particular gene may identify the particular variants of one or more SNPs associated with a particular gene.

[0112] In certain embodiments, genotyping data is obtained from a multi-gene panel. In certain embodiments, genotyping data is obtained from assays (e.g., TaqMan™ assays) that detect one or more specific variants of specific SNPs. In certain embodiments, genotyping data is obtained from genetic sequencing measurements. In certain

embodiments, genotyping data is generated in response to a purchase or request by an individual. In certain embodiments, genotyping data comprises data for a portion of a genotype (e.g., of an individual). In certain embodiments, genotyping data comprises all available measurements of a genotype (e.g., of an individual).

[0113] Supplement: As used herein, the term "supplement" refers to a product ingested, consumed, and/or applied by a user in order to do at least one of: enhance wellbeing, improve performance or function, and counteract effects of a chronic condition. A supplement may be a vitamin, multivitamin, mineral, dietary supplement, herb, botanical, concentrate, metabolite, extract, amino acid, over-the-counter medication, prescription medication, topical product, or health/treatment regimen or program. In certain embodiments, a supplement is to be taken on a recurring basis (e.g., daily or twice daily) by a user for a period of time. A period of time may be an ongoing basis with no pre-determined cessation period. In certain embodiments, a supplement is a program or regimen that a user can enroll in or purchase access to. For example, a supplement may be a behavioral program such as a focus program or a personalized fitness plan (e.g., for use in home exercise). [0114] Variant: As used herein, the terms "variant" refers to a specific variation of a specific S P occurring in the genetic material of a population. In certain embodiments, a variant is a specific combination of a first allele of a first copy of an individual's genetic material (e.g., corresponding to an individual's paternal DNA) and a second allele of a second copy of an individual's genetic material (e.g., corresponding to an individual's maternal DNA), as occurs in diploid organisms (e.g., humans).

[0115] Qualifier: As used herein, the term "qualifier" refers to a classification

(e.g., a label) of a particular variant of a given SNP. The qualifier associated with a given variant is the particular classification (e.g., label) of that variant. For example, a given variant may be associated with a particular qualifier of a predefined set of possible qualifiers. For example, a given variant may be associated with a qualifier selected from a group of labels such as "Adapt," "Normal," and "Gifted." In certain embodiments, for a given variant of a given SNP, a qualifier corresponds to a classification of the given variant based on (/^') the prevalence of the given variant within a population (e.g., if the variant is common, e.g., if the variant is rare) and/or {if) a health-related trait associated with the variant. For example, a common variant may be associated with the qualifier "Normal". A rare variant that confers a disadvantageous phenotype, such as a

predisposition to high cholesterol, may be associated with the qualifier "Adapt" (e.g., classified as rare and disadvantageous). A rare variant that confers an advantageous phenotype, such as a predisposition to lower cholesterol, may be associated with the qualifier "Gifted" (e.g., accordingly, the variant is classified as rare and advantageous). [0116] Variant object: As used herein, the term "variant object" refers to a data structure corresponding to (e.g., that is used to represent) a specific variant of a physical gene within a given genome (e.g., the genome of a human).

[0117] Voice Assistant: As used herein, the term "voice assistant" refers to a device which can provide audio interaction with an individual (e.g., a user of the voice assistant). For example, as opposed to providing an alphanumeric input, for example by typing an textual entry in a GUI, a user may speak to a voice assistant and be provided with computer-generated speech as feedback. For example, as described herein, a structured request comprising a textual query can be generated by a voice assistant in response to user speech. A voice assistant may, for example, generate a structured response by detecting and/or recognizing user speech, generating speech data corresponding to at least a portion of the detected/recognized speech, and processing the speech data to generate a textual query. The textual query may be provided to (e.g., received by) a computing device (e.g., via a network). Furthermore, in certain embodiments, the voice assistant may deliver an audible simulated speech response to the textual query (e.g., a recommendation or other information in response to a user query). In certain embodiments, a voice assistant includes hardware components in the vicinity of 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., signals corresponding to detected speech may be conveyed electronically, e.g., via a network).

[0118] SNP object: As used herein, the term "SNP object" refers to a data structure corresponding to (e.g., that is used to represent) a specific single nucleotide polymorphism (SNP). In certain embodiments, a SNP object comprises a SNP reference that identifies the specific SNP to which the SNP object corresponds. The SNP reference may be an alphanumeric code such as an accepted name of the SNP or other identifying mark or label capable of being stored electronically. The SNP reference may be an alphanumeric code such as a National Center for Biotechnology Information (NCBI) database reference number.

[0119] Gene object: As used herein, the term "gene object" refers to a data structure corresponding to (e.g., that is used to represent) a specific physical gene within a given genome (e.g., the human genome).

[0120] Category: As used herein, the term "category" refers to a data structure corresponding to (e.g., that is used to represent) a particular health-related trait or characteristic.

[0121] Product, Genetic Profile Product, Personal Genetic Profile Product: As used herein, the terms "product," "genetic profile product," and "personal genetic profile product," refer to a data structure corresponding to (e.g., that is used to represent) a general class of health-related traits and/or characteristics. In certain embodiments, a product is associated with one or more categories that correspond to health-related traits and characteristics related to the general class of health-related traits and characteristics to which the product corresponds.

[0122] Genetic profile Assessment: As used herein, the term "genetic profile assessment" refers to a data structure (e.g., a hierarchy of data structures) corresponding to (e.g., that is used to represent) the phenotype of a user for one or more general classes of health-related traits and/or characteristics. In certain embodiments, a genetic profile assessment of a user is generated by associating genotyping data of the user with premade (e.g., stored) generic genetic profile products. In certain embodiments, a user's genetic profile assessment is viewed using an assessment graphical user interface ("assessment GUI") on a computing device (e.g., a smartphone).

[0123] Developer: As used herein, the term "developer" refers to a person, company, or organization that uses a graphical user interface to create data structures. In certain embodiments, a developer also genotypes a biological sample in response to an assessment corresponding to a product being purchased or made accessible to an individual.

[0124] User: As used herein, the term "user" refers to a person who uses an assessment graphical user interface in order to view information about a genome. The user may supply one or more biological samples to be genotyped in order for a genetic profile assessment to be formed. The user may purchase or be given access to one or more products in order to view a genetic profile assessment. The user may purchase one or more supplements from a list of purchase recommendations provided in the graphical user interface that are based on the user's genetic profile assessment. The terms "user" and "individual" are used interchangeably herein.

[0125] Graphical Control Element: As used herein, the term "graphical control element" refers to an element of a graphical user interface element that may be used to provide user and/or individual input. A graphical control element may be a textbox, dropdown list, radio button, data field, checkbox, button (e.g., selectable icon), list box, or slider.

[0126] Associate, Associated with: As used herein, the terms "associate," and

"associated with," as in a first data structure is associated with a second data structure, refer to a computer representation of an association between two data structures or data elements that is stored electronically (e.g., in computer memory).

[0127] Provide: As used herein, the term "provide", as in "providing data", refers to a process for passing data in between different software applications, modules, systems, and/or databases. In certain embodiments, providing data comprises the execution of instructions by a process to transfer data in between software applications, or in between different modules of the same software application. In certain embodiments, a software application may provide data to another application in the form of a file. In certain embodiments, an application may provide data to another application on the same processor. In certain embodiments, standard protocols may be used to provide data to applications on different resources. In certain embodiments, a module in a software application may provide data to another module by passing arguments to that module.

[0128] Mobile health device: As used herein, the term "mobile health device", refers to any one of a variety of mobile devices that a user uses to record data such as biological/physical measurements as well as activity data about activities they perform related to physical health. Data recorded by a mobile health device is referred to herein as "mobile health data". In certain embodiments, mobile health data includes measurements such as weight, glucose levels, recorded calorie intake, as well as data about physical activities such as an average or aggregate number of steps taken over a given period of time, recorded workouts (e.g., as recorded by a fitness monitoring software app operating on a mobile health device), sleep quality data, and brain wave data (e.g., EEG

measurements). In certain embodiments, mobile health devices are network connected devices, such that mobile health data recorded by a given mobile health device can be accessed and/or received by a processor (e.g. of another computing device) over a network. In certain embodiments, mobile health devices include activity tracking devices (e.g., devices for monitoring exercise, steps, pulse rate, sleep, eating, or other activity), mobile phones (e.g., smartphones), tablet computers, brain activity monitoring devices (e.g., devices for monitoring mental focus, alertness, mental stress, relaxation, sleep, or the like), connected home devices (e.g., a network connected scale).

BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Drawings are presented herein for illustration purposes, not for limitation.

The foregoing and other objects, aspects, features, and advantages of the invention will become more apparent and may be better understood by referring to the following description taken in conjunction with the accompanying drawings, in which:

[0130] FIG. 1 A is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0131] FIG. IB is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0132] FIG. 1C is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0133] FIG. ID is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0134] FIG. IE is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0135] FIG. IF is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention; [0136] FIG. 1G is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0137] FIG. 1H is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0138] FIG. II is a screenshot of a user interaction with an artificial intelligence chatbot via a messaging interface, according to an illustrative embodiment of the invention;

[0139] FIG. 2 is a block flow diagram of a method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with an artificial intelligence chatbot, according to an illustrative embodiment of the invention;

[0140] FIG. 3 A is a portion of code (an AIML file) for matching a user input to a genetic profile test, according to an illustrative embodiment of the invention;

[0141] FIG. 3B is a portion of code (an ADVIL file) for providing an identification to the user of a genetic profile test which may be purchased, according to an illustrative embodiment of the invention;

[0142] FIG. 4A is a block flow diagram of a method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with a voice assistant, according to an illustrative embodiment of the invention;

[0143] FIG. 4B is a block flow diagram of a method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with an artificial intelligence chatbot, according to an illustrative embodiment of the invention;

[0144] FIG. 4C is a block flow diagram of a method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with a voice assistant, according to an illustrative embodiment of the invention; [0145] FIG. 5 is a block diagram of a method for linking supplement purchase recommendations with personal genetic profile products, according to an illustrative embodiment of the invention;

[0146] FIG. 6 is a block diagram illustrating associations between different data structures in a genetic profile product, according to an illustrative embodiment of the invention;

[0147] FIG. 7 is a block diagram showing an organizational hierarchy of a personal genetic profile product, according to an illustrative embodiment of the invention;

[0148] FIG. 8 is a block flow diagram showing a process for creating a genetic profile assessment, according to an illustrative embodiment of the invention;

[0149] FIG. 9 is a portion of a text file comprising genotyping data, according to an illustrative embodiment of the invention;

[0150] FIG. 10 is a block diagram of an example network environment for use in the methods and systems described herein, according to an illustrative embodiment; and

[0151] FIG. 11 is a block diagram of an example computing device and an example mobile computing device, for use in illustrative embodiments of the invention.

[0152] The features and advantages of the present disclosure will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. DETAILED DESCRIPTION OF THE INVENTION

[0153] It is contemplated that systems, architectures, devices, methods, and processes of the claimed invention encompass variations and adaptations developed using information from the embodiments described herein. Adaptation and/or modification of the systems, architectures, devices, methods, and processes described herein may be performed, as contemplated by this description.

[0154] Throughout the description, where articles, devices, systems, and architectures are described as having, including, or comprising specific components, or where processes and methods are described as having, including, or comprising specific steps, it is contemplated that, additionally, there are articles, devices, systems, and architectures of the present invention that consist essentially of, or consist of, the recited components, and that there are processes and methods according to the present invention that consist essentially of, or consist of, the recited processing steps.

[0155] It should be understood that the order of steps or order for performing certain action is immaterial so long as the invention remains operable. Moreover, two or more steps or actions may be conducted simultaneously.

[0156] The mention herein of any publication, for example, in the Background section, is not an admission that the publication serves as prior art with respect to any of the claims presented herein. The Background section is presented for purposes of clarity and is not meant as a description of prior art with respect to any claim. Documents are incorporated herein by reference as noted. Where there is any discrepancy in the meaning of a particular term, the meaning provided in the Definition section above is controlling. [0157] Headers are provided for the convenience of the reader and are not intended to be limiting with respect to the claimed subject matter.

[0158] 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.

[0159] In certain embodiments, the systems and methods described herein allow a user to converse naturally with a chatbot by providing an input via a familiar messaging interface, for example, as shown in screenshots 100, 105, 110, 115, 120, 125, 130, 135, and 140 of FIGs. 1A, IB, 1C, ID, IE, IF, 1G, 1H, and II, respectively. As shown in FIGs. 1A - II, a user can enter an alphanumeric input into a chat window as though she/he is conversing with a human, and the user subsequently receives a relevant response from the artificial intelligence chatbot. In certain embodiments, the user input may be provided as speech rather than as text typed into a GUI. The speech is detected by a voice assistant, which processes the detected speech and provides a textual query to the chatbot.

[0160] In certain embodiments, the artificial intelligence-enhanced chatbot (i) deciphers what information the user is requesting (e.g., from a written or spoken query), (ii) formulates product, service, and/or program recommendations based on the user query, and (iii) provides textual and/or audio feedback to communicate such recommendations. In certain embodiments, information regarding the user's stored genetic profile is also used by the chatbot to formulate these recommendations, before providing a textual and/or audible feedback to communicates the recommendations.

[0161] In certain embodiments, the chatbot allows an individual to easily purchase one or more of the recommended product(s), service(s), and/or program(s). For example, various genetic profile tests can be purchased, as described herein, to provide information about an individual's genotype. A specific genetic profile test may measure a specific set of (e.g., one or more) single nucleotide polymorphisms (S Ps) to determine which particular variant of the S P an individual has. In certain embodiments, the artificial intelligence chatbot may provide one or more recommendations, based on the set of SNPs and/or other information in the genetic profile, that are related to other products which may be purchased (e.g., nutritional supplements, meal programs, behavioral program(s), app(s), diagnostic tests for specific diseases and/or conditions, and additional genetic profile tests).

A. Chatbot for Identifying Genetic Profile Tests

[0162] In certain embodiments, the artificial intelligence chatbot described herein provides recommendations and/or other information related to personal genetic profile tests in response to a textual query. For example, as shown in FIGs. 1 A - II, a user enters an alphanumeric input that corresponds to various phrases that would be used in normal conversation (e.g., with a human). The chatbot provides relevant responses, mimicking a conversational interaction. In the illustrative example of FIGs. 1 A - II, the chatbot identifies a genetic profile test that the user is interested in by analyzing a textual query associated with the alphanumeric input of the user. As shown in FIG. ID, the chatbot causes a graphical representation of the genetic profile test (product) to be rendered in the messaging interface display. In this example, the genetic profile test is named "AURA", and the graphical representation rendered includes a picture associated with the test. The chatbot also provides a selectable link on the graphical user interface - "BUY NOW." The user may select the link (e.g., by clicking on it with a mouse, or, in the case of a touch sensitive interface, tapping on the link) in order to be transferred to a webpage where the user can purchase the recommended test.

A.i. Receiving User Input

[0163] Referring now to FIG. 2, method 200 is an exemplary method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with an artificial intelligence chatbot, according to an illustrative embodiment. In step 202, a processor of a computing device receives user input of a textual query. The chatbot systems and methods described herein may use a variety of inputs from the user

(represented by the textual query) in order to determine an appropriate genetic profile test to recommend to the user. For example, users may input textual queries using a graphical user interface (GUI) to ask questions at varying levels of specificity with respect to the hierarchical organization of product, category, gene, and SNP used to represent genetic profile tests and their results, as described herein (e.g., including in Section C below). In certain embodiments, the user input may be audible speech from the user, and a voice assistant may process the speech input to generate a textual query, as described more below. A.ii. Identifying Genetic Profile Tests

[0164] Still referring to FIG. 2, based on the received textual query, one or more genetic profile tests are identified (e.g., using a machine learning module) in step 204. The identified genetic profile test(s) are related to the textual query. Each genetic profile test is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more S Ps. For example, each corresponding S P may influence a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with.

[0165] In certain embodiments, the one or more genetic profile tests are identified in accordance with step 204 shown in FIG. 2 by accessing a database (e.g., a set of text files such as AIML files). The database comprises (for each of a predefined set of genetic profile tests) 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 textual label (e.g., representing a name of the genetic profile test). For each identified genetic profile test, one or more terms in the textual query is then matched to (i) the identifier of the genetic profile test and/or (ii) at least one of the one or more keywords. In certain embodiments, the one or more keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0166] For example, the chatbot may analyze a user's input to match words that are input by the user to names of one or more genetic profile tests, which can then be presented for purchase by the user. The names of the genetic profile tests may be stored in a database of text files, such as artificial intelligence markup language (AIML) files. Text files in the database may be formatted to allow specific words and patterns to be identified in a user's input and matched to responses provided by the chatbot in the messaging interface.

Examples of portions 300 and 350 of AIML files are shown in FIG. 3 A and FIG. 3B, respectively.

[0167] As shown in FIG. 3 A, the code <pattern>AURA</pattern> provides the name of the genetic profile test to be 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 interest in the product. The portion of code shown in FIG. 3B confirms a user interest in the "AURA" genetic profile test, and provides a response that describes the test, and offers it for purchase.

[0168] In certain embodiments, user input may also be matched with keywords that are associated with particular genetic profile tests, in order to identify one or more genetic profile tests in which the user is interested, and to offer them for purchase. A similar approach to the approach described with respect to FIG. 3 A may be used to match various keywords to specific genetic profile tests.

[0169] In certain embodiments, each genetic profile test is associated with a general class of health-related phenotypes and corresponds to a measurement of a specific set of one or more SNPs. For example, each SNP may influence a specific health related trait associated with the general class of health-related phenotypes that the genetic profile test is associated with. Accordingly, keywords associated with a given genetic profile test may include names of genes associated with SNPs that the genetic profile test measures, as well as keywords associated with the particular health related phenotypes that the genetic profile test is associated with.

[0170] In certain embodiments, the data framework described herein, for example in Section C below, can be used to associate genetic profile tests with particular keywords. In particular, as described herein, product data structures may be used to represent general classes of health related phenotypes, and to store associations with the various S Ps and genes that influence particular traits within a given class of health related phenotypes represented by a given product. Product data structures may also include additional information, such as descriptions of various SNPs, and the phenotypes that they influence. A given genetic profile test may be associated with a particular product data structure (e.g., effectively, the product data structure represents the genetic profile test). Names of genes, as well as other keywords may, accordingly, be extracted from the product data structure associated with a given genetic profile test, and used to populate (e.g., automatically) a database of text files. In certain embodiments, the chatbot accesses a database of product data structures directly as well, or alternatively.

[0171] Furthermore, terms in a user question (e.g., as included in a textual query) may be matched to various data structure elements of the hierarchical framework

(described in Section C herein). Lists of particular terms and associated elements of the hierarchal framework may be stored and used for matching directly. Such lists may also be used along with (e.g., for training) a machine-learning module that evaluates terms in a textual query to identify recommended genetic profile tests.

[0172] In certain embodiments, a database of reference documents is used to identify particular genetic profile tests (products) that a user is interested in purchasing, based on the user input. For example, in certain embodiments, reference documents associated with particular SNPs and/or genes are mined and compared with the user input in order to determine one or more relevant SNPs and/or genes that they are interested in. This approach may be carried out by a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents.

[0173] For example, the one or more genetic profile tests may be identified based in part on information within one or more reference documents stored in a database of reference documents. For example, the one or more genetic profile tests can be identified in accordance with step 204 of FIG. 2 by accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database). Each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests. For example, each reference document comprises information regarding one or more SNPs and the specific health related phenotypes that they influence.

[0174] Using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query may then be identified [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document]. For example, keywords can be extracted from the textual query and matches can be searched for in the reference documents. For example, a machine learning module may be used that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents. A degree of matching can then be determined, for each of one or more prospective genetic profile tests, between the one or more SNPs relevant to the user textual query and the set of one or more SNPs that the prospective genetic profile test measures. The one or more genetic profile tests can subsequently be determined based on the degree of matching.

A.iii. Providing Graphical Representation for Identified Genetic Profile Test(s)

[0175] Returning again to FIG. 2, in step 206, the processor provides (for each of the one or more identified genetic profile tests) a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising an identification of the genetic profile test. For example, identification of the genetic profile test can include a name of the test (e.g., rendered as text) and/or an image associated with the genetic profile test. In certain embodiments, the graphical representation comprising the identification of the genetic profile test includes a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

[0176] In certain embodiments, the processor causes display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window. In certain embodiments, rather than using a GUI, the textual query is provided by a voice assistant in response to a user's audible speech. In both embodiments, the textual query can be received (e.g., via the chat window GUI or the voice assistant), and a graphical representation, which includes an identification of the genetic profile test, is rendered within the chat window GUI as a response to the textual query. For example, the processor may cause display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results). This can allow the user to identify and purchase additional genetic profile tests they may be interested in. The processor may cause display of the chat window GUI, for example, within an interactive app. The interactive app may be an app (e.g., executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results. This can allow the user to identify and purchase additional genetic profile tests which may be of interest to the user (e.g., via in-app purchasing).

[0177] For example, a user may ask questions inquiring about which test will inform them about a general class of health related traits and characteristics, such as skin, fitness, nutrition, and behavior. Accordingly, various terms in a user question (and included in a textual query of a structured request received by the processor via a GUI) associated with different general classes of health related traits and characteristics that various different products represent may be used to identify associated genetic profile tests that are represented by particular products.

[0178] A user may ask more specific questions, for example, inquiring about categories of related genes, as represented by categories in the hierarchical organization described herein. Accordingly, user terms associated with categories, such as skin hydration, longevity, power performance, and vitamins, may be used to identify genetic profile tests that measure SNPs included categories associated with the terms in the user question.

[0179] Similarly, a user may ask about specific genes, SNPs, and the particular traits that they influence. For example, a user may be interested in learning if they have a genetic predisposition to a large appetite, e.g., to help them manage their weight. Such a user may inquire as to which test will tell them about appetite. As described herein, appetite is influenced by the rsl7782313 S P, occurring in the FTO gene. This S P is measured, for example, in the FUEL™ (or Nutrition) test, and accordingly associated with it via the hierarchical organization shown in FIG. 6 (described in Section C below).

Accordingly, the FUEL™ (or Nutrition) test may be recommended to the user.

[0180] Accordingly, by receiving a structured request via a GUI, the systems and methods described herein determine one or more recommended genetic profile tests. Once the one or more recommended genetic profile tests are determined, they can be included in a structured response that is displayed in the GUI. The structured response may include an identification of the one or more recommended genetic profile tests, along with any additional information that system will use to generate a response to the user.

A.iv. Audio Interaction with a Voice Assistant

[0181] In certain embodiments, the systems and methods described herein provide for audio-based user interaction, for example by utilizing a voice assistant. Instead of providing alphanumeric input by typing in a GUI chat window, a user may speak within the detection range of a voice assistant and be provided with computer-generated speech as feedback. In this manner, by interacting with a user through a voice assistant, the chatbot technology described herein allows a user to carry out a spoken, simulated conversation with the chatbot (e.g., via a voice assistant). Based on a user's speech input, the user is provided with one or more recommended genetic profile tests. The illustrative genetic profile tests described with reference to FIG. 4 in Section C below may be referred to by alternate names. For example, the AURA™ test is at times referred to herein as the Beauty test; the FITCODE test is at times referred to as the Fitness test; and the FUEL test is at times referred to as the Nutrition test.

[0182] Referring now to FIG. 4A, method 400 is an exemplary method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with a voice assistant. In step 402, a processor of a computing device receives (e.g., via a network) a structured request comprising a textual 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 user speech. For example, the structured request may be generated by detecting the user speech and generating speech data corresponding to at least a portion of the user speech and processing the user speech data to generate the textual query. For example, the user speech data may be processed by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion of the matching recognizable textual phrases as terms in the textual query.

[0183] In step 404, the processor identifies one or more genetic profile tests related to the user input using the textual query of the structured request. For example, the processor may match the textual query (e.g., one or more terms of the textual query) to one or more identifiers of genetic profile tests (e.g., stored in a database). Matching may be performed via a machine learning module. In certain embodiments, one or more subroutines are identified based on a first portion of the textual query, and a second portion of the textual query is then passed to the identified sub-routines as variables evaluated by the sub-routines to identify the one or more genetic profile tests. [0184] In certain embodiments, the one or more genetic profile tests are identified in accordance with step 404 of FIG. 4A by matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, where each keyword is associated with one or more genetic profile tests. In certain embodiments, the one or more keywords comprise, for each S P of a set of S Ps that the genetic profile test(s) measure, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences). In certain embodiments, the genetic profile test(s) are identified (e.g., in step 404) based in part on information within one or more reference documents stored in a database of reference documents (e.g., as described in Section A.ii above with respect to step 204 of method 200 shown in FIG. 2).

[0185] Still referring to FIG. 4A, in step 406, the processor provides (e.g., via a network) one or more structured responses to the voice assistant. The one or more structured responses comprise identifications of each of the one or more genetic profile tests identified in step 404. When each structured response is executed by the voice assistant (e.g., by a processor of the voice assistant), the voice assistant generates an audio output corresponding to simulated speech based on the structured response. Audio feedback is thereby provided to the user that corresponds to recommendations associated with the one or more identified genetic profile tests.

[0186] In certain 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. In particular, in order to evaluate and provide a response based on user speech, a structured request is received from the voice assistant. The structured request comprises a textual query that can be analyzed by a processor to generate an appropriate response. The voice assistant may generate the structured request by detecting user speech and generating data, such as text, corresponding to the detected user speech. The generated data can then be analyzed to generate the textual query and/or various terms that it comprises. These concepts are illustrated in more detail below. In certain embodiments, these concepts may be applied where the textural query is derived from alphanumeric input received from the user (e.g., via a graphical user interface, GUI).

[0187] For example, the voice assistant may be provided with sets of structured question phrases. Each set of structured question phrases corresponds to a particular question that the user intends to ask - i.e., a user intent. Each structured question phrase of the set of structured questions may, for example, represents a specific variation in what a user is likely to say when asking the particular question. In other words, a set of structured question phrases accounts for the fact that myriad different specific questions may be asked for a single specific purpose or user intent.

[0188] For example, a user interested in which genetic profile test they should purchase to learn about their skin health may ask any of "What test will tell me about my skin?", "What genetic profile test will tell me about my skin health?", "Which genetic profile test should I purchase to learn about skin health?", and so on. The voice assistant may use a list of variations in form of a particular question in order to account for variability in the exact words spoken by a user. The voice assistant may be provided with a list of phrases such as these, grouped together as associated with particular user intent. The voice assistant may include processing instructions and modules (e.g., artificial

intelligence), such as a machine-learning module, to allow user questions to be robustly evaluated. In particular, in certain embodiments, the voice assistant need not rely on a one- to-one match between spoken words by a user and a stored structured question phrase to accurately determine user intent.

[0189] The voice assistant may include multiple sets of structured question phrases.

These sets of structured question phrases may be stored by the voice assistant (e.g., in memory of the voice assistant), or made accessible to the voice assistant, e.g., via a network.

[0190] Accordingly, the voice assistant may detect and analyze user speech, using various sets of structured question phrases, to determine a particular intent of the user. An identification of the user intent determined based on the user speech may then be included in the textual query of the structured request provided by the voice assistant. For example, if a user asks a question that, based on the sets of structured question phrases, is determined to correspond to an intent to learn which genetic profile test will inform the user about the portion of their genotype that influences skin health, the voice assistant may include a predefined keyword such as WhatTestSkinHealth in the textual query of the structured request.

[0191] In certain embodiments, the structured question phrases may include a constant, base portion as well as a variable portion. For example, a user may be interested in various different genetic profile tests that inform them about different portions of their genotype and the health related traits and characteristic that are influenced by those portions. Accordingly, a structured question phrase such as

What test will tell me about {test type}?

with a constant base portion,

What test will tell me about and a variable portion,

{test type}

may be used. User speech may be evaluated by the voice assistant to determine user intent along with one or more variable key terms. Accordingly, the voice assistant may provide a structured request with a textual query that comprises one or more predefined keywords that indicate the determined user intent, along with one or more variable key terms.

[0192] For example, if a user asks, "Which test will tell me about skin?", a predefined intent keyword, such as WhatTest, along with a variable, skin, may be included in the textual 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 textual query, along with a different variable, fitness. As described herein, variations in the structured question phrase may be used to allow the voice assistant to determine user intent even as the user varies the manner in which they ask 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 manner, if the user asks a similar question, such as "Which test will help me with skin?", the appropriate intent keyword (e.g., WhatTest) and variable (e.g., skin) is still determined. In this manner, including multiple variations in the form of the same question as structured question phrases in a list accessible to the voice assistant allows the voice assistant to accurately determine user intent and provide a structured response that includes a textual query that captures user intent and any variable portions of their questions. The structured response and textual query can be processed by the systems and methods described herein in order to determine one or more genetic profile tests to recommend to the user and include in a structured response provided to the voice assistant. [0193] In particular, in certain 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 recommend to the user. The systems and methods described herein may use the user intent keywords along with any variable key terms included in the textual query of the structured request received from the voice assistant to determine the one or more recommended genetic profile tests.

[0194] For example, based on a structured request comprising a textual query with an intent keyword WhatTest and a variable skin, the chatbot technology described herein may determine the Beauty test as the recommended genetic profile test. A structured request comprising a textual query with an intent keyword WhatTest and a variable fitness may be evaluated to determine the Fitness test as the recommended test.

[0195] The chatbot systems and methods described herein may use a variety of inputs from the user, as represented in the textual query, in order to determine an appropriate genetic profile test to recommend to the user. For example, users may ask questions at varying levels of specificity with respect to the hierarchical organization of product, category, gene, and S P used to represent genetic profile tests and their results as described herein.

[0196] For example, a user may ask questions inquiring about which test will inform them about a general class of health related traits and characteristics, such as skin, fitness, nutrition, and behavior. Accordingly, various terms in a user question (and included in a textual query of a structured request received from a voice assistant) associated with different general classes of health related traits and characteristics that various different products represent may be used to identify associated genetic profile tests that are represented by particular products.

[0197] Terms in a user question (e.g., in a textual query) may be matched to the structured response provided by the voice assistant. For example, various data structure elements of the hierarchical framework described herein (in Section C) may be associated with one or more structured responses. Lists of particular terms and associated elements of the hierarchal framework may be stored and used for matching directly to the textual query and/or structured response directly. Such lists may also or alternatively be used along with (e.g., for training) a machine-learning module that evaluates terms in a textual query to identify recommended genetic profile tests.

[0198] Accordingly, by receiving a structured request from a voice assistant, the systems and methods described herein determine one or more recommended genetic profile tests. Once the one or more recommended genetic profile are determined, they can be included in a structured response that is provided to the voice assistant in order to allow it to respond to the user. The structured response may include an identification of the one or more recommended genetic profile tests, along with any additional information or speech that the voice assistant will use to generate speech in response to the user.

[0199] For example, a user may verbally ask a question such as, "What test will tell me about skin?". The voice assistant may detect and process the user speech to determine intent keyword WhatTest and variable skin. The intent keyword WhatTest and variable skin are included in a textual query of a structured request that is provided to and received by the chatbot systems and methods described herein. The intent keyword WhatTest and variable skin are evaluated to identify the Beauty (i.e., AURA™) test as a recommended genetic profile test. A structured response including text such as the text block shown below

The Beauty DNA Test decodes information in your unique DNA, giving you unprecedented insights about your skin. Discover how your genes influence your skin's hydration, aging, elasticity, and UV sensitivity with this 18-gene test so you can take better care of your skin. may then be provided to the voice assistant. The structured response may also include other elements, such as code to define speech properties such, as intonation, which the voice assistant uses to respond to the user. The voice assistant evaluates the structured response and responds to the user via simulated speech.

[0200] In this manner, the systems and methods described herein may interact with a user to guide them to provide them with recommendations of genetic profile tests to purchase. Examples of various structured question phrases, the genetic profile test recommended, and text of the structured response provided are shown in Table 1 below. Other structured question phrases and structured responses, which do not necessarily directly relate to recommendations of genetic profile tests may also be used. Such structured question phrases and structured responses may be used to inform the user about their tests, the testing process, and facilitate their conversation with the chatbot as mediated by the voice assistant. Such conversation may assist in encouraging a user to purchase one or more particular genetic profile tests. Examples of structured question phrases and text of the structured responses that they elicit are shown in Table 2 below. Table 1. Structured question phrases, genetic profile tests identified, and structured responses provided. Variable portions of structured question phrases are indicated in brackets, with the particular value of the variable portion (e.g., for which the particular genetic profile test and structured response shown in the associated entry are determined) shown. Question phrases are shown in bold text, with the identified genetic profile test and corresponding response text shown below.

Which test will tell me about {skin}?

Recommended test: Beauty

The Beauty DNA Test decodes information in your unique DNA, giving you

unprecedented insights about your skin. Discover how your genes influence your skin's hydration, aging, elasticity, and UV sensitivity with this 18-gene test so you can take better care of your skin.

Which test will tell me about {fitness}?

Recommended test: Fitness

The Fitness DNA Test is a 24-gene profile that helps you understand how your DNA affects your fitness potential, so you can get the information you need to fine-tune your routine and reach your goals faster. Discover how your genes influence things like exercise recovery, metabolism, muscle strength, joint health, movement, and power performance.

Which test will tell me about {nutrition}?

Recommended test: Nutrition

The Nutrition DNA Test is a 24-gene profile that reveals how your body responds to food and nutrients. Discover which foods work best for you, what to avoid, and what might make you feel better. Learn how your genes influence things like hunger and weight, food breakdown, vitamins, and food sensitivities.

Which test will tell me about {strength}?

Recommended test: Fitness

The Fitness DNA Test is a 24-gene profile that helps you understand how your DNA affects your fitness potential, so you can get the information you need to fine-tune your routine and reach your goals faster. Discover how your genes influence things like exercise recovery, metabolism, muscle strength, joint health, movement, and power performance. If you are interested in speed, you can also try out our Superhero DNA Test, which looks at speed, strength, and intelligence.

Which test will tell me about {vitamins}?

Recommended test: Nutrition

Which test will tell me about {caffeine}?

Recommended test: Nutrition

The Nutrition DNA Test is a 24-gene profile that reveals how your body responds to food and nutrients. Discover which foods work best for you, what to avoid, and what might make you feel better. Learn how your genes influence things like hunger and weight, food breakdown, vitamins, and food sensitivities. Which test will help me with {skin}?

Recommended test: Beauty

The Beauty DNA Test decodes information in your unique DNA, giving you

unprecedented insights about your skin. Discover how your genes influence your skin's hydration, aging, elasticity, and UV sensitivity with this 12-gene test so you can take better care of your skin.

Which test will help me with {healthy eating}?

Recommended test: Nutrition

Table 2. Structured question phrases and structured response text examples used for carrying out additional conversation with the user. Question phrases are shown in bold text, with corresponding response text shown below.

What will the {Beauty DNA Test} tell me?

With the Beauty DNA Test, you will learn how your genes impact your skin's hydration, aging, elasticity, and UV sensitivity. Discover if you're more sensitive to the sun, if your genes will impact how you age, and if your skin is predisposed to being firm or not. Get the details you need to perfect your skincare routine and select products that meet your needs.

How does a DNA Test work?

With an Orig3n DNA Test, you will get a peek inside yourself with a snapshot of your genes. A DNA test compares a sample of your genetic material against known genetic sequences, based on established scientific and medical research. When you buy an Orig3n DNA Test online, you will receive our DNA Test kit in the mail. Inside, you'll find instructions, a cheek swab, and a prepaid envelope to send back to our certified on-site lab. There, our scientists analyze these comparisons to generate your personalized profile, which you will receive on our mobile and web app. How do I take a DNA Test?

It's easy! First, choose an Orig3n DNA test. We have many tests, including ones related to fitness, nutrition, skincare, behavior, and child development. Order from our online shop to have a collection kit sent directly to you, or buy a kit at any of our events. Our kit makes the DNA collection process simple. Just swab your cheek for 30 seconds, then send it to our lab in the prepaid envelope. Then, all you need to do is register either by downloading our LifeProfile app or viewing it online, and we will send you your results!

B. Chatbot for Identifying Recommendations of product(s), servicers), and/or program(s)

[0201] In certain embodiments, the systems and method described herein allow recommendations (e.g., of product(s), service(s), and/or program(s) a user may wish to purchase or use) to be identified based on the user's genetic profile. Recommendations may include, for example, a set of nutritional supplements to purchase, a meal program, a behavioral program, diagnostic tests for specific diseases and/or conditions, apps, and additional genetic profile tests. For example, recommendations may include any of the purchase recommendations described herein and in detail in PCT Application No. PCT/US2017/067277, filed December 19, 2017, the content of which is hereby incorporated by reference in its entirety.

B.i. Identifying and Providing Recommendations Based on User Genetic Profile Data

[0202] Recommendations can be identified and provided in a manner similar to the approach for identifying recommended genetic profile tests described above in Section A, and may also include using information in the user's genetic profile (e.g., by accessing the user's genetic profile assessment, described in Section C). For example, recommendations may be identified automatically based on a variant of a SNP in a genome of the user (e.g., as identified via the user's genetic profile), or other genotyping data stored in the user's genetic profile. Various approaches for leveraging user genetic profile information to determine purchase recommendations, such as those described in PCT Application No. PCT/US2017/067277 and herein, may be used.

[0203] Once a user has had a genetic profile test performed, genotyping data from the genetic profile test is stored in a secure (e.g., password-protected) user genetic profile. The genotyping data identifies, for a set of specific SNPs associated with the genetic profile test, the specific variants that the user has. As described herein, the variants of various SNPs that a user has influence various health related traits and characteristics, such as joint health, metabolism, and ability to process certain foods.

[0204] Based on the various different SNP variants an individual has (and/or other genotyping data), certain supplements or combinations thereof may be useful for that individual. For example, if an individual has a particular variant of a SNP that causes him or her to be prone to weight gain (e.g., a particular variant of a SNP of the ADIPOQ gene) then it would be valuable for that individual to take supplements that help to manage or prevent weight gain and obesity. For example, if an individual has a particular variant of a SNP that causes him or her to have a reduced ability to convert beta carotene to retinol, that individual may benefit from taking a vitamin A supplement. Similarly, depending on whether an individual has particular SNP variants that influence longevity, joint health, muscle recovery, endurance and lean body mass, and skin health, different supplements may be identified that would benefit the individual. [0205] Based on genotyping measurement results as obtained via a genetic profile test and stored in an individual's genetic profile assessment, various relevant supplements that are of particular benefit to the individual can be identified. The identified supplements may be provided to the user via a user interaction with an artificial intelligence chatbot as described herein.

[0206] Referring now to FIG. 4B, method 410 is an exemplary method of providing consumer feedback (e.g., purchase recommendations) corresponding to one or more genetic profile tests via a user interaction with an artificial intelligence chatbot. In step 412, user input of a textual query is received by a processor of a computing device. The textual query may be provided as a text input typed in a GUI by the user or provided by a voice assistant in response to audible speech from the user. The user is associated with one or more genetic profiles (e.g., one or more genetic profiles representing results of genetic profile tests performed for the user). For example, the user can be a subscribed (e.g., logged in) user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor.

[0207] Responsive to the user input received in step 412, one or more

recommendations (e.g., purchase recommendations) are identified (e.g., automatically) in step 414 based on the textual query and based at least in part on the one or more genetic profiles of the user (e.g., using a machine learning module).

[0208] In step 416, the one or more recommendations are provided to the user via the artificial intelligence chatbot. Similar to the manner in which recommended genetic profile tests may be provided to a user via a GUI, such as within a chat window, or via an audio interaction via a voice assistant, purchase recommendations may also be provided to a user via a GUI and/or voice assistant. In certain embodiments, a graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test. In certain embodiments, a voice assistant may facilitate the purchase of the genetic profile test .

[0209] In certain embodiments, the one or more recommendations are identified

(e.g., in step 414 of method 410) by accessing, by the processor, a database (e.g., a set of text files such as ADVIL files such as the portions of example AIML files shown in FIG. 3 A and FIG. 3B). The database includes, for each of a predefined set of recommendations, an identifier [e.g., a textual label (e.g., representing a name of the recommendation)] of the recommendation. The database also includes, for each of a predefined set of

recommendations, one or more keywords associated with the identifier of the

recommendation. For each identified recommendation, one or more terms in the textual query are matched, by the processor, to (i) the identifier of the recommendation and/or (ii) at least one of the one or more keywords. In certain embodiments, each recommendation is associated with a set of one or more S Ps, and the one or more keywords comprise, for each S P of the set of SNPs associated with the recommendation, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

[0210] In certain embodiments, at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs. For example, each corresponding SNP may influence a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with. The set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

[0211] The one or more recommendations can be identified (e.g., in step 414 of method 410 shown in FIG. 4B) based in part on information within one or more reference documents stored in a database of reference documents. For example, one or more recommendations can be identified by accessing, by the processor, a database that includes a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database). Each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations. For example, each reference document includes information regarding one or more SNPs and/or genes and the specific health-related phenotypes that they influence.

[0212] Using the textual query and information within the plurality of reference documents, one or more SNPs are then determined that are relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document]. For example, keywords may be extracted from the textual query, and the processor may search for matches of these keywords in the reference documents (e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents). For each of one or more prospective recommendations, a degree of matching is determined between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation. The one or more recommendations are identified based on the determined degree of matching.

[0213] In certain embodiments, each recommendation is associated with a set of one or more genes, and the one or more keywords correspond to names of the genes associated with the recommendation. In certain embodiments, the one or more

recommendations comprise a recommended genetic profile test. In certain embodiments, the one or more recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy).

[0214] In certain embodiments, the processor causes display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window. The textual query is received via the chat window GUI and the graphical representation that includes an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query. In certain embodiments, the processor causes display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results). This thereby allows the user to identify and purchase additional genetic profile tests which may be of interest to the user. In certain embodiments, the processor causes display of the chat window GUI within an interactive app. The interactive app may be an app (e.g., executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results. This can allow the user to identify and purchase additional genetic profile tests which may be of interest to the user (e.g., via in-app purchasing). B.i.a. Using a Voice Assistant to Provide a Textual Query

[0215] Turning to FIG. 4C, method 420 is an exemplary method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with a voice assistant, according to an illustrative embodiment. In step 422, a structured request is received (e.g., via a network), by a processor of a computing device, from the voice assistant (e.g., a local or remote processor of the voice assistant). Illustrative examples of structured requests are described in Section A above. In general, a structured request comprises a textual query and is generated by the voice assistant in response to user speech. For example, the structured request may be generated by detecting the user speech and generating speech data corresponding to at least a portion (up to all) of the user speech and processing the user speech data to generate the textual query. For example, user speech data may be processed by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion (up to all) of the matching recognizable textual phrases as terms in the textual query. The textual query of the structured request comprises an identification of the user associated with one or more genetic profiles. For example, the user associated with the genetic profiles may be a subscribed user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor.

[0216] In step 424, the processor identifies one or more recommendations (e.g., purchase recommendations) using the textual query of the structured request. For example, the one or more recommendations may comprise a recommended genetic profile test. The one or more recommendations are identified based at least in part on the one or more genetic profiles associated with the user. In certain embodiments, step 424 comprises matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with at least one of the one or more recommendations (e.g., purchase recommendations).

[0217] In certain embodiments, each recommendation is associated with a set of one or more SNPs, and the one or more stored keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences). The one or more recommendations are automatically identified based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles). In certain embodiments, the one or more recommendations are identified based in part on information within one or more reference documents stored in a database of reference documents (e.g., as described above with reference to step 414 of method 410 shown in FIG. 4B).

[0218] In certain embodiments, at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with). The set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures. For example, each recommendation may be associated with a set of one or more genes, and the one or more keywords may correspond to names of the genes associated with the recommendation.

[0219] In step 426, the processor provides (e.g., via a network) one or more structured responses to the voice assistant. The one or more structured responses comprise identifications of each of the one or more recommendations, and each structured response, when executed by the voice assistant (e.g., a local or remote processor of the voice assistant) causes the voice assistant to generate an audio output corresponding to simulated speech based on the structured response. Audio feedback corresponding to the one or more recommendations is thus provided to the user. In certain embodiments, the structured response comprises data corresponding to an identification of a location and name of a vendor from which the user can purchase the recommendation and, the structure response, when executed by the voice assistant, causes the voice assistant to generate audio output providing the location and name of the vendor.

[0220] In certain embodiments, at least one of the identified recommendations is associated with one or more S Ps and, for each of the one or more associated S PS, the recommendation is associated with a particular variant of the S P (e.g., identified via a qualifier). The recommendation is based at least in part on a correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation and particular variants of the one more SNPs that the user has (e.g., as identified in the user's one or more genetic profiles).

[0221] As described herein, the one or more recommendations may, for example, comprise a recommended diagnostic test (e.g., a test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy). The one or more recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase. In certain embodiments, the one or more recommendations may include a meal program, a fitness program, a brain wave feedback program, a behavioral program (e.g., a focus program, an ADHD assistance program), and/or an individualized therapy. These recommendations may be

individualized programs and/or therapies based on the one or more genetic profiles of the user. For example, the one or more recommendations comprise one or more recommended purchases of one or more mobile health devices (and/or one or more software apps operating on a mobile health device).

B.ii. Storing Recommendations

[0222] Recommendations (e.g., recommended purchases) may be stored and associated with various genetic profile objects, such as those described herein (e.g., in Section A) in order to utilize an individual's genetic profile assessment to identify relevant purchases. In certain embodiments, recommended purchase objects (e.g., data structures corresponding to recommended purchases) are created by a developer and associated with data structures (e.g., gene objects, S P objects, and/or variant objects) in a user's genetic profile assessment based on the relevance of the corresponding recommended purchases to the corresponding genomic constituents (e.g., genes, SNPs, and/or variants). Thus, automatic identification may comprise calling or identifying one or more of those stored associations. In some embodiments, recommended purchases are identified by searching a database of all possible recommended purchases using a query comprising data from a user's genetic profile assessment. [0223] For example, purchase recommendation objects that represent specific potential recommended purchases may be stored in a purchase recommendation database. Each purchase recommendation object stored in the purchase recommendation database is associated with one or more stored variant objects. The stored variant objects associated with a particular purchase recommendation object represent the particular variants of various SNPs for which the potential recommended purchase represented by the particular purchase recommendation object is recommended. For example, a purchase

recommendation object representing a Vitamin A supplement could be associated with a stored variant object that represents a particular variant of a SNP that causes an individual to have a reduced ability to convert beta carotene to retinol.

[0224] A user's genotyping data (e.g., as stored in their genetic profile assessment) can then be used to query the purchase recommendation database to identify particular recommended purchases that will be beneficial to them. In particular, the user's genotyping data represents results of genotyping measurements performed on a biological sample from the user in order to determine the specific variants of various SNPs that are present in their genome. These results can be represented in the genotyping data via a plurality of user-specific variant objects, each of which represents the specific variant of a specific SNP that the user has in their genome.

[0225] Accordingly, the user-specific variant objects can be matched to the stored variant objects. Variant objects may be matched based on measurement outcomes and/or qualifiers that they are associated with. The purchase recommendation objects that are associated with the stored variant objects that match the user specific variant objects of the genotyping data can thus be identified to determine a set of potential recommended purchases. One or more recommended purchases can then be selected from the determined set of potential recommended purchases. In certain embodiments, all the potential recommended purchases may be selected. In certain embodiments, additional criteria, such as a user rating, cost, availability to the user, whether a particular recommended purchase conflicts with others, may be used to select the one or more recommended purchases from the determined set of one or more potential recommended purchases.

[0226] In certain embodiments, recommendations are provided via a GUI, such as a chat window, by rendering, within the chat window, a graphical representation of the recommended purchase. The graphical representation may include one or more icons and/or text for display within an assessment GUI or it may include a link (e.g., a button, hyperlink, selectable icon) that a user selects to access a separate GUI for viewing the purchase recommendations. In certain embodiments, a user may purchase

recommendations directly using the chat window GUI.

[0227] For example, in certain embodiments, purchase of a particular

recommended purchase by the user is facilitated by rendering a selectable button corresponding to the particular recommended purchase and associating the selectable with a link (e.g., a weblink) to a predefined website of a specific merchant. In this manner, a user selection of the selectable button initiates their purchase of the particular

recommended purchase to which it corresponds. In certain embodiments, the user may store sets of their information that can be provided to the merchant site automatically. For example, they may store address and payment information (e.g., credit card information) in a secure database. Upon their selection of the selectable button for purchasing the particular recommended purchase, the systems and methods described herein access the user information and automatically provide it to the merchant site. In certain embodiments, all information necessary for the purchase is stored and automatically provided to the merchant site, such that the user purchase can be completed with a single click of the selectable button (e.g., no further user interaction is required).

[0228] In certain embodiments, recommendations provided to the user via a voice assistant may also include conversation that prompts a user to enter quantities of product to buy, payment information, and the like, such that the user may confirm purchase and purchase the recommendation directly via their interaction with the voice assistant. In certain embodiments, user payment is stored and a user may purchase a particular recommended purchase simply via an affirmative command to the voice assistant.

[0229] In certain embodiments, one or more of the identified recommendations are personalized based on the users genetic profile. In certain embodiments, at least some of the recommendations are recommended purchases offered to a user are programs and/or therapies that are personalizeable (e.g., personalized) to the user. Such recommended purchases may be personalized based on the textual query received by the user and/or the genetic profile of the user. For example, a fitness program recommended to a user based generally on genotype(s) of the user may further be personalized to the user based on one or more particular genotypes. More specifically, again for example, a fitness program may be recommended based on several traits of a user, but certain particular exercises in the fitness program may be substituted based on the particular phenotype of the user that make the user more susceptible to experiencing joint inflammation and/or pain. As an additional example, a meal program recommended to a user based on health-related phenotypes that suggest the user has sugar sensitivity may be modified to exclude dairy products from the program based on lactose intolerance of the user, as determined from the textual query and/or the user's genetic profile.

B.iii. Example Purchase Recommendations

[0230] Additional illustrative examples of recommendations which may be provided by the chatbot, according to the systems and methods described herein, are described below.

[0231] In certain embodiments, custom meal programs may be determined for a user using a dietary profile created based on their textual query and/or their genetic profile (e.g., their genotyping data.) The dietary profile for the user represents guidelines and/or taste preferences for the user and comprises a set of user specific dietary tags (e.g., alphanumeric strings) that identify common diets and/or allergens. For example, dietary tags such "vegetarian", "vegan", "pescatarian", "low-cholesterol", "dairy-free", "lactose- free", "gluten-free", "paleo", "low-sugar", and the like may be used to identify various diets that, based on the user genotyping data, are recommended. For example, dietary tags such as "dairy", "peanut", "nut", "gluten", and the like, may be used to identify allergens that the user's genotyping data results indicates that they are allergic to and/or that the textual query indicates they do not wish to consume. The dietary tags may be determined from the user genotyping data based on their association with particular variants of various different S Ps and/or qualifiers that classify them.

[0232] For example, SNPs associated with the FADSl, KCTDIO and PPARg influence cholesterol and fat storage levels. Accordingly, based on the presence of a variant and/or qualifier for any SNPs associated with these genes in a user's genotyping data, tags such as "low-cholesterol" may be added to a determined dietary profile for the user. Various dietary tags and associations between them and variant objects and/or qualifiers that identify and/or classify, respectively, specific possible variants of various S Ps may be stored, such that a dietary profile may be populated with dietary tags via automated matching between (i) user-specific variant objects and/or user-specific qualifiers from the genotyping data and (ii) stored variant objects and/or stored qualifiers.

[0233] Once determined, the user dietary profile can be used to identify meal programs and specific recipes that are recommended for the user. For example, in certain embodiments, a meal database comprising a plurality of predefined meal programs, each associated with one or more program-specific dietary tags. User-specific dietary tags of the user's dietary profile can be matched to the program-specific dietary tags to identify meal programs stored in the meal database that are recommended for the user. The identified meal programs may comprise multiple recipes that the user can select from to follow a diet that will benefit their health.

[0234] In certain embodiments, the meal database comprises a plurality of recipes, each of with is associated with one or more recipe-specific dietary tags. User-specific dietary tags of the user's dietary profile can be matched to the recipe-specific dietary tags to identify recipes stored in the meal database that are recommended for the user. One or more of the recommended recipes can be selected and combined, automatically, to create a custom meal plan for the user.

[0235] In certain embodiments, the meal database comprises ingredient lists for various recipes that can be queried. Based on the ingredient list of a particular recipe, the systems and methods described herein may determine whether or not the particular recipe conforms to one or more of the diets identified by the user-specific dietary tags and/or does not comprise any allergens identified by the one or more user-specific dietary tags. This approach of querying ingredient lists of recipes may be used in place of, or in combination with querying recipe-specific dietary tags.

[0236] In certain embodiments, the custom meal plan includes information about the various recipes it comprises, such as titles of the recipes, and pictures of them. In certain embodiments, titles of the recipes and/or their pictures are graphically rendered. In certain embodiments, the custom meal plan comprises an identification of a website to which a user can subscribe to obtain ingredient lists and/or cooking procedures for one or more of the recipes it comprises. In certain embodiments, graphics and/or text

corresponding to ingredient lists and/or cooking procedures for one or more recipes are graphically rendered for presentation to the user.

[0237] In certain embodiments, the custom meal plan comprises an identification of one or more specific restaurants and/or food delivery services through which the user can obtain at least one recipe of the recommended recipes (e.g., participating restaurants and/or participating food delivery services that provide recipe information for storage in the meal database).

[0238] In certain embodiments, a custom fitness program is identified and recommended to the user. In certain embodiments, the custom fitness program comprises one or more recommended workout classes (e.g., offered in the user's area; e.g., offered by participating merchants (e.g., gyms)) that are identified as recommended for the user based on their genotyping data and/or their textual query. Identifications of workout classes may be stored in a workout class database. Each workout class may be associated with one or more variant objects and/or qualifiers that represent and/or classify, respectively, specific variants of specific S Ps. User-specific variant objects and/or qualifiers in their genotyping data and/or in their textual query can be matched to the stored variant objects and/or qualifiers to identify relevant workout classes. For example, SNPs associated with the COL5al gene influence joint strength and flexibility. Certain variants of SNPS associated with the COL5al gene render an individual prone to reduced flexibility, hypertension, and risk of injury during specific types of exercise. Accordingly, certain workout classes that, for example, offer low impact stretching and flexibility exercises may be associated with variant objects and/or qualifiers that correspond to these variants, such that they can be recommended to users that will benefit from them. Similarly, a textual query can include an indication of a user's preference for a given exercise activity. For example, low impact workouts may be recommended to a user using a measure of the health status of the user (e.g., the presence of an injury) determined based on the textual query.

[0239] In certain embodiments, a physical fitness profile, similar to the above described dietary profile, may be determined for the user based on their genotyping data and/or their textual query. The physical fitness profile may comprise a set of user-specific fitness tags that identify specific workout classifications (e.g., that are recommended for the user, e.g., that the user should avoid) (e.g., alphanumeric strings such as "HUT", "aerobic"; "cardio"; "high intensity", "flexibility", and the like) having been determined, by the processor, as associated with (e.g., beneficial to) the user based on their genotyping data. The user-specific fitness tags can then be used to query a workout class database comprising a plurality of workout classes, each associated with one or more program- specific fitness tags. By matching the user-specific fitness tags to program-specific fitness tags, relevant workout classes can be identified via their associate to matched program- specific fitness tags.

[0240] Once identified, the one or more recommended workout classes may be provided for presentation to the user. In certain embodiments, graphics and/or text corresponding to a recommended workout class are graphically rendered for presentation to the user. In certain embodiments, graphics and/or text representing additional information associated with the recommended workout class [e.g., one or more times when the class is offered; e.g., one or more locations (e.g., of specific gyms) at which the class is offered, e.g., a cost of the class, e.g., a link to sign up for the class] are graphically rendered for presentation to the user.

[0241] In certain embodiments, locations of gyms near the user that offer a recommended workout class are identified, for example based on location data (e.g., GPS coordinates) of the user, provided e.g., by their mobile computing device (e.g., a cell phone, e.g., a smartwatch). In certain embodiments, the location data for the user is used in combination with the identified locations of gyms offering the recommended workout class to provide a map that shows the location of the nearby gym, directions to the nearby gym, and the like, to the user. For example, lists of nearby gyms, maps, directions, and the like can be displayed on the user's mobile computing device.

[0242] Where the genetic profile is based on S P variants associated with identified traits, one or a combination of products may be automatically recommended according to one or more identified traits (e.g., via reference to a look-up table or other mapping). The following are example genetic traits (e.g., informed by associated, identified SNP variants determined from a biological sample of a user) that can be part of a genetic profile.

B.iii.a. Weight Management

[0243] Genetic traits associated with weight management that can be identified based (e.g., at least in part) on SNP variants include, for example, weight regain, food reward, feeling full, appetite, obesity, hunger, sweet tooth, fatty acid sensitivity, age related metabolism, lipid metabolism, fat processing ability, feeling full, mono-unsaturated fat, and sugar sensitivity. In certain embodiments, based on a user's genetic profile results with respect to one or more of these traits, one or more of the following supplements are automatically identified which may be presented to the user as an optional recommendation (e.g., purchase recommendation, e.g., in-app purchase). For example, customized supplement packs may be recommended (e.g., garcinia cambogia, CLA, raspberry ketones, green tea extract, green coffee bean extract, carbohydrate and fat blockers, tonalin, hoodia, and/or meal replacements).

B.iii.b. Daily Support

[0244] Genetic traits associated with an individual's need for vitamins and/or the individual's ability to effectively utilize vitamins, which can be identified based (e.g., at least in part) on SNP variants, include, for example, those involving beta carotene (vitamin A), vitamin Bi₂, vitamin D, folate levels, vitamin B₆, vitamin E, and vitamin C. In certain embodiments, based on a user's genetic profile results with respect to one or more of these traits, the system automatically identifies one or more of the following supplements which may be presented to the user as an optional recommendation (e.g., purchase recommendation, e.g., in-app purchase). For example, customized supplement packs may be recommended (e.g., multivitamins, B complex, folate and Sam-E, vitamin A, vitamin C, vitamin D, and/or vitamin E).

B.iii.c. Longevity

[0245] Genetic traits associated with longevity may be identified, for example, based on SNP variants of an individual. In certain embodiments, based on a user's genetic profile results, the system automatically identifies one or more of the following

supplements which may be presented to the user as an optional recommendation (e.g., purchase recommendation, e.g., in-app purchase). For example, customized supplement packs may be recommended (e.g., oxaloacetate, curcumin, turmeric, rhodiola, carnitine, and/or N-acetylcysteine).

B.iii.d. Joint Health and Exercise Recovery

[0246] 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 force, muscle power, cardiorespiratory capacity, exercise recovery, strength building, and blood flow regulation. In certain embodiments, based on a user's genetic profile results with respect to one or more of these traits, the system automatically identifies one or more of the following supplements, which may be presented to the user as an optional recommendation (e.g., purchase recommendation, e.g., in-app purchase). For example, customized supplement packs may be recommended [e.g., joint health supplements (glucosamine chondroitin, fish oil, MSM, and/or collagen), and/or muscle recovery supplements (branch chain amino acids (BCAA), glutamine, and/or whey protein powder].

B.iii.d. Endurance and Lean Body Mass

[0247] Genetic traits associated with an individual's endurance and lean body mass include, for example, cardiac output, oxygen capacity, V0₂ max, muscle function, energy output, muscle efficiency, cardiorespiratory capacity, blood flow regulation, lean body mass, and muscle mass. In certain embodiments, based on a user's genetic profile results with respect to one or more of these traits, the system automatically identifies one or more of the following supplements, which may be presented to the user as an optional recommendation (e.g., purchase recommendation, e.g., in-app purchase). For example, customized supplement packs may be recommended [e.g., creatine, caffeine, beta-alanine, sodium phosphate, N0₂ (arginine), and/or pre-workout supplements].

B.iii.e. Skin Health

[0248] Genetic traits associated with an individual's skin health include, for example, sun sensitivity, skin protection, skin renewal, skin tone, skin protection, skin health, photo aging, and skin hydration. In certain embodiments, based on a user's genetic profile results with respect to one or more of these traits, the system automatically identifies one or more of the following supplements, which may be presented to the user as an optional recommendation (e.g., purchase recommendation, e.g., in-app purchase). For example, customized supplement packs may be recommended [e.g., biotin, vitamin E, fern extract (sun protection), primrose, black currant oil, collagen, and/or phytoceramides].

[0249] For any of the above examples, in certain embodiments, a particular formulation of a recommended supplement may also be automatically identified and presented to a user based on the user's genetic profile and/or the textual query. In certain embodiments, one or more recommended meal programs are automatically identified (e.g., via food delivery service) for rendering and presentation to a user based on the user's genetic profile results. In certain embodiments, one or more recommended fitness programs, brain wave feedback programs (e.g., for stress relief) and/or behavioral programs (e.g., focus programs, ADHD assistance, improved mental acuity programs, MCI prevention programs, and/or Alzheimer's prevention programs) are automatically identified for rendering and presentation to a user based on the user's genetic profile results.

B. iv. Backend for Automated Identification of Recommendations Related to

Personal Genetic Profiles

[0250] In certain embodiments, a personal genetic profile product (e.g., that presents results of a user genetic profile) identifies and presents recommendations for other products, services, and/or programs associated with the user's specific genetic profile. For example, in certain embodiments, a recommendation for nutritional supplements is determined and presented to a user from a set of stored recommendations input by a developer using a recommendation creation back end (e.g., a creation graphical user interface). A developer may manually or automatically upload a set of purchase recommendation objects (i.e., data structures that correspond to recommendations) in order for those purchase recommendations to be available to be made to a user. For example, a developer may upload a set of purchase recommendations for supplements for a range of variants of S Ps corresponding to weight management. When users view their genetic profile assessment that includes, which in this example is a weight management genetic profile product, they may then see the purchase recommendations from the set uploaded by the developer that correspond to the particular variants the user has.

[0251] In some embodiments, the set of recommendations is indexed and stored such that it may be queried based on a user's genetic profile assessment. In some embodiments, additional information associated with an object in a genetic profile product comprises a recommendation, for a plurality of objects (e.g., wherein the purchase recommendation object defines a selectable link). In certain embodiments,

recommendation objects are associated with generic data structure hierarchies such that when a user's genetic profile assessment is formed from the user's genotyping data and a generic data structure hierarchy, the relevant associated recommendations are

automatically (indirectly) associated with the user's genetic profile assessment.

[0252] In certain embodiments, a developer creates new recommendation objects and associates them with existing objects in a genetic profile product using a graphical user interface. Referring now to FIG. 5, method 500 is an exemplary method for creating recommendation objects associated with stored objects in a genetic profile product. In step 502, a developer is presented with a graphical user interface element for creating a recommendation object. The developer inputs data into the graphical user interface element to be included in the recommendation object. Part of the graphical user interface element allows a developer to select one or more stored genomic objects to be associated with the recommendation object being created. A stored genomic object is any data structure in a hierarchy of data structures that defines a genetic profile product (e.g., as described in Section C below). In step 504, a processor of a computing device receives a purchase recommendation object containing the data input by the developer as well as the selection of one or more stored genomic objects made by the developer. In step 506, the processor associates the one or more stored genomic objects with the purchase

recommendation object. In step 508, the processor stores the purchase recommendation object and the association for further updating or retrieval (e.g., in order to populate an assessment GUI with purchase recommendations).

[0253] A graphical user interface element provided to a developer for creating a purchase recommendation object comprises one or more graphical control elements used to input data related to the purchase recommendation corresponding to the purchase recommendation object. For example, graphical control elements may be provided for entering a name or title of the purchase recommendation, descriptive text and information, a hyperlink (if the purchase recommendation is provided to users on a separate web interface or GUI), and icons used in displaying the purchase recommendation to a user. In certain embodiments, a graphical user interface element provides one or more graphical control elements (e.g., drop down lists) for a developer to select a previously created purchase recommendation object and associate it with a stored genomic object (e.g., for updating purchase recommendations for certain genotypes or health-related phenotypes based on new research or guidelines). [0254] A purchase recommendation object may be associated with any stored object of a genetic profile product. In certain embodiments, purchase recommendation objects are most frequently associated with variant objects, because certain purchase recommendations are suitable only for users with a particular variant of a S P. For example, a user with a neutral variant for a SNP corresponding to joint pain would not experience elevated joint pain or an increased likelihood of joint pain. Hence, associating a purchase recommendation object for an anti-inflammation supplement with this joint pain SNP object would lead this particular user to receive an unnecessary purchase

recommendation for the anti-inflammation supplement. In contrast, a user with an "adapt" qualified variant (e.g., having a higher susceptibility to joint pain or elevated joint pain) would benefit from such a supplement recommendation. In certain embodiments, a purchase recommendation object is associated with a SNP object, gene object, category, or product if the supplement of the purchase recommendation is believed to be beneficial to all or most users regardless of the particular variant any of the users has.

[0255] In certain embodiments, purchase recommendation objects can comprise data input from a developer that causes a purchase recommendation normally shown to users with a variant of a SNP to be hidden from view of a user if the user has a particular variant of another SNP. A user may, absent all other genotyping data, receive a purchase recommendation based on a particular health-related trait they possess. However, due to a different health-related trait, the user may not receive that same purchase recommendation as the supplement being recommended would confer or increase the likelihood of conferring a negative effect based on the different health-related trait. For example, if a user's phenotype makes the user easily build muscle mass, but the user's phenotype also makes the user sensitive to sugar, based on data input by the developer, a recommendation for a muscle-mass-building supplement normally provided may not be shown to the user because the supplement is high in sugar and the user has a sugar sensitivity.

B. v. Interaction with Mobile Health Devices

[0256] In certain embodiments, the systems and methods described herein provide for interaction with one or more mobile health devices of the user. Mobile health devices can be used to record health data about a user. Data recorded via a mobile health device (e.g., mobile health data) includes a range of biological/physical measurements of the user, such as their weight, glucose levels, brain activity (e.g., as measured via an EEG), and the like, as well as data about activities the user performs, such as physical activity level and diet. Biological/physical measurements can be performed via devices such as a network connected 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 workouts, sleep, and meals via various different apps. A given mobile health devices may record one or more biological/physical measurements and/or activity measurements. Mobile health data may be recorded in an automated fashion, and/or in connection with a user interaction with the mobile health device.

[0257] Mobile health data about a user may be received and/or accessed by the systems and methods described herein and utilized in combination with the user's genotyping data (e.g., genetic profile assessment) to provide and/or update purchase recommendations to the user and/or to provide feedback to the user about their activities. [0258] For example, any of the approaches described above for identifying purchase recommendations to a user based on genotyping data may be augmented by incorporating mobile health data in addition to genotyping data in the identification process. For example, if genotyping data of a user indicates that they are prone to obesity, while their mobile health data (e.g., recorded via an activity monitor, or a smartphone) shows that they have a low physical activity level, a recommendation corresponding to a fitness program may be identified.

[0259] In certain embodiments, recommended purchases identified for a user include one or more mobile health devices that are related to another recommended purchase. For example, if for a given user, a recommended purchase corresponding to a fitness program is identified, one or more mobile health devices, such as activity trackers or specific smartphone apps that facilitate the ability of the user to adhere to the fitness program are also identified. Similarly, in certain embodiments a recommended purchase corresponding to a brain wave feedback program may be linked to one or more recommended purchases corresponding to wearable brain wave monitoring and/or meditation assistance devices. Recommended purchases may be products in the form of hardware, software, or combinations of hardware and software.

C. Storage and Presentation of Genetic Profile Assessments

[0260] The data framework utilized by the chatbot has a relational and hierarchical structure which provides benefits for the systems and methods described herein. For example, each of the various genetic profile tests is linked to particular categories and/or characteristics of an individual, and these categories and/or characteristic are in turn linked to specific measureable genotypes (e.g., particular SNPs and/or genes associated with the categories and/or characteristics). The structure of this data allows an individual's genetic profile assessment to be reliably determined and the results of the determination to be reliably stored (e.g., in a database) in an efficiently searchable fashion, allowing the chatbot described herein to identify relevant recommendations for the user.

C.i. Relationship Between Products, Categories, SNP Objects, and Gene Objects

[0261] In certain embodiments, in order to provide an individual not only with their genetic profile assessment, but also to convey information related to the particular traits and characteristics that are influenced by the specific SNP variants present in their genetic material in an organized and intuitive fashion, the systems and methods described herein include a data framework which comprises an intuitive hierarchical organization of data structures. The framework provides for storing relationships (e.g., associations) between particular SNPs, biological traits and characteristics, and general classes of such traits and characteristics, based on the specific traits that each particular SNP influences.

[0262] In certain embodiments, a first (e.g., top level) class of data structures, referred to herein as products, are used to represent different general classes of health- related traits and characteristics. In certain embodiments, a product data structure corresponds to a particular assessment ordered (e.g., purchased by the individual), in which unique versions of genes and/or SNPs that an individual has that influence the particular general class of health-related traits and characteristics that the corresponding product represents are identified (e.g., via genotyping measurements). [0263] Referring now to FIG. 6, in certain embodiments, each product has a name

[e.g., a product data structure comprises a name (e.g., text data representing the name)] that provides a convenient, and memorable way to refer to the product. For example, a particular product 612 (e.g. named "FUEL™" or "Nutrition" ) is used to represent a class of traits corresponding to the way in which an individual's body processes different foods and nutrients. Another product 614 (e.g. named "AURA™" or "Beauty") is used to represent a class of traits corresponding to skin health. Another product 616 (e.g., named "FITCODE™" or "Fitness") is used to represent a class of traits corresponding to physical fitness. Another product 618 (e.g., named "SUPERHERO™") is used to represent a class of traits corresponding to physical and intellectual performance. In certain embodiments, a name of a product is the same as the name under which a particular assessment is offered for sale. For example, assessments FUEL™, FITCODE™, AURA™, and

SUPERHERO™ are offered for sale by Org3n, Inc. of Boston, MA.

[0264] In certain embodiments, each product is in turn associated with one or more of a second class of data structures, referred to as categories. In certain embodiments, each category corresponds to a particular health related trait or characteristic (e.g., food sensitivity, food breakdown, hunger and weight, vitamins, skin ultraviolet (uv) sensitivity, endurance, metabolism, joint health, muscle strength, intelligence). In certain

embodiments, the categories with which a particular product is associated each corresponds to different health-related traits or characteristics that are related to the general class of health-related traits or characteristics to which the particular product corresponds (e.g., the general class of health-related traits or characteristics that the product represents). As with products, in certain embodiments, each category has a name [e.g., a category data structure comprises a name (e.g. text data representing the name)] that provides a convenient, and memorable way to refer to the category.

[0265] In turn, each category is associated with one or more SNP objects, each

SNP object corresponding to a specific SNP. Each SNP object associated with a particular category corresponds to a specific SNP that influences a specific health related trait that relates to the trait or characteristic to which the particular category corresponds. Each SNP object may identify the specific SNP to which it corresponds via a SNP reference that the SNP object comprises. The SNP reference may be an alphanumeric code such as an accepted name of the SNP or other identifying mark or label capable of being stored electronically. The SNP reference may be an alphanumeric code such as a National Center for Biotechnology Information (NCBI) database reference number.

[0266] For example, the schematic of FIG. 6 shows a block diagram 600 of an example of series of products, categories, and SNP objects that are associated with each other. Associated gene objects, to be described in the following, are also shown. The different products and categories are identified by their particular names, and the SNP objects each are identified by a respective SNP reference each comprises. In the example of FIG. 6, the SNP references are NCBI database reference numbers.

[0267] The "FUEL™" product 612 is associated with categories such as "Food

Sensitivity" 622, "Food Breakdown" 624, "Hunger and Weight" 626, and "Vitamins" 628. Several SNP objects corresponding to specific SNPs that influence characteristics related to an individual's sensitivity to different types of foods, and, accordingly, are associated with the "Food Sensitivity" category 622 are shown. In FIG. 6, the lines connecting the SNP objects to different categories indicate the association of each particular SNP object with one or more different categories. The associations may be direct associations or indirect associations (e.g., through mutual association with an intermediate data structure not shown).

[0268] For example, SNP object 642 corresponds to the rs671 SNP, which influences the manner in which an individual processes alcohol. In particular, depending on the particular variant of the rs671 SNP that an individual has, the individual may process alcohol normally, or be impaired in their ability to process alcohol, and likely suffer from adverse effects resulting from alcohol consumption, such as flushing, headaches, fatigue, and sickness. Accordingly, providing individuals with knowledge of the particular variant of the rs671 SNP they possess may allow them to modify their behavior accordingly, for example, by being mindful of the amounts of alcohol that they consume (e.g., on a regular basis, e.g., in social settings).

[0269] Other SNP objects corresponding to SNPs that influence food sensitivity related characteristics, and, accordingly, are associated with the "Food Sensitivity" category 622 are shown. For example, SNP object 644 corresponds to the rs762551 SNP that influences caffeine metabolism, SNP object 646 corresponds to the rs4988235 SNP that influences lactose intolerance, and SNP object 648 corresponds to the rs72921001 SNP that influences an aversion to the herb cilantro (e.g., depending on the particular variant of this SNP that an individual has, they may either perceive cilantro as pleasant tasting or bitter with a soap-like taste).

[0270] In certain examples, multiple SNPs are associated with a particular characteristic and, accordingly, the SNP objects to which they correspond may be grouped together. For example, three SNPS - rs713598 (corresponding to SNP object 650a), rs 10246939 (corresponding to SNP object 650b), and rs 1726866 (corresponding to SNP object 650c) - influence the sensitivity of individuals to bitter tasting foods (e.g., cabbage, broccoli, cauliflower, kale, brussel sprouts, and collard greens), and, accordingly, their enjoyment of or aversion to such foods.

[0271] SNPs correspond to specific locations within or nearby (e.g., a SNP may occur in a promoter region that influences transcription of a particular gene, e.g., a SNP may occur within 5 kb upstream or downstream of a particular gene, e.g., a SNP may occur within 100 kb upstream or downstream of a particular gene, e.g., a SNP may occur within 500 kb upstream or downstream of a particular gene, e.g., a SNP may occur within 1 Mb upstream or downstream of a particular gene) genes in an individual's genetic material. Accordingly, in certain embodiments, as shown in FIG. 6, each SNP object is associated with a gene object that corresponds to the particular gene within or nearby to which the SNP to which the SNP object corresponds is present. For example, the rs671 SNP corresponds to a location within the ALDH2 gene; the rs762551 SNP corresponds to a location within the CYPl A2 gene; the rs4988235 SNP occurs within the MCM6 gene; and the rs72921001 SNP occurs within the OR10A2 gene. Accordingly, SNP object 642

(corresponding to the rs671 SNP) is associated with gene object 662 (corresponding to the ALDH2 gene). Similarly, SNP object 644 (corresponding to the rs762551 SNP) is associated with gene object 664 (corresponding to the CYPl A2 gene); SNP object 646 (corresponding to the rs4988235 SNP) is associated with gene object 666 (corresponding to the MCM6 gene); and SNP object 648 (corresponding to the rs72921001 SNP) is associated with gene object 668 (corresponding to the OR10A2 gene). [0272] Other SNP objects correspond to SNPs that are nearby particular genes of interest and thereby influence characteristics associated with expression of the gene. For example, rsl2696306 is a SNP that lies 1.5 kb downstream from the TERC gene, and influences biological aging associated with the TERC gene. Accordingly, in one example, a SNP object corresponding to the rsl2696306 SNP is associated a gene object

corresponding to the TERC gene.

[0273] In certain embodiments, multiple SNPs of interest occur within a single gene. For example, the three SNPs related to bitter taste - rs713598, rsl0266939, and rs 1726866 - occur within the TAS2R38 gene. Accordingly, SNP objects 650a, 650b, and 650c, which correspond to the rs713598, rsl0246939, and rsl726866 SNPs, respectively, are all associated with a gene object 670 corresponding to the TAS2R38 gene.

[0274] In certain embodiments, different products correspond to different general classes of health-related traits and characteristics. For example, products may be based on particular organs (e.g., product 614, named "AURA™", is related to skin health), or particular habits, activities, or bodily functions. For example, food-related biological characteristics and traits may be covered by a single product or a plurality of products. A single product or a plurality of products may be based on learning and brain function characteristics and traits. A single product or a plurality of products may be based on physical fitness (e.g., cardiovascular strength, agility, flexibility, and/or muscular strength).

[0275] For example, as shown in FIG. 6, another product 616 (e.g., named

"FITCODE™"), relates to a general class of physical fitness-related traits, and,

accordingly, comprises categories associated with endurance 630 ("Endurance"), metabolism 632 ("Metabolism"), the ability of an individual to recover effectively following exercises 634 ("Exercise Recovery"), and cardiovascular fitness and skeletal muscle makeup 636 ("Power Performance").

[0276] In certain embodiments, a particular SNP object is associated with two or more categories. For example, the rsl7782313 SNP, occurring in the FTO gene, influences an individual's appetite. Accordingly, as shown in FIG. 6, the SNP object 652

corresponding to the rs 17782313 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 gene object 672, reflecting the fact that the rs 17782313 SNP occurs in the FTO gene. In certain

embodiments, as with the rsl7782313 SNP object, each of a first category and a second category with which a particular SNP object is associated are associated with a different product. In certain embodiments, a particular 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.

[0277] For example, the SNP object 654 corresponding to the rs 1800795 SNP of the IL-6 gene (accordingly, SNP object 654 is associated with gene object 674, which corresponds to the IL-6 gene) is associated with the "Exercise Recovery" category 634 and the "Power Performance" category 636, both of which are associated with the

"FITCODE™" product 616. In addition, in certain embodiments, a category is associated with two or more products. For example, the "Power Performance" category 636 is associated with the "FITCODE™" product 616, as well as the "SUPERHERO™" product 618, which provides an assessment of a general class of traits related to physical and intellectual performance. [0278] In certain embodiments the hierarchical organization of product, category,

SNP object, gene object, and variant object data structures serves as a flexible template that facilitates both the rapid creation of individual genetic profile assessments from genotyping measurements taken from a plurality of individuals, and the presentation of an individual's genetic profile assessment. In particular, an individual may purchase assessments corresponding to different products, in order to gain insight into the manner in which their personal genome influences the different general classes of health-related traits and characteristics to which each different product corresponds. Accordingly, an individual's genetic profile assessment corresponding to one or more products comprises, for each specific SNP associated with each category that is associated with each of the one or more products, an identification of the particular variant of the specific SNP that the individual has. Typically, the identification is obtained via one or more genotyping measurements performed on a biological sample taken from the individual (e.g., a blood sample, e.g., a cheek swab sample, e.g., a saliva sample, e.g., a hair sample, e.g., hair follicle cells).

[0279] In certain embodiments, an individual may purchase a first assessment corresponding to a first product, and provide a biological sample for genotyping. The individual's biological sample may be stored (e.g., cryogenically frozen). After a period of time, the individual may choose to purchase additional assessments corresponding to other products, and the individual's previously stored biological sample may be taken from storage for additional genotyping measurements of the additional SNPs that are associated with the new products. Moreover, in certain embodiments, additional new products may be created over time, and new assessments corresponding to new products offered to and purchased by individuals. In certain embodiments, as new information related to the influence of new and/or existing SNPs on different specific health related characteristics is elucidated, new SNP objects and gene objects may be created, and new associations between them and new or existing categories and/or products established. In certain embodiments, existing genetic profile assessments of individuals are automatically updated to reflect new information.

C. ii. Hierarchy of Data Structures

[0280] In certain embodiments, in order to facilitate the creation and presentation of individual genetic profile assessments (e.g. corresponding to one or more different products) based on the framework described above, the product, category, SNP object, and gene object data structures described herein are created and associated as a generic hierarchy of data structures to be later associated with the genotyping data of an individual. FIG. 7 is a block diagram of a hierarchy of data structures 700 of an example genetic profile product. In certain embodiments, a developer creates and stores one or more generic hierarchies of data structures in accordance with FIG. 7 that define one or more products that may be purchased and/or accessed by an individual. The hierarchies of data structures are generic in that they contain no personal information for any one individual, but instead define the collection of genes, SNPs, and variants that have relevance to the biological characteristics and/or traits that are encompassed by a product.

[0281] An exemplary data structure of each type is shown to be associated with sub-data structures in FIG. 7 in order to simplify presentation of the figure. It is understood that data structures may be associated to any number of other data structures in the hierarchy if the association is consistent with the associations shown in the block diagram 700 of FIG. 7. For example, category 720b is shown to be associated with gene objects 730a-b while category 720c may be associated with one or more gene objects and/or SNP objects, but any such associations are not shown. In some embodiments, data structures may be created without also forming associations between other structures of relevant types. For example, unassociated or partially associated data structures may be created for planning purposes such as during product or category development (e.g., category 720a has no associations yet because its scope has not been determined yet by the user). For example, unassociated or partially associated data structures may be created to allow genotyping data to be associated with relevant gene objects or SNP objects in order to retain the data in a ready-to-use format in the event that the gene objects and/or SNP objects are later associated with one or more categories.

[0282] Referring still to FIG. 7, product 710 comprises three categories 720a-c and additional information 722. Additional information 722 may be a name of the product, an icon associated with the product, and/or a description of the product. Category 720b comprises two gene objects 730a-b, one SNP object 740, and additional information 732. Additional information 732 may comprise a name of the category, a background image associated with the category, an icon associated with the category, a category order identifier, and/or a description of the category. SNP object 740 is associated with gene object 770. Gene object 730a is associated to three SNP objects 742a-c. Categories may be associated directly to SNP objects, such as category 720b is associated with SNP object 740, or they may be associated indirectly such as SNP objects 742a-c are associated to category 720b via gene object 730a. The ability to form associations indirectly allows all SNP objects associated with a particular gene object to be associated with a category by forming a single association in cases where all SNP objects of a particular gene are relevant to a particular category. The ability to form associations directly allows a particular SNP object to be associated with a category without also forming an association with all other SNP objects associated with the gene object associated with the particular SNP object in cases where only one or a subset of SNP objects of a particular gene object are relevant to a category.

[0283] Gene object 730a is also associated with additional information 744.

Additional information 744 may comprise one or more data structures comprising information such as a unique gene identifier that corresponds gene object 730a to a specific physical gene and descriptive information about the corresponding gene. The gene identifier may be an alphanumeric code such as an accepted name of the gene or other identifying mark or label capable of being stored electronically. Additional information may be stored as a single data structure or a plurality of data structures.

[0284] SNP object 742b is associated with SNP reference 750, and additional information 754. SNP reference 750 is a unique identifier of the SNP that corresponds the SNP object to a specific physical SNP. The SNP reference may be an alphanumeric code such as an accepted name of the gene or other identifying mark or label capable of being stored electronically. The SNP reference may be an alphanumeric code such as a National Center for Biotechnology Information (NCBI) database reference number. Additional information 754 may comprise one or more data structures with other descriptive information about the corresponding SNP.

[0285] Variants of a particular SNP can be represented within a corresponding SNP object using various combinations of data elements such as a measurement outcomes, and qualifiers. For example, a particular variant of a SNP can be identified by a measurement outcome, which is an identifier, such as an alphanumeric code, that identifies the specific alleles corresponding to the particular variant. For example, a measurement outcome such as the string "CC" identifies a first variant of the rs762551 SNP in which an individual has a cytosine (C) at the rs762551 position in each copy of their genetic material. A

measurement outcome such as the string "AC" identifies a second variant of the rs762551 SNP in which an individual has a C in one copy and an adenine (A) in the other at the rs762551 position. A measurement outcome such as the string "AA" identifies a second variant of the rs762551 SNP in which an individual has an A at the rs762551 position in each copy of their genetic material.

[0286] A qualifier is an identifier, such as an alphanumeric code, that identifies a classification of a variant, wherein the classification may be based on the prevalence of the variant within a population, a health-related trait associated with the variant, and/or other relevant classification bases. Qualifiers may be words or short phrases that characterize the variant. For example, "adapt" may be used to characterize variants that are uncommon and/or disadvantageous; "normal" may be used to characterize variants that are common and/or neither advantageous nor disadvantage; and "gifted" may be used to characterize variants that are uncommon and/or advantageous. Additional information may also be included within a SNP object to describe a particular variant.

[0287] In certain embodiments, measurement outcomes and qualifiers that identify and classify, respectively, the same variant are associated with each other to form a variant object associated with the SNP object. For example, variant object 752a comprises measurement outcome 760, qualifier 762. Variant object 752a is also comprises additional information 764. Additional information 764 comprises a description of the variant. For example, the additional information comprises a description of the specific health-related phenotype that an individual with the variant represented by variant object 752a exhibits or an explanation of the prevalence of the variant. A S P object may be associated with a variant object to represent each variant of the particular SNP to which it corresponds. For example, SNP object is associated with three variant objects 752a-c.

[0288] In certain embodiments, the data structures described herein above are stored as a generic hierarchy for use in generating an individual's genetic profile assessment. A collection of data structures corresponding to genes, SNPs, and variants may be organized into one or more categories within a product (as visualized in FIG. 7, for example). Products can be personalized to a particular individual in order to provide them with specific information about their particular genome by populating or associating the generic product with the individual's genotyping data. In certain embodiments, a genetic profile assessment is used to populate an assessment graphical user interface ("assessment GUI") through which an individual views an assessment of his/her genetic profile. In this way, the individual can view an assessment GUI that visualizes his/her genetic profile assessment by showing the individual the particular variants of SNPs that the individual has (e.g., organized in a hierarchy of products and categories).

C. Hi. Adding Genotyping Data to an Individual 's Genetic Profile Assessment

[0289] In certain embodiments, in order to populate an assessment GUI to provide to an individual, genotyping data must be added or associated to the individual's genetic profile assessment. FIG. 8 is a block diagram of exemplary method 800 for adding genotyping data to an individual's genetic profile assessment. In step 810, a processor of a computing device receives genotyping data. In step 820, the processor identifies a gene object corresponding to a gene measured in the genotyping data and a S P object corresponding to a SNP in or nearby the gene (e.g., the SNP occurring within the gene or occurring nearby the gene (e.g., within a promotor region that influences transcription of the gene, e.g., within 5 kb upstream or downstream of the gene, e.g., within 100 kb upstream or downstream of the gene, e.g., within 500 kb upstream or downstream of the gene, e.g., within 1 Mb upstream or downstream of the gene). In certain embodiments, genotyping data is stored as a table of data in a text file where each row corresponds to a unique SNP.

[0290] In step 830, a particular variant of the identified SNP object and its associated qualifier are determined based on data from genotyping measurements. For example, data corresponding to the measurement outcome of a particular variant may be stored as one or more columns at the end of each row. In step 840, the data is stored in the individual's genetic profile assessment. In accordance with method 800, at step 840, the data may be stored in a (previously generic) hierarchy of data structures or the data may be stored separately along with an association between the data and the identified gene object and SNP object. In any case, the stored data (and any generated and stored associations) define the genetic profile assessment for the individual. In step 850, the processor determines if all data of the genotyping data has been stored. If all data has not been stored in the individual's genetic profile assessment, then the method returns to step 820. If all data has been stored, then the method ends 860. In some embodiments, the processor determines if unstored data exists by determining if there is a row of data in the genotyping data below the just processed row.

[0291] FIG. 9 shows exemplary genotyping data 900 that may be added to an individual's genetic profile assessment in accordance with method 800. Genotyping data may take the form of a text file saved by a user, wherein the text file is generated manually or as output from equipment for performing genotyping measurements (e.g. TaqMan™ SNP genotyping assays). FIG. 8 comprises 6 rows of genotyping data from a single biological sample ("RONEN147"). Each row corresponds to data for a different SNP. Each SNP of genotyping data 900 is identified by at least a gene identifier 910 and a SNP reference 920. The gene identifier identifies the gene with which the SNP is associated. In certain embodiments, multiple (e.g., two or more) genes are associated with the SNP (e.g., the SNP may occur nearby two or more genes and influence phenotypes associated with each of the associated genes), and, accordingly, two or more corresponding gene identifiers are listed. Each SNP in the genotyping data has a corresponding variant identified by the allele measurements 930. The measurements "allele 1" and "allele 2" for a given SNP may be compared with measurement outcomes associated with the variants of a SNP object corresponding to the given SNP to populate an individual's genetic profile assessment.

[0292] The genotyping data in FIG. 9 used to populate an individual's genetic profile assessment is generated from one or more biological samples of the individual. However, the one or more biological samples used in populating an individual's genetic profile assessment may also be taken from a different human or a non-human animal. In some embodiments, genotyping data is generated from one or more biological samples of a non-human animal. For example, an individual may supply biological samples of his or her pet in order to understand information about the pet's phenotype in order to assist in providing better care. The animal may be a pet or may be an animal cared for by an individual. For example, the individual may be a veterinarian or a caretaker at a zoo charged with caring for the animal. In some embodiments, genotyping data is generated from one or more biological samples of a ward to whom the individual is a guardian. For example, a parent may supply one or more biological samples to genotyping data for their child in order to improve his/her childrearing.

D. Computer System and Network Architecture

[0293] An implementation of a network environment 1000 for use in the systems, methods, and architectures described herein, is shown in FIG. 10. In 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, 1002c (collectively, 1002). Each resource provider 1002 may include computing resources. In some implementations, computing resources may include any hardware and/or software used to process 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 application servers and/or databases 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 implementations, the resource providers 1002 may be connected over a computer network 1008. Each resource provider 1002 may be connected to one or more computing device 1004a, 1004b, 1004c (collectively, 1004), over the computer network 1008.

[0294] The cloud computing environment 1000 may include a resource manager

1006. The resource manager 1006 may be connected to the resource providers 1002 and the computing devices 1004 over the computer network 1008. In some implementations, the resource manager 1006 may facilitate the provision of computing resources by one or more resource providers 1002 to one or more computing devices 1004. The resource manager 1006 may receive a request for a computing resource from a particular computing device 1004. The resource manager 1006 may identify one or more resource providers 1002 capable of providing the computing resource requested by the computing device 1004. The resource manager 1006 may select a resource provider 1002 to provide the computing resource. The resource manager 1006 may facilitate a connection between the resource provider 1002 and a particular computing device 1004. In some implementations, the resource manager 1006 may establish a connection between a particular resource provider 1002 and a particular computing device 1004. In some implementations, the resource manager 1006 may redirect a particular computing device 1004 to a particular resource provider 1002 with the requested computing resource.

[0295] FIG. 11 shows an example of a computing device 1100 and a mobile computing device 1150 that can be used in the methods and systems described in this disclosure. The computing device 1100 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 1150 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to be limiting.

[0296] The computing device 1100 includes a processor 1102, a memory 1104, a storage device 1106, a high-speed interface 1108 connecting to the memory 1104 and multiple high-speed expansion ports 1110, and a low-speed interface 1112 connecting to a low-speed expansion port 1114 and the storage device 1106. 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 busses, and may be mounted on a common motherboard or in other manners as

appropriate. The processor 1102 can process instructions for execution within the computing device 1100, including instructions stored in the memory 1104 or on the storage device 1106 to display graphical information for a GUI on an external input/output device, such as a display 1116 coupled to the high-speed interface 1108. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Thus, as the term is used herein, where a plurality of functions are described as being performed by "a processor", this encompasses embodiments wherein the plurality of functions are performed by any number of processors (one or more) of any number of computing devices (one or more). Furthermore, where a function is described as being performed by "a processor", this encompasses embodiments wherein the function is performed by any number of processors (one or

- Ill more) of any number of computing devices (one or more) (e.g., in a distributed computing system).

[0297] The memory 1104 stores information within the computing device 1100. In some implementations, the memory 1104 is a volatile memory unit or units. In some implementations, the memory 1104 is a non-volatile memory unit or units. The memory 1104 may also be another form of computer-readable medium, such as a magnetic or optical disk.

[0298] The storage device 1106 is capable of providing mass storage for the computing device 1100. In some implementations, the storage device 1106 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (for example, processor 1102), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices such as computer- or machine-readable mediums (for example, the memory 1104, the storage device 1106, or memory on the processor 1102).

[0299] The high-speed interface 1108 manages bandwidth-intensive operations for the computing device 1100, while the low-speed interface 1112 manages lower bandwidth- intensive operations. Such allocation of functions is an example only. In some

implementations, the high-speed interface 1108 is coupled to the memory 1104, the display 1116 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 1110, which may accept various expansion cards (not shown). In the implementation, the low-speed interface 1112 is coupled to the storage device 1106 and the low-speed expansion port 1114. The low-speed expansion port 1114, which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

[0300] The computing device 1100 may be implemented in a number of 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, components from the computing device 1100 may be combined with other components in a mobile device (not shown), such as a mobile computing device 1150. Each of such devices may contain one or more of the computing device 1100 and the mobile computing device 1150, and an entire system may be made up of multiple computing devices communicating with each other.

[0301] 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, and a transceiver 1168, among other components. The mobile computing device 1150 may also be provided with a storage device, such as a micro-drive 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 the components may be mounted on a common motherboard or in other manners as appropriate. [0302] The processor 1152 can execute instructions within the mobile computing device 1150, including instructions stored in the memory 1164. The processor 1152 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 1152 may provide, for example, for coordination of the other components of the mobile computing device 1150, such as control of user interfaces, applications run by the mobile computing device 1150, and wireless communication by the mobile computing device 1150.

[0303] 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 appropriate display technology. The display interface 1156 may comprise appropriate circuitry for driving the display 1154 to present graphical and other information to a user. The control interface 1158 may receive commands from a user and convert them for submission to the processor 1152. In addition, an external interface 1162 may provide communication with the processor 1152, so as to enable near area communication of the mobile computing device 1150 with other devices. The external interface 1162 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

[0304] The memory 1164 stores information within the mobile computing device

1150. The memory 1164 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 1174 may also be provided and connected to the mobile computing device 1150 through an expansion interface 1172, which may include, for example, a SIMM (Single In Line Memory Module) card interface or a DIMM (Double In Line Memory Module) card interface. The expansion memory 1174 may provide extra storage space for the mobile computing device 1150, or may also store applications or other information for the mobile computing device 1150. Specifically, the expansion memory 1174 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, the expansion memory 1174 may be provided as a security module for the mobile computing device 1150, and may be programmed with instructions that permit secure use of the mobile computing device 1150. In addition, secure applications may be provided via the DIMM cards, along with additional information, such as placing identifying information on the DIMM card in a non-hackable manner.

[0305] The memory may include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below. In some

implementations, instructions are stored in an information carrier and, when executed by one or more processing devices (for example, processor 1152), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as one or more computer- or machine-readable mediums (for example, the memory 1164, the expansion memory 1174, or memory on the processor 1152). In some implementations, the instructions can be received in a propagated signal, for example, over the transceiver 1168 or the external interface 1162.

[0306] The mobile computing device 1150 may communicate wirelessly through the communication interface 1166, which may include digital signal processing circuitry where necessary. The communication interface 1166 may provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA

(Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication may occur, for example, through the transceiver 1168 using a radio-frequency. In addition, short-range communication may occur, such as using a Bluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 1170 may provide additional navigation- and location-related wireless data to the mobile computing device 1150, which may be used as appropriate by applications running on the mobile computing device 1150.

[0307] The mobile computing device 1150 may also communicate audibly using an audio codec 1160, which may receive spoken information from a user and convert it to usable digital information. The audio codec 1160 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 1150. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile computing device 1150.

[0308] The mobile computing device 1150 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 1180. It may also be implemented as part of a smart-phone 1182, personal digital assistant, or other similar mobile device. [0309] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0310] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine- readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.

[0311] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0312] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of

communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0313] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0314] The systems and methods described herein can include one or more artificial intelligence modules (e.g., machine learning modules). For example, an artificial intelligence module (e.g., machine learning module) may be configured to perform a variety of machine learning techniques including, for example, linear and nonlinear regressions, principal component analysis, k-nearest neighbor methods, support vector machine regressions, clustering, and the like. To perform one or more of these techniques, an artificial intelligence module (e.g., machine learning module) may be trained, for example, using example speech data (e.g., pre-recorded questions) and/or alphanumeric data (e.g., lists of particular terms) to identify relationships (e.g., correlations) between a user input and other data types described herein (e.g., data associated with textual queries, structured responses, recommended genetic profile tests, other recommendations related to purchases, and the like). In certain embodiments, the artificial intelligence module (e.g., machine learning module) includes a neural network (e.g., a convolutional neural network).

[0315] While the invention has been particularly shown and described with reference to specific preferred embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

What is claimed is:

1. A method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with an artificial intelligence chatbot, the method comprising:

(a) receiving, by a processor of a computing device, user input of a textual query;

(b) identifying, by the processor, based on the textual query, one or more genetic profile tests related to the textual query (e.g., using a machine learning module), wherein each genetic profile test is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more S Ps (e.g., wherein each corresponding S P influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with); and

(c) providing, by the processor, for each of the one or more identified genetic profile tests, a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising an identification of the genetic profile test (e.g., a name of the test, rendered as text; e.g., an image associated with the test).

2. The method of claim 1, wherein identifying the one or more genetic profile tests comprises:

accessing, by the processor, a database (e.g., a set of text files such as AIML files) comprising, for each of a predefined set of genetic profile tests: (i) an identifier [e.g., a textual label (e.g., representing a name of the genetic profile test)] 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, matching one or more terms in the textual query to (i) the identifier of the genetic profile test and/or (ii) at least one of the one or more keywords.

3. The method of claim 2, wherein the one or more keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

4. The method of any one of claims 1 to 3, comprising identifying the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

5. The method of claim 4, wherein identifying the one or more genetic profile tests comprises:

accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., each reference document comprises information regarding one or more SNPs and the specific health-related phenotypes that they influence);

determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)];

for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and

identifying the one or more genetic profile tests based on the determined degree of matching.

6. The method of any one of claims 1 to 5, wherein the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

7. The method of any one of claims 1 to 6, comprising causing display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

8. The method of claim 7, comprising causing display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

9. The method of claim 7, comprising causing display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

10. The method of any one of claims 1 to 9, wherein the textual query is provided by a voice assistant.

11. A method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with an artificial intelligence chatbot, the method comprising:

(a) receiving, by a processor of a computing device, user input of a textual query, wherein the user is associated with one or more genetic profiles [e.g., the one or more genetic profiles representing results of genetic profile tests performed for the user; e.g., wherein the user is a subscribed (e.g., logged in) user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor];

(b) identifying, by the processor, based on the textual query, one or more recommendations (e.g., purchase recommendation(s)) responsive to the received user input and based at least in part on the one or more genetic profiles for the user (e.g., using a machine learning module); and

(c) providing, by the processor, a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising the one or more recommendations [e.g., a name of a recommended purchase (e.g., a nutritional supplement; e.g., a mobile health device to purchase), rendered as text; e.g., an image associated with a recommended purchase, a textual description responsive to the query (e.g., a recommended meal plan, a recommended exercise plan, etc.)].

12. The method of claim 11, wherein identifying the one or more recommendations comprises:

accessing, by the processor, a database (e.g., a set of text files such as AIML files) comprising, for each of a predefined set of recommendations:

(i) an identifier [e.g., a textual label (e.g., representing a name of the recommendation)] of the recommendation; and

(ii) one or more keywords associated with the identifier of the recommendation; and for each identified recommendation, matching one or more terms in the textual query to (i) the identifier of the recommendation and/or (ii) at least one of the one or more keywords.

13. The method of claim 12, wherein each recommendation is associated with a set of one or more S Ps, and the one or more keywords comprise, for each S P of the set of S Ps associated with the recommendation, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

14. The method of claim 12, wherein at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

15. The method of any one of claims 12 to 14, wherein each recommendation is associated with a set of one or more genes, and the one or more keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

16. The method of any one of claims 11 to 14, comprising identifying the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

17. The method of claim 16, wherein identifying the one or more recommendations comprises:

accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations (e.g., each reference document comprises information regarding one or more SNPs and/or genes and the specific health- related phenotypes that they influence);

for each of one or more prospective recommendations, determining a degree of matching between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation; and identifying the one or more recommendations based on the determined degree of matching.

18. The method of any one of claims 11 to 17, comprising automatically identifying the one or more recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

19. 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 associated with a particular variant of the SNP (e.g., identified via a qualifier), and

the identification of the recommendation is based at least in part on a

correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation, and particular variants of the one more SNPs that the user has, as identified in the genetic profiles associated with the user.

20. 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 the user; and

automatically identifying the one or more recommendations based on the genetic profiles of the user and the received the mobile health data.

21. The method of any one of claims 11 to 20, wherein the one or more recommendations comprise a recommended genetic profile test.

22. The method of any one of claims 11 to 21, wherein the one or more

recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy).

23. The method of any one of claims 11 to 22, wherein the one or more

recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase.

24. The method of any one of claims 11 to 23, wherein the one or more

recommendations comprise one or more members selected from the group consisting of a meal program, a fitness program, a brain wave feedback program, a behavioral program (e.g., a focus program, an ADHD assistance program), and an individualized therapy.

25. The method of claim 24, wherein the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

26. The method of any one of claims 11 to 25, wherein the one or more

recommendations comprise one or more recommended purchases of one or more mobile health devices (and/or one or more software apps operating on a mobile health device).

27. The method of any one of claims 11 to 26, wherein the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

28. The method of any one of claims 11 to 27, comprising causing display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

29. The method of claim 28, comprising causing display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

30. The method of claim 28, comprising causing display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

31. The method of any one of claims 11 to 30, wherein the textual query is provided by a voice assistant.

32. A method of providing purchase recommendations corresponding to genetic profile tests via a user interaction with a voice assistant, the method comprising:

(a) receiving (e.g., via a network), by a processor of a computing device, from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion of the matching recognizable textual phrases as terms in the textual query)];

(b) identifying, by the processor, using the textual query of the structured request, one or more genetic profile tests related to the user speech [e.g., by matching the textual query (e.g., one or more terms of the textual query) to one or more identifiers of genetic profile tests (e.g., stored in a database) (e.g., via a machine learning module; e.g., by identifying one or more sub-routines based on a first portion of the textual query and passing a second portion of the textual query to the identified sub-routines as variables evaluated by the sub-routines to identify the one or more genetic profile tests)]; and (c) providing (e.g., via a network), by the processor, to the voice assistant, one or more structured responses comprising identifications of each of the one or more genetic profile tests, 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 simulated speech based on the structured response, thereby providing to the user audio feedback corresponding to recommendations associated with the one or more identified genetic profile tests.

33. The method of claim 32, wherein step (b) comprises matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with one or more genetic profile tests.

34. The method of claim 33, wherein the one or more keywords comprise, for each SNP of a set of SNPs that the one or more genetic profile tests measure, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

35. The method of any one of claims 32 to 34, comprising identifying the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

36. The method of claim 35, wherein identifying the one or more genetic profile tests comprises: accessing, by the processor, a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., wherein each reference document comprises information regarding one or more SNPs and the specific health related phenotypes that they influence);

determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)];

37. A method of providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with a voice assistant, the method comprising: (a) receiving (e.g., via a network), by a processor of a computing device, from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query,

wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion (up to all) of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion (up to all) of the matching recognizable textual phrases as terms in the textual query)], and

wherein the textual query of the structured request comprises an

identification of the user (e.g., a subscribed user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor) associated with one or more genetic profiles;

(b) identifying, by the processor, using the textual query of the structured request, one or more recommendations (e.g., purchase recommendations) based at least in part on the one or more genetic profiles associated with the user; and

(c) providing (e.g., via a network), by the processor, to the voice assistant, one or more structured responses comprising identifications of each of the one or more recommendations, 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 simulated speech based on the structured response, thereby providing to the user audio feedback corresponding to the one or more recommendations.

38. The method of claim 37, wherein step (b) comprises matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with at least one of the one or more recommendations (e.g., purchase recommendations).

39. The method of claim 38, wherein each recommendation is associated with a set of one or more SNPs, and the one or more stored keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

40. The method of claim 39, wherein at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

41. The method of any one of claims 38 to 40, wherein each recommendation is associated with a set of one or more genes, and the one or more stored keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

42. The method of any one of claims 37 to 41, comprising identifying the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

43. The method of claim 42, wherein identifying the one or more recommendations comprises:

for each of one or more prospective recommendations, determining a degree of matching between the one or more SNPs and/or genes relevant to the user textual query to the SNPs and/or genes associated with the prospective recommendation; and

identifying the one or more recommendations based on the determined degree of matching.

44. The method of any one of claims 37 to 43, comprising automatically identifying the one or more recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

45. The method of any one of claims 37 to 44, wherein:

the identification of the recommendation is based at least in part on a

correspondence (e.g., relationship, e.g., correlation) between particular variants of one or more SNPs associated with the recommendation, and particular variants of the one more SNPs that the user has, as identified in the user's one or more genetic profiles.

46. The method of any one of claims 37 to 45, comprising:

47. The method of any one of claims 37 to 46, wherein the one or more

recommendations comprise a recommended genetic profile test.

48. The method of any one of claims 37 to 47, wherein the one or more

49. The method of any one of claims 37 to 48, wherein the one or more

50. The method of any one of claims 37 to 49, wherein the one or more

51. The method of claim 50, wherein the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

52. The method of any one of claims 37 to 51, wherein the one or more

53. The method of any one of claims 37 to 52, wherein the structured response comprises data corresponding to an identification of a location and name of a vendor from which the user can purchase the recommendation and, the structure response, when executed by the voice assistant, causes the voice assistant to generate audio output providing the location and name of the vendor.

54. A system for providing purchase recommendations corresponding to genetic profile tests via a user interaction with an artificial intelligence chatbot, the system comprising: a processor; and

a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:

(a) receive user input of a textual query;

(b) identify, based on the textual query, one or more genetic profile tests related to the textual query (e.g., using a machine learning module), wherein each genetic profile test is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with); and

(c) provide, for each of the one or more identified genetic profile tests, a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising an identification of the genetic profile test (e.g., a name of the test, rendered as text; e.g., an image associated with the test).

55. 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 a database (e.g., a set of text files such as ADVIL files) comprising, for each of a predefined set of genetic profile tests:

(i) an identifier [e.g., a textual label (e.g., representing a name of the genetic profile test)] of the genetic profile test; and

56. The system of claim 55, wherein the one or more keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

57. The system of any one of claims 54 to 56, wherein the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

58. 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 database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database), wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., each reference document comprises information regarding one or more SNPs and the specific health-related phenotypes that they influence);

determining, using the textual query and information within the plurality of reference documents, one or more SNPs relevant to the user textual query [e.g., by comparing the textual query with textual data (e.g., written words) within the reference document (e.g., by extracting keywords from the textual query and searching for matches in the reference documents; e.g., using a machine learning module that receives as input the textual query and determines the one or more SNPs relevant to the user textual query based on the reference documents)]; for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and

59. The system of any one of claims 54 to 58, wherein the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

60. The system of any one of claims 54 to 59, wherein the instructions, when executed by the processor, cause the processor to cause display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

61. The system of claim 60, wherein the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

62. The system of claim 60, wherein the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

63. The system of any one of claims 54 to 62, wherein the textual query is provided by a voice assistant.

64. A system for providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with an artificial intelligence chatbot, the system comprising:

a processor; and

(a) receive user input of a textual query, wherein the user is associated with one or more genetic profiles [e.g., the one or more genetic profiles

representing results of genetic profile tests performed for the user; e.g., wherein the user is a subscribed (e.g., logged in) user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor]; (b) identify, based on the textual query, one or more recommendations (e.g., purchase recommendation(s)) responsive to the received user input and based at least in part on the one or more genetic profiles for the user (e.g., using a machine learning module); and

(c) provide a graphical representation (e.g., for rendering and/or graphical display on a computing device of the user) comprising the one or more recommendations [e.g., a name of a recommended purchase (e.g., a nutritional supplement; e.g., a mobile health device to purchase), rendered as text; e.g., an image associated with a recommended purchase, a textual description responsive to the query (e.g., a recommended meal plan, a recommended exercise plan, etc.)].

The system of claim 64, wherein the instructions, when executed by the processor, the processor to identify the one or more recommendations by:

accessing, by the processor, a database (e.g., a set of text files such as ADVIL files) comprising, for each of a predefined set of recommendations:

(ii) one or more keywords associated with the identifier of the recommendation; and

for each identified recommendation, matching one or more terms in the textual query to (i) the identifier of the recommendation and/or (ii) at least one of the one or more keywords.

66. The system of claim 65, wherein each recommendation is associated with a set of one or more S Ps, and the one or more keywords comprise, for each SNP of the set of S Ps associated with the recommendation, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

67. The system of claim 66, wherein at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set one or more SNPs (e.g., wherein each corresponding SNP influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

68. The system of any one of claims 65 to 67, wherein each recommendation is associated with a set of one or more genes, and the one or more keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

69. The system of any one of claims 64 to 68, wherein the instructions, when executed by the processor, cause the processor to identify the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

The system of claim 69, wherein the instructions, when executed by the processor, the processor to identify the one or more recommendations by:

accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs and/or genes that are associated with one or more recommendations (e.g., each reference document comprises information regarding one or more SNPs and/or genes and the specific health-related phenotypes that they influence);

71. The system of any one of claims 64 to 70, the instructions, when executed by the processor, cause the processor to automatically identify the one or more recommendations based on a variant of a S P in a genome of the user (e.g., identified via the user's one or more genetic profiles).

72. The system of any one of claims 64 to 71, wherein:

the identification of the recommendation is based at least in part on a

73. 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) mobile health data recorded by a mobile health device of the user; and

automatically identify the one or more recommendations based on the genetic profiles of the user and the received the mobile health data.

74. The system of any one of claims 64 to 73, wherein the one or more

recommendations comprise a recommended genetic profile test.

75. The system of any one of claims 64 to 74, wherein the one or more recommendations comprise a recommended diagnostic test (e.g., test for a particular disease and/or condition, such as a blood measurement of cholesterol levels; e.g., a blood test; e.g., a biopsy).

76. The system of any one of claims 64 to 75, wherein the one or more

77. The system of any one of claims 64 to 76, wherein the one or more

78. The system of claim 77, wherein the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

79. The system of any one of claims 64 to 78, wherein the one or more

80. The system of any one of claims 64 to 79, wherein the graphical representation comprising an identification of the genetic profile test comprises a selectable link that, upon selection by the user (e.g., via a user click using a mouse; e.g., via user tap gesture upon the link using a touch sensitive interface such as a touchscreen) directs a user to a website allowing the user to purchase the genetic profile test.

81. The system of any one of claims 64 to 80, wherein the instructions, when executed by the processor, cause the processor to cause display of (e.g., on a computing device of the user) a graphical user interface (GUI) corresponding to a chat window, wherein the textual query is received via the chat window GUI and the graphical representation comprising an identification of the genetic profile test is rendered within the chat window GUI as a response to the textual query.

82. The system of claim 81, wherein the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive website (e.g., a website that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in).

83. The system of claim 81, wherein the instructions, when executed by the processor, cause the processor to cause display of the chat window GUI within an interactive app [e.g., an app (e.g., an app executing on a mobile device, such as a mobile phone) that the user uses to view genetic profile test results, e.g., thereby allowing the user to identify and purchase additional genetic profile tests they may be interested in (e.g., via in-app purchasing)].

84. The system of any one of claims 64 to 83, wherein the textual query is provided by a voice assistant.

85. A system for providing purchase recommendations corresponding to genetic profile tests via a user interaction with a voice assistant, the system comprising:

a processor; and

(a) receive (e.g., via a network), from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data

corresponding to at least a portion of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion of the matching recognizable textual phrases as terms in the textual query)];

(b) identify, using the textual query of the structured request, one or more genetic profile tests related to the user speech [e.g., by matching the textual query (e.g., one or more terms of the textual query) to one or more identifiers of genetic profile tests (e.g., stored in a database) (e.g., via a machine learning module; e.g., by identifying one or more sub-routines based on a first portion of the textual query and passing a second portion of the textual query to the identified sub- routines as variables evaluated by the sub-routines to identify the one or more genetic profile tests)]; and

(c) provide (e.g., via a network), to the voice assistant, one or more structured responses comprising identifications of each of the one or more genetic profile tests, 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 simulated speech based on the structured response, thereby providing to the user audio feedback corresponding to recommendations associated with the one or more identified genetic profile tests.

86. The system of claim 85, wherein the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests related to the user speech by matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with one or more genetic profile tests.

87. The system of claim 86, wherein the one or more keywords comprise, for each SNP of a set of SNPs that the one or more genetic profile tests measure, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

88. The system of any one of claims 85 to 87, wherein the instructions, when executed by the processor, cause the processor to identify the one or more genetic profile tests based in part on information within one or more reference documents stored in a database of reference documents.

The system of claim 88, wherein the instructions, when executed by the processor, the processor to identify the one or more genetic profile tests by:

accessing a database comprising a plurality of reference documents (e.g., published literature; e.g., a plurality of webpages of a public database) wherein each reference document is associated with one or more SNPs that are measured via the one or more genetic profile tests (e.g., wherein each reference document comprises information regarding one or more SNPs and the specific health related phenotypes that they influence);

for each of one or more prospective genetic profile tests, determining a degree of matching between the one or more SNPs relevant to the user textual query to the set of one or more SNPs that the prospective genetic profile test measures; and identifying the one or more genetic profile tests based on the determined degree of matching.

90. A system for providing consumer feedback corresponding to one or more genetic profile tests via a user interaction with a voice assistant, the system comprising:

a processor; and

(a) receive (e.g., via a network), from the voice assistant (e.g., a processor of the voice assistant), a structured request comprising a textual query, wherein the structured request is generated by the voice assistant in response to user speech [e.g., by: detecting the user speech and generating speech data corresponding to at least a portion (up to all) of the user speech and processing the user speech data to generate the textual query (e.g., by generating textual speech data, matching (e.g., via a machine learning module) the textual speech data to one or more recognizable textual phrases stored in a database, and using at least a portion (up to all) of the matching recognizable textual phrases as terms in the textual query)], and wherein the textual query of the structured request comprises an identification of the user (e.g., a subscribed user for whom one or more genetic profile tests have been conducted and for whom one or more genetic profiles have been created and are stored in a database accessible by the processor) associated with one or more genetic profiles; (b) identify, using the textual query of the structured request, one or more recommendations (e.g., purchase recommendations) based at least in part on the one or more genetic profiles associated with the user; and

(c) provide (e.g., via a network), to the voice assistant, one or more structured responses comprising identifications of each of the one or more recommendations, 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 simulated speech based on the structured response, thereby providing to the user audio feedback corresponding to the one or more recommendations.

91. The system of claim 90, wherein the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by matching the textual query (e.g., one or more terms of the textual query) to one or more stored keywords, each associated with at least one of the one or more recommendations (e.g., purchase recommendations).

92. The system of claim 91, wherein each recommendation is associated with a set of one or more SNPs, and the one or more stored keywords comprise, for each SNP of the set of SNPs that the genetic profile test measures, a name of a gene with which the SNP is associated (e.g., a name of a gene within which the SNP occurs; e.g., a name of a gene whose transcription the SNP influences).

93. The system of claim 92, wherein at least one recommendation of the one or more recommendations is a genetic profile test that is associated with a general class of health related phenotypes (e.g., represented by a product) and corresponds to a measurement of a specific set of one or more S Ps (e.g., wherein each corresponding S P influences a specific health related trait associated with the general class of health related phenotypes that the genetic profile test is associated with), and the set of SNPs associated with the genetic profile test are the SNPs that the genetic profile test measures.

94. The system of any one of claims 91 to 93, wherein each recommendation is associated with a set of one or more genes, and the one or more keywords comprise one or more keywords corresponding to names of the genes associated with the recommendation.

95. The system of any one of claims 90 to 94, wherein the instructions, when executed by the processor, cause the processor to identify the one or more recommendations based in part on information within one or more reference documents stored in a database of reference documents.

96. The system of claim 95, wherein the instructions, when executed by the processor, cause the processor to identify the one or more recommendations by:

97. The system of any one of claims 90 to 96, wherein the instructions, when executed by the processor, cause the processor to automatically identify the one or more

recommendations based on a variant of a SNP in a genome of the user (e.g., identified via the user's one or more genetic profiles).

98. The system of any one of claims 90 to 97, wherein:

the identification of the recommendation is based at least in part on a

99. The system of any one of claims 90 to 98, wherein the instructions, when executed by the processor, cause the processor to:

100. The system of any one of claims 90 to 99, wherein the one or more

recommendations comprise a recommended genetic profile test.

101. The system of any one of claims 90 to 100, wherein the one or more

102. The system of any one of claims 90 to 101, wherein the one or more recommendations comprise a recommendation of one or more supplements (e.g., nutritional supplements) to purchase.

103. The system of any one of claims 90 to 102, wherein the one or more

104. The system of claim 103, wherein the one or more members are individualized programs and/or therapies based on the one or more genetic profiles of the user.

105. The system of any one of claims 90 to 104, wherein the one or more

106. The system of any one of claims 90 to 105, wherein the structured response comprises data corresponding to an identification of a location and name of a vendor from which the user can purchase the recommendation and, the structure response, when executed by the voice assistant, causes the voice assistant to generate audio output providing the location and name of the vendor.