JP6538222B1 - INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING PROGRAM - Google Patents

Abstract

An object of the present invention is to appropriately provide information on a disease to a user whose information on a gene is unknown. According to one embodiment, an information processing apparatus includes an acquisition unit and a notification unit. An acquisition part acquires the action information which shows the action on the network of the risk user who is a user estimated to have the onset risk which a predetermined disease develops based on the analysis result by gene analysis. The notification unit notifies a target user who is a user having action information associated with the action information acquired by the acquisition unit, with predetermined information based on a predetermined disease. [Selected figure] Figure 4

Description

  Embodiments of the present invention relate to an information processing apparatus, an information processing method, and an information processing program.

  In recent years, with the development of networks represented by the Internet, various services have come to be provided to users.

  For example, Patent Document 1 discloses a technique for providing information useful for user's health according to user's genetic test information.

JP, 2016-33795, A

  However, in the above-mentioned prior art, it is not always possible to appropriately provide information on a disease to a user whose information on a gene is unknown. For example, in the above-mentioned prior art, the target user is identified the content detected by the search using at least one of the disease related to the target user and the factor of the disease specified from the gene analysis result of the target user. Send.

  In the above-mentioned prior art, for example, even if it is possible to efficiently deliver information useful for health, for example, the information on the gene is appropriately provided to the user having unknown information on the information on the gene. It is not always possible.

  This application is made in view of the above, and an object of the present invention is to provide an information processor which can appropriately provide information on a disease to a user whose information on a gene is unknown.

  An acquisition unit for acquiring behavior information indicating an activity on a network of a risk user who is a user estimated to have a risk of developing a predetermined disease based on an analysis result by genetic analysis, and the behavior acquired by the acquisition unit The information processing apparatus is characterized by further comprising: a notification unit that notifies predetermined information based on the predetermined disease to a target user who is a user having action information related to the information.

  According to one aspect of the embodiment, it is possible to appropriately provide information on a disease to a user whose information on a gene is unknown.

FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. FIG. 2 is a diagram illustrating an example of the information notification performed in the information processing according to the embodiment. FIG. 3 is a diagram illustrating an exemplary configuration of the information processing system according to the embodiment. FIG. 4 is a diagram illustrating an exemplary configuration of the information processing apparatus according to the embodiment. FIG. 5 is a diagram illustrating an example of the behavior information storage unit according to the embodiment. FIG. 6 is a diagram showing an example of a disease information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of the tendency information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of the content information storage unit according to the embodiment. FIG. 9 is a flowchart of the analysis procedure of the information processing apparatus according to the embodiment. FIG. 10 is a flowchart of a process procedure of the notification process performed by the information processing apparatus according to the embodiment. FIG. 11 is a hardware configuration diagram showing an example of a computer for realizing the function of the information processing apparatus.

  Hereinafter, an information processing apparatus, an information processing method, and a mode for carrying out an information processing apparatus program according to the present application (hereinafter, referred to as “embodiment”) will be described with reference to the drawings. Note that the information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by the embodiments. Moreover, in the following embodiments, the same reference numerals are given to the same parts, and the overlapping description will be omitted.

[1. Information processing]
First, an example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. Information processing according to the embodiment is performed by the information processing apparatus 100 illustrated in FIG.

  Further, as shown in FIG. 1, the information processing system 1 according to the embodiment includes a terminal device 10 and an information processing device 100. The terminal device 10 and the information processing device 100 are communicably connected by wire or wireless via a network. The information processing system 1 illustrated in FIG. 1 may include a plurality of terminal devices 10 and a plurality of information processing apparatuses 100. Although described later, the information processing system 1 also includes the external server 60.

  The terminal device 10 is a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. For example, the terminal device 10 displays the information received from the information processing device 100 on a screen or outputs the sound.

  Here, the premise of the present embodiment will be described. For example, based on the relationship between the purchase behavior of the user on the network (for example, the behavior of buying a product that is a site) and the search behavior (for example, the behavior of searching information using a certain search keyword) There is a data analysis method (also referred to as "quality expansion search") to search what kind of purchase behavior a user who is not acting performs.

  The "quality extension search" will be described more specifically using an example. For example, it is assumed that a user who performs purchasing behavior such as "buy sake" tends to search for information using a search keyword "beer", and a relationship is found between the purchasing behavior and the search behavior. . In such a case, if an unknown target person among the target persons whose purchasing behavior is unknown searches for information using the search keyword "beer", there is a possibility that this unknown target person also "buys alcohol" Predict that there is. Thus, the "quality expansion search" is, for example, a user who is expected to perform an action from a group of users using a plurality of types of information (in this example, there is a possibility of "purchase") People) are effectively explored.

  The information processing performed by the information processing apparatus 100 according to the embodiment focuses on such “quality expanded search” and effectively gives the user “notice” about a disease. For example, among the diseases, there is a disease called rare disease with very low morbidity, such as one in hundreds of thousands. Many rare diseases are hereditary, and include rare types of infections, rare types of immune diseases, rare types of cancer, and the like.

  Even if the patient is suffering from a rare disease, the patient may not be able to recognize it as a rare disease without having any awareness of suffering from a rare disease, because there may be no obvious symptoms immediately. There are many. The information processing according to the embodiment uses the concept of quality expanded search as described above to make the user effectively recognize the possibility of suffering from such rare diseases and various other diseases, and directs them to treatment. It is processing to make it happen.

  Specifically, the information processing apparatus 100 according to the embodiment is action information indicating an action on the network of a risk user who is a user estimated to have a risk of developing a predetermined disease based on an analysis result by gene analysis. Is acquired, and predetermined information based on a predetermined disease is notified to a target user who is a user having action information related to the acquired action information. For example, a user who is genetically at risk of developing a predetermined disease tends to take action to promote the onset.

  To give a simple example, a user who develops alcoholism is likely to be a gene type that causes alcoholism, and such a user is a gene type or a gene related to that gene type. It is also likely to be a constitution that prefers alcoholic beverages because of the influence of having a type. For this reason, these users tend to purchase alcoholic beverages.

  In the information processing apparatus 100, a user whose genetic information is unknown because a genetic test has not been received is a user who has a gene type that is a onset factor for a predetermined disease to develop based on an analysis result by gene analysis, that is, a predetermined disease occurs. If you are acting similar to a risk user who is a user who is estimated to have a risk of developing the disease (in the above example, purchase an alcoholic drink), predict that there is a risk of developing a similar disease, Provide information to make the person aware of possible alcoholism. Hereinafter, the information processing according to the embodiment will be described using an example.

  First, the information processing apparatus 100 acquires action information indicating an action of each user on the Internet (step S1). The target user whose behavior information is to be acquired is, for example, a user who can log in directly or indirectly to the information processing apparatus 100 (a login ID has been issued). Further, the target user whose behavior information is to be acquired is, for example, a user who is performing member registration with respect to the business owner who manages the information processing apparatus 100 (referred to as "business owner Z").

  Further, the action information indicating the action on the Internet of the user is, for example, search information which is information related to a search action (for example, information search using a search keyword キ ー ワ ー ド), purchase action (for example, a predetermined shopping site) In purchasing information that is information about purchasing 〇, browsing information that is information about browsing behavior (for example, browsing sports information site ○), and reply information that indicates the contents answered to a predetermined questionnaire etc. is there. Therefore, the search information can be restated as a search history, the purchase information as a purchase history, the browsing information as a browsing history, and the answer information as an answer history.

  For example, the information processing apparatus 100 can acquire search information from a server apparatus corresponding to the external server 60 and providing a search service. Further, the information processing apparatus 100 is a server device corresponding to the external server 60, and can acquire purchase information from a server device that provides a shopping service. Further, the information processing apparatus 100 can acquire browsing information from a server apparatus corresponding to the external server 60 and providing a portal site including various article contents and the like. Further, the information processing apparatus 100 stores the acquired action information in the action information storage unit 121.

  Next, the information processing apparatus 100 performs gene analysis among the users who have acquired the action information (for the user who has received the gene test and who knows the gene information), the gene analysis result of the user That is, based on the genetic information, it is estimated whether or not there is a risk of developing a disease, and a user having a risk of developing is extracted (step S2). For example, the information processing apparatus 100 estimates, based on the gene type indicated by the gene information, whether or not the user has a risk of developing a disease for each user.

  The genetic information is an analysis result obtained by genetic testing, and is, for example, a list of genes possessed by the user. Also, genetic information is information indicating what type (genotype) a gene related to the onset of a specific disease is. For example, a gene related to the onset of “cancer” is referred to as “GINE1” for convenience of explanation. And if the type of "GINE1" is "a" type, it is known that the incidence rate of "cancer" is high, especially if it is known that the incidence rate of "cancer" is high.

  Note that the information providing apparatus 100 can obtain such gene information in advance from an examination organization that has performed a gene examination. For example, the information providing apparatus 100 acquires gene information by accessing a server device (examination institution server) belonging to the examination institution. In addition, a predetermined contract is concluded between the business owner who manages the information providing apparatus 100 (hereinafter, referred to as “business owner Z”) and the inspection organization, and the user who receives the genetic test receives approval. Such acquisition of genetic information is possible. The information providing apparatus 100 may receive provision of gene information directly from the user who has tested the gene test. That is, it is not limited how the information providing apparatus 100 acquires the gene information.

  Further, the information providing apparatus 100 may store the acquired gene information in a predetermined storage unit (not shown) in the own apparatus. For example, the information providing device 100 can store, as gene information, what type of gene each user has. For example, the information providing apparatus 100 can store, as gene information, what type of gene each user has among genes having a plurality of types by having SNP (Single Nucleotide Polymorphism). In addition, the information providing apparatus 100 can store, as genetic information, what type of gene each user has in a gene involved in the onset of a disease. Therefore, in step S2, the information processing apparatus 100 can estimate whether or not to have the onset risk based on the gene information stored in advance.

  Here, in the example of FIG. 1, the information processing apparatus 100 is a user who has acquired the behavior information and has identified the user U11, U12, and U13 (users U11 to U13) among the users who have received the genetic test. Is assumed to have a risk of developing disease A because it is a gene type (referred to as “gene type TY1”) that is a cause of alcoholism (hereinafter referred to as “disease A”). . Below, users U11-U13 are written with risk users U11-U13.

  Next, the information processing apparatus 100 acquires purchase information as action information corresponding to the risk users U11 to U13, and the correlation between the acquired purchase information and the disease A corresponding to the risk users U11 to U13 is Analyze (correlation analysis) (step S3). For example, the information processing apparatus 100 acquires purchase information corresponding to the risk users U11 to U13 from the action information storage unit 121. As another example, when it is estimated that the information processing apparatus 100 has a risk of developing the disease B (gene type TY2) for the users U14 and U15, the information processing apparatus 100 acquires purchase information of the risk users U14 and U15. , The correlation between the acquired behavior information and the disease B corresponding to the risk users U14 and U15. That is, the information processing apparatus 100 analyzes the correlation between the behavior information and the disease for each combination of the disease and the behavior information of the risk user who is estimated to have the risk of developing the disease. In addition, correlation is relationship based on a tendency.

  Here, the action information storage unit 121 will be briefly described. In the example of FIG. 1, the action information storage unit 121 stores, as action information, purchase information (purchase history) indicating the product name of the product purchased by the user so far for each user. Therefore, in the example of FIG. 1, the information processing apparatus 100 is purchase information indicating that the risk user U11 has purchased “beer PD2-1”, purchase information indicating that “Sake PD2-2” has been purchased, “supplement information” Purchase information indicating that the PD2-3 has been purchased is acquired. The description of the risk users U12 and U13 is omitted.

  As shown in FIG. 1, according to the correlation analysis, the information processing apparatus 100 has a risk that the risk users U11 to U13 who are at risk of developing the disease A tend to purchase the beer PD2-1 and the sake PD2-2. It is assumed that analysis results are obtained. In other words, the information processing apparatus 100 “purchase beer PD2-1” and “purchase sake PD2-2” have a correlation (relationship based on the tendency) with the disease A corresponding to the risk users U11 to U13. It is assumed that the analysis result of “to do” is obtained. Moreover, this analysis result shows that "purchasing beer PD2-1" and "purchasing Sake PD2-2" are correlated with disease A. Here, this correlation is referred to as “correlation T”.

  Next, the information processing apparatus 100 identifies a target user among unknown users who have not performed gene analysis and whose gene information is unknown among the users who have acquired the action information (step S4). For example, the information processing apparatus 100 acquires behavior information corresponding to an unknown user, and associates with the behavior information corresponding to the correlation T among unknown users based on the acquired behavior information (for example, identical or similar) ) Identify a target user who is a user having behavior information. In the example of FIG. 1, the behavior information corresponding to the correlation T is behavior information indicating “purchasing beer PD 2-1” and behavior information indicating “purchasing sake S PD 2-2”.

  In the example shown in FIG. 1, in the action information storage unit 121, the user U 52 among the unknown users indicates action information indicating "purchasing beer PD2-1" and "purchasing sake Sake PD2-2". Have action information. Therefore, in such an example, the information processing apparatus 100 identifies the user U52 as a target user. The user U52 is hereinafter referred to as a target user U52.

  Then, the information processing apparatus 100 notifies the target user U 52 of predetermined information based on the disease (step S5). In the example of FIG. 1, the target user U52 has the same action information as the risk users U11 to U13. From this, it can be said that the target user U52 is highly likely to develop the disease A since the risk users U11 to U13 have the same gene type TY1. From such a point of view, the information processing apparatus 100 causes the symptom related to the disease A to eventually appear in the target user U52, or from the viewpoint that the target user U52 eventually develops the disease A, The predetermined information based on the disease A is notified in order to make the disease be recognized and to be dealt with.

  An example of the information notification performed by the information processing apparatus 100 will be described with reference to FIG. FIG. 2 is a diagram illustrating an example of the information notification performed in the information processing according to the embodiment. As shown in FIG. 1, the information processing apparatus 100 notifies in stages the first content, the second content, and the third content. The first content is content in which information related to a disease is displayed, and is content displayed in a predetermined page in a manner such as advertisement content (for example, banner advertisement). The second content is content (check site) for causing the user to input a symptom of feeling. The third content is content in which information on a disease predicted based on input information to the second content is displayed.

  It will be described more specifically. For example, it is assumed that the target user U 52 uses the terminal device 10 to access, among the external servers 60 that cooperate with the information processing device 100, a server device (referred to as a content server) that provides a portal site. In such a case, the content server distributes the portal site P1 to the terminal device 10. Further, assuming that the portal site P1 includes the display frame F1 in which the advertising content and the like are displayed, the terminal device 10 that has received the portal site P1 displays the information frame 100 on the display frame F1. Request the content to be. When the information processing apparatus 100 receives the request, the information processing apparatus 100 generates a first content.

  For example, the information processing apparatus 100 displays the content C1 that displays the content of the symptom related to the disease A that may appear in the target user U52 and / or the disease A that the target user U52 may develop. 1) An example of content is generated. The information processing apparatus 100 may generate the content C1 so that content information indicating the content of a typical symptom of the disease A is displayed. Then, the information processing apparatus 100 distributes the generated content C1 to the terminal device 10. Thereby, the terminal device 10 displays the portal site P1 such that the content C1 including the content information is displayed in the display frame F1, as shown in FIG. 2A.

  The information processing apparatus 100 does not display the disease name of the disease A (in this example, “alcoholism”) on the content C1. For example, even if the content C1 described as "alcoholism" is suddenly displayed, the target user U52 has no feeling at all in "alcoholism" or a disease name that "alcoholism" first sees If so, I do not want to select such content C1. On the other hand, if the content information indicating the content of the symptom related to the disease A is displayed, it is considered that the possibility of raising the interest and selecting it becomes high. Therefore, the information processing apparatus 100 can enhance the appeal of the content C1 by notifying the content C1 in a state in which the disease name can not be identified only by displaying only the content of the symptom. .

  In such a state, it is assumed that the target user U52 selects the content C1. In such a case, the information processing apparatus 100 generates the content C2 (an example of the second content) for causing the target user U52 to input a symptom with a feeling. For example, the information processing apparatus 100 generates content C2 for selecting content information corresponding to a symptom with a heart from content information indicating the content of a typical symptom of the disease A. Then, the information processing apparatus 100 distributes the generated content C2 to the terminal device 10. Thereby, the terminal device 10 displays the content C2 as shown in FIG. 2 (b).

  In such a state, it is assumed that the target user U 52 selects the content information as shown in FIG. 2B and presses the “next” button. In such a case, the information processing apparatus 100 finally predicts a disease that the target user U52 may develop based on the input information input (selected) by the target user U52 in the content C2 (step S6). For example, it is assumed that the information processing apparatus 100 has finally predicted that the target user U 52 has a high risk of developing the disease A. Further, it is assumed that the information processing apparatus 100 predicts that a symptom related to the disease A will appear in the future.

  Based on such prediction, the information processing apparatus 100 generates content C3 (an example of the third content) in which information related to the disease A is displayed. For example, the information processing apparatus 100 generates content C3 in which proposal information suggesting to visit a specialized medical institution of disease A is displayed together with the disease name "alcoholism" of disease A. Then, the information processing apparatus 100 distributes the generated content C3 to the terminal device 10. Thereby, the terminal device 10 displays the content C3 as shown in FIG. 2 (c).

  Now, as described with reference to FIG. 1, the information processing apparatus 100 according to the embodiment is a user of a risk user who is a user estimated to have the onset risk of developing a predetermined disease based on an analysis result by gene analysis. Behavior information indicating a behavior on the network is acquired, and predetermined information based on a predetermined disease is notified to a target user who is a user having behavior information related to the acquired behavior information.

  Thereby, the information processing apparatus 100 according to the embodiment can appropriately provide the information on the disease to the user whose information on the gene is unknown. Specifically, the information processing apparatus 100 can specify with high accuracy a user who is highly likely to develop a disease caused by a gene type even if the genetic information is unknown, and the information processing apparatus 100 can detect the disease for the user Can be effectively recognized. Further, as a result, since the information processing apparatus 100 can contribute to, for example, early detection of a disease, it can contribute to the comfortable daily life of the user.

[2. System configuration]
Next, the configuration of the information processing system according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating an exemplary configuration of the information processing system 1 according to the embodiment. As shown in FIG. 3, the information processing system 1 includes a terminal device 10, an external server 60, and an information processing device 100. The terminal device 10, the inspection agency server 30, the external server 60, and the information processing apparatus 100 are communicably connected by wire or wireless via the network N.

  The inspection organization server 30 is a server device belonging to a predetermined inspection organization which conducts genetic examination, and accumulates genetic information as a result of examination. For example, in response to a request from the information processing device 100, the inspection agency server 30 transmits gene information to the information processing device 100.

  The external server 60 is a server device that is the source of various action information. For example, the external server 60 is a shopping server that provides an EC (Electronic Commerce) site such as a shopping site or an electronic mall. The shopping server stores purchase information (an example of action information) which is information related to the purchase made by the user.

  Also, for example, the external server 60 is a search server that provides a search service. The search server stores a search action (an example of action information) which is information on a search performed by a user using a search keyword.

  Also, for example, the external server 60 is a content server that distributes web pages including advertisement content and article content. The content server stores advertisement browsing information (an example of behavior information) which is information related to browsing behavior in which a user browses advertisement content. In addition, the content server stores article browsing information (an example of behavior information) which is information on a browsing behavior in which a user browses article content.

  In addition, for example, the external server 60 can also accumulate action information indicated by the contents answered to a predetermined questionnaire.

[3. Configuration of information processing apparatus]
Next, the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram illustrating an exemplary configuration of the information processing apparatus 100 according to the embodiment. As shown in FIG. 4, the information processing apparatus 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(About communication unit 110)
The communication unit 110 is realized by, for example, a network interface card (NIC). The communication unit 110 is connected to the network N in a wired or wireless manner, and transmits and receives information to and from, for example, the terminal device 10, the inspection agency server 30, and the external server 60.

(About storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a random access memory (RAM), a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes an action information storage unit 121, a disease information storage unit 122, a tendency information storage unit 123, and a content information storage unit 124.

(Action information storage unit 121)
The action information storage unit 121 stores action information indicating the action of the user on the network. For example, the action information storage unit 121 stores, as action information, purchase information that is information related to the purchase action of the user, search information that is information related to the search action of the user, and browsing information that is information related to the user's browse action. In addition, the behavior information storage unit 121 may also store behavior information indicated by a result of an answer to the questionnaire.

  The inside of the action information storage unit 121 is divided into a purchase information storage unit 121-1 that stores purchase information, a search information storage unit 121-2 that stores search information, a browsing information storage unit 121-3 that stores browsing information, and the like. Be Here, FIG. 5 shows an example of the action information storage unit 121 according to the embodiment. In this example, the purchase information storage unit 121-1 is shown as an example. The purchase information storage unit 121-1 stores, as purchase information, a history of purchase behavior (purchase history) indicating what and when the user purchased. Here, an example of the action information storage unit 121 according to the embodiment is shown. In the example of FIG. 5, the purchase information storage unit 121-1 has items such as “user type”, “classification of gene predicted disease”, “user ID”, “purchased product”, and “purchase date”.

  "User type" indicates a category such as what kind of user the user is classified into. In the present embodiment, the users are roughly classified into “risk users” who are users estimated to have an onset risk based on genetic information, and “risk-free users” estimated to have no onset risk. Although "risk users" and "risk-free users" both have genetic information known by genetic testing, some unknown users have unknown genetic information because they have not received genetic testing. Therefore, as the "user type", there is also an "unknown user".

  The process of estimating the classification for each user is performed by the estimation unit 132 described later. For example, the estimation unit 132 is one of “risk user” or “risk-free user” for each user whose gene information is known among the users whose behavior information is acquired, based on the user's gene information Estimate.

  The “classification of gene predicted diseases” indicates symptoms or diseases corresponding to the user. For example, "risk user" is associated with disease information indicating a disease at risk of developing. In the example of FIG. 5, in accordance with the explanation in FIG. 1, “information indicating disease A” is associated with the risk users U11, U12, and U13 as “classification regarding disease”. In addition, the information of the gene type used as the onset factor of a disease may also be input into "classification of a gene prediction disease." For example, “TY1” may be input as a gene type that is a cause of disease A, together with information indicating disease A.

  In addition, since the "non-risk user" has no risk of developing the disease, the "classification of the gene prediction disease" corresponding to the "non-risk user" is blank as shown in FIG. The “unknown user” can not estimate the onset risk from the gene information because the gene information is unknown. Therefore, the "classification of gene predicted diseases" corresponding to the "unknown user" is blank as shown in FIG.

  "User ID" indicates identification information for identifying "risk user", "risk free user" and "unknown user". Although shown above, in the present embodiment, for example, the expression such as the user U11 means that the user is the user associated with the user ID "U11". “Purchased product” indicates a product purchased by the user indicated by the corresponding user ID. “Purchasing date” indicates the date and time when the “purchased item” was purchased by the user indicated by the corresponding user ID.

  That is, in the example of FIG. 5, the risk user U11 who is estimated to have the risk of developing the disease has performed the purchasing action of "buy beer PD2-1" at "Jan. 10, 2018". Show.

  The configuration of the stored information is the same for the search information storage unit 121-2 and the browse information storage unit 121-3, and thus the detailed description will be omitted. For example, the search information storage unit 121-2 associates and stores a search keyword and the date and time when the search keyword was input for each user. Further, the browse information storage unit 121-3 associates and stores identification information identifying the content browsed for each user and the browsed date and time.

(Disease information storage unit 122)
The disease information storage unit 122 associates and stores a symptom and a disease (a disease name) caused by the symptom. Here, FIG. 6 shows an example of the disease information storage unit 122 according to the embodiment. In the example of FIG. 6, the disease information storage unit 122 has items such as "symptom" and "disease name".

  "Symptom" is content information indicating the contents of various symptoms related to a disease. In the example of FIG. 6, a conceptual expression such as the symptom Sy1 is used, but it is a concrete symptom such as "high blood pressure, severe palpitations", etc. in practice. "Disease name" indicates the disease name of the disease caused by the corresponding "symptom". That is, in the example of FIG. 6, an example in which the disease caused by the symptom Sy1 is the disease A is shown.

(Trend information storage unit 123)
The tendency information storage unit 123 stores the combination of the behavior information and the disease information for which the analysis result of being correlated with each other is obtained. Here, FIG. 7 shows an example of the tendency information storage unit 123 according to the embodiment. In the example of FIG. 7, the tendency information storage unit 122 has items such as “gene predicted disease” and “action tendency”.

  The “gene predicted disease” is disease information (eg, a disease name) indicating a disease for which an analysis result of correlation is obtained. The "behavioral tendency" indicates behavioral information correlated with the corresponding "gene predictive disease". That is, in the example of FIG. 7, the user who is estimated to have the risk of developing the symptom A "purchase beer PD2-1", "purchase sake PD2-2", and "search for information on liquors" Indicates a trend. In other words, it indicates that the disease information is correlated with the action information indicating an action such as “purchase beer PD2-1”, “purchase sake PD2-2”, “search for information on liquor”. .

(Content information storage unit 124)
The content information storage unit 124 is a storage unit that stores content (specifically, data of content) to be notified to the user according to a disease or condition corresponding to the user. Here, FIG. 8 shows an example of the content information storage unit 124 according to the embodiment. In the example of FIG. 8, the content information storage unit 124 has items such as “gene predicted disease” and “content information”. Note that "C1" included in the "content information" indicates content data that is an example of the first content, "C2" indicates content data that is an example of the second content, and "C3" is an example of the third content Indicates content data.

  The “gene predicted disease” is disease information indicating the disease of the user estimated to be at risk of developing the disease. The "content information" is content corresponding to the corresponding "gene prediction disease", and is, for example, content to which content information of a symptom represented by the corresponding "gene prediction disease" is given in advance. That is, in the example of FIG. 8, for the user corresponding to the disease A, the data of the first content is “DA1-1”, the data of the second content is “DA1-2”, and the data of the third content is The example by which "DA1-3" is matched is shown. The user corresponding to the disease A is a risk user who is estimated to be at risk of developing the disease A.

(About the control unit 130)
Returning to FIG. 4, the control unit 130 executes various programs stored in a storage device in the information providing apparatus 100 using a RAM as a work area by a central processing unit (CPU), a micro processing unit (MPU) or the like. Is realized by Further, the control unit 130 is realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As shown in FIG. 4, the control unit 130 includes an acquisition unit 131, an estimation unit 132, a prediction unit 133, an analysis unit 134, a generation unit 135, a notification unit 136, and an update unit 137. Implement or execute the functions and actions of the information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 4, and may be another configuration as long as it performs the information processing described later. Further, the connection relationship of each processing unit included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 4, and may be another connection relationship.

(About acquisition unit 131)
The acquisition unit 131 acquires behavior information indicating the behavior of the user on the network. For example, the acquisition unit 131 acquires, as the action information of the user, purchase information that is information related to the purchase made by the user. For example, the acquisition unit 131 acquires purchase information by accessing the external server 60 (for example, a shopping server). For example, the acquisition unit 131 periodically accesses the external server 60 to acquire purchase information. In addition, the acquisition unit 131 stores the acquired purchase information in the behavior information storage unit 121 as illustrated in FIG. The acquisition unit 131 also acquires search information and browsing information in the same manner.

  In addition, the acquisition unit 131 acquires behavior information of a risk user who is estimated to have a risk of developing a disease based on genetic information. For example, when it is estimated by the estimation unit 132 whether or not there is a risk of developing a disease for each user, the acquisition unit 131 acquires the action information of the risk user from the action information storage unit 121. In addition, when it is estimated by the estimation unit 132 whether or not there is a risk of developing a disease for each user, the acquisition unit 131 acquires behavior information of a non-risk user from the behavior information storage unit 121.

(About the estimation unit 132)
Among the users whose behavior information has been acquired by the acquisition unit 131, the estimation unit 132 performs gene analysis (for which a genetic test has been received) and the user whose gene information is known is the gene analysis result of the user. That is, based on the genetic information, it is estimated whether the disease has a risk of onset. When a particular gene is a particular gene type, disease risk information is known in advance that certain diseases are likely to develop.

  For example, it is assumed that, in the disease risk information, information indicating that the gene type TY1 is a cause of disease A (alcoholism) is included. In such a case, the estimation unit 132 compares the gene information with the disease risk information, and for example, if there is a user whose gene information indicates the gene type TY1 as described in FIG. It is estimated that there is a risk of developing

(About the analysis unit 134)
The analysis unit 134 analyzes the relationship (correlation) based on the tendency between the behavior information acquired by the acquisition unit 131 and the information on the disease corresponding to the risk user. For example, the analysis unit 134 performs correlation analysis to analyze the correlation between the behavior information of the risk user and the disease at risk for the risk user. For example, the analysis unit 134 analyzes the correlation between the behavior information and the disease for each combination of the disease and the behavior information of the risk user who is estimated to have the risk of developing the disease. Further, the analysis unit 134 associates the disease information (disease information) that has obtained an analysis result indicating that there is a correlation by correlation analysis with the behavior information, and stores the association in the tendency information storage unit 123.

  Here, based on the behavior information stored in the tendency information storage unit 123, that is, the behavior information having a correlation, and the behavior information corresponding to the unknown user in the behavior information storage unit 121, the analysis unit 134 determines whether the unknown user A process of specifying a target user who is a user having behavior information related to behavior information in correlation among them may also be performed.

  Describing using the example in FIG. 1, the analysis unit 134 is a target user who is a user having (for example, the same or similar) action information associated with action information corresponding to the correlation T among non-risk users Identify Specifically, the analysis unit 134 performs action information corresponding to the correlation T, that is, action information indicating "purchasing beer PD2-1", and action information indicating "purchasing sake Sake PD2-2". And a user having related behavior information as a target user. In the example of FIG. 1, the analysis unit 134 identifies the user U 52 as a target user.

(About the generation unit 135)
The generation unit 135 generates content. Specifically, the generation unit 135 generates content in which predetermined information based on a disease estimated to have the onset risk for the risk user is displayed. In addition, the generation unit 135 generates content in which predetermined information based on a disease is displayed as predetermined information based on a disease, which is information notified to a risk user or a target user.

  For example, the generation unit 135 generates, in response to a user operation, content notified in stages according to the user operation. For example, the generation unit 135 generates the first content, the second content, and the third content described in FIG. For example, the generation unit 135 generates first content in which content information indicating the content of a symptom corresponding to a disease is displayed. In addition, when the first content is selected, the generation unit 135 generates second content, which is content for causing the user to input a subjective symptom. Also, the generation unit 135 generates third content, which is content for which prediction information predicted based on input information input to the second content is displayed. For example, the generation unit 135 generates, as the third content, content in which the disease name of the disease predicted based on the input information is displayed, and which proposes the consultation at a medical institution corresponding to the disease. .

(About notification unit 136)
The notification unit 136 notifies a target user who is a user having action information associated with the action information acquired by the acquisition unit 131, with predetermined information based on a predetermined disease. Specifically, the notification unit 136 is a predetermined based on the disease at the onset risk of the acquisition source risk user for which the action information corresponding to (related to) the action information possessed by the object user is acquired for the object user. Notify of information. In the present embodiment, the expression “to notify” may include expressions such as “provide” or “distribute”.

  Further, the notification unit 136 is based on the tendency for the target user having action information related to the action information related to the disease and the tendency corresponding to the risk user among the action information acquired by the acquisition unit 131. The predetermined information based on the disease corresponding to the acquisition source risk user from which the relevant action information is acquired is notified. Further, the notification unit 136 notifies the risk user of predetermined information based on the disease corresponding to the risk user.

  Further, the notification unit 136 notifies, as the predetermined information based on the disease, the first content, which is the content in which the information on the disease is indicated, in a state where the user can not specify the disease name of the disease. In addition, when the first content is selected, the notification unit 136 notifies, as the predetermined information based on the disease, the second content which is a content for causing the user to input the subjective symptom. In addition, the notification unit 136 notifies, as the predetermined information based on the disease, the third content in which the predicted information predicted based on the input information input to the second content is displayed. For example, the notification unit notifies, as the third content, content in which the disease name of the disease predicted based on the input information is displayed, and proposes content that proposes consultation at a medical institution corresponding to the disease.

(Update unit 137)
Among the users who are estimated to have no risk of developing a predetermined disease based on the analysis result by genetic analysis, the updating unit 137 indicates that there is a symptom related to a disease based on the action information indicating the action of the user on the network. Information on the analysis result of the conscious user who is the user estimated to be aware is updated based on the symptom.

  In the example of FIG. 5, the estimation unit 132 estimates that the user U31 is a no-risk user (no-risk user U31) based on the gene information of the user U31. In such a state, the updating unit 137 estimates whether or not it is aware that there is a disease-related symptom based on the action information indicating the action of the no-risk user U31 on the network. For example, the estimation unit 132 estimates that a user who has performed an action such as “search for a symptom” is aware that there is a symptom.

  For example, it is assumed that a risk-free user searches information as a search keyword for the content of a symptom such as “sometimes you can see hallucinations”. In such a case, the updating unit 137 estimates that the no-risk user U31 is aware of a symptom such as “sometimes the illusion is visible”. Here, the updating unit 137 predicts what is a disease caused by a symptom such as “sometimes you can see a hallucination”. The updating unit 137 updates the analysis result of the no-risk user U31, for example, if it is predicted that the symptom such as “sometimes you can see a hallucination” is due to “alcoholism”. In the example of FIG. 5, the analysis result that the risk-free user U32 has no onset risk is obtained. However, based on the behavior information of the no-risk user U31, the updating unit 137 estimates that the no-risk user U31 is aware of a symptom such as "sometimes a visual sensation may be seen", so that the no-risk user U31 Disease is expected to develop.

  Since "the alcoholism has developed", the updating unit 137 states that the risk-free user U31 may actually have a gene type that is a cause of "the alcoholism". Predict. Therefore, in the example of FIG. 5, the updating unit 137 updates a place where “class of gene prediction disease” of the risk-free user U31 is blank at present as “A” indicating “alcoholism”, Update "User Type" to "Risk User".

  Note that the updating unit 137 updates the analysis result, for example, when the non-risk user has action information related to a disease predicted from the symptom estimated to be perceived by the non-risk user. It is also good. For example, since the updating unit 137 predicts that "no alcoholism" has occurred since the no-risk user U31 is aware of a symptom such as "sometimes a hallucination can be seen", the no-risk user U31 is further When there is action information related to “alcoholism” such as “purchase liquor”, the non-risk user U31 predicts that “alcoholism” is developed. Then, the updating unit 137 updates the analysis result as described above.

  As described above, when it is estimated that the non-risk user is aware that there is a symptom related to a disease based on the action information of the non-risk user, the information processing apparatus 100 according to the embodiment is based on this symptom. Update the analysis results of risk-free users.

  Thereby, the information processing apparatus 100 according to the embodiment can compare the gene information (gene type) indicated by the gene test with the gene type corresponding to the disease predicted from the user's action information. Whether the analysis result of the inspection is correct or not can be determined with high accuracy.

[4. Processing procedure]
Next, a procedure of information processing performed by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. In FIG. 9, among information processing performed by the information processing apparatus 100 according to the embodiment, a processing procedure up to analysis processing for analyzing a correlation will be described. FIG. 9 is a flowchart of the analysis procedure of the information processing apparatus 100 according to the embodiment. Note that FIG. 9 will be described using the example of FIG. 1 as appropriate.

  First, the acquisition unit 131 acquires action information indicating an action of each user on the Internet (step S101). For example, the acquisition unit 131 acquires behavior information from an external server apparatus that cooperates with the information processing apparatus 100. For example, the acquisition unit 131 acquires purchase information, search information, and browsing information as action information. Note that the acquisition unit 131 may acquire any kind of action information as long as the action information indicates the action of each user on the Internet, and is not limited to purchase information, search information, and browse information.

  Next, among the users whose behavior information has been acquired by the acquisition unit 131, the estimation unit 132 performs gene analysis (tests on a genetic test), and for users whose gene information is known, the user Based on the result of the gene analysis of, that is, based on the gene information, it is estimated whether or not there is a risk of developing a disease (step S102). For example, as described in FIG. 1, assuming that the gene information of the users U11 to U13 indicates the gene type TY1 as in the estimation unit 132, the disease causes the user U11 to U13 to become the onset factor of the gene type TY1 (alcohol dependent It is estimated that there is a risk of developing

  In such a state, the estimation unit 132 classifies the risk user, the non-risk user, and the unknown user for each disease based on the genetic locus information, and stores it in the storage unit according to the classification (step 103). For example, as illustrated in FIG. 5, the estimation unit 132 classifies users into risk users, risk-free users, and unknown users, associates disease and behavior information for each user included in each classification, and performs behavior information. It is stored in the storage unit 121.

  Next, the analysis unit 134 analyzes the relationship (correlation) based on the tendency with the information related to the disease corresponding to the risk user based on the risk user behavior information (step S104). For example, the analysis unit 134 analyzes the correlation between the behavior information and the disease for each combination of the disease and the behavior information of the risk user who is estimated to have the risk of developing the disease. For example, it is assumed that the analysis unit 134 obtains the analysis result that the users U11 to U13 who are at risk of developing the disease A tend to purchase the beer PD2-1 and the sake PD2-2 by the correlation analysis. The analysis results show that there is a correlation ("Correlation T" between "purchasing beer PD2-1" and "purchasing sake PD2-2") and disease A (or a user who is at risk of developing disease A). To indicate that the

  In addition, since both the beer PD2-1 and the sake PD2-2 are alcoholic beverages, the analysis unit 134 tends to purchase the beer PD2-1 and the sake PD2-2 by the users U11 to U13 who are at risk of developing the disease A. As a result of the analysis that the user U11 to U13 are at risk of developing the disease A, it is also possible to obtain an analysis result that the users U11 to U13 tend to purchase an alcoholic drink or liquor.

  Further, the analysis unit 134 detects a combination of behavior information and a disease having a correlation, and associates and stores behavior information included in the detected combination and content information indicating a disease (Step S105). In the case of the above example, the analysis unit 134 detects that there is a correlation between "purchasing beer PD2-1" and "purchasing sake PD2-2" and the disease A, this behavior information And the content information are stored in the tendency information storage unit 123 in association with each other.

[5. Processing procedure]
Next, the procedure of the information processing performed by the information processing apparatus 100 according to the embodiment will be described using FIG. In FIG. 10, a processing procedure of notification processing after analysis processing in the information processing executed by the information processing apparatus 100 according to the embodiment will be described. FIG. 10 is a flowchart of a process procedure of the notification process performed by the information processing apparatus 100 according to the embodiment. Note that FIG. 10 will be described using the example of FIG. 1 as appropriate. Note that the information processing shown in FIG. 10 may be performed at another timing than the information processing shown in FIG.

  For example, based on the behavior information stored in the tendency information storage unit 123, that is, the correlated behavior information stored in the tendency information storage unit 123 and the behavior information corresponding to the unknown user in the behavior information storage unit 121, the analysis unit 134 From the above, the target user who is the user having the action information related to the action information in correlation is specified (step S201).

  Describing using the example in FIG. 1, the analysis unit 134 is a target user who is a user having (for example, the same or similar) action information associated with action information corresponding to the correlation T among non-risk users Identify Specifically, the analysis unit 134 performs action information corresponding to the correlation T, that is, action information indicating "purchasing beer PD2-1", and action information indicating "purchasing sake Sake PD2-2". And a user having related behavior information as a target user. In the example of FIG. 1, the analysis unit 134 identifies the user U 52 as a target user.

  When the target user is identified, the notification unit 136 notifies the first content as the information based on the disease corresponding to the risk user of the acquisition source from which the action information related to the action information of the target user is acquired. (Step S202). For example, when the notification unit 136 receives a distribution request for the first content from the terminal device 10, the notification unit 136 notifies the first content. The target user U52 and FIG. 2 will be described as an example. When the notification unit 136 receives, from the terminal device 10 of the target user U52, a distribution request for content to be displayed in the display frame F1 included in the portal site P1, the notification unit 136 instructs the generation unit 135 to generate the first content.

  The generation unit 135 generates the first content in response to the instruction from the notification unit 136. For example, the generation unit 135 generates content C1 (an example of the first content) in which content information indicating the content of the symptom related to the disease A that the target user U 52 may develop is displayed. At this time, the generation unit 135 generates the content C1 not including the disease name of the disease A. That is, the generation unit 135 generates the content C1 in which the target user U52 can not specify the disease name.

  The notification unit 136 distributes the content C1 generated by the generation unit 135 to the terminal device 10. Thereby, the terminal device 10 displays the portal site P1 such that the content C1 including the content information is displayed in the display frame F1, as shown in FIG. 2A.

  Further, the notification unit 136 determines whether the first content is selected (step S203). When the first content is not selected (step S203; No), the notification unit 136 stands by until the first content is selected. On the other hand, when the first content is selected (step S203; Yes), the notification unit 136 notifies the second content, which is a content for causing the user to input the subjective symptom (step S204).

  For example, when detecting that the content C1 has been selected (for example, tapped or clicked) by the target user U52, the notification unit 136 instructs the generation unit 135 to generate the second content. For example, the generation unit 135 generates the content C2 (an example of the second content) for causing the target user U52 to input a certain symptom of the heart. For example, the generation unit 135 generates content C2 for selecting content information corresponding to a symptom with a heart from content information indicating the content of a typical symptom of a disease predicted from the symptom A.

  The notification unit 136 distributes the content C2 generated by the generation unit 135 to the terminal device 10. Thereby, the terminal device 10 displays the content C2 as shown in FIG. 2 (b) as shown in FIG. 2 (b).

  In such a state, the notification unit 136 determines whether the second content is answered (step S205). If the second content is not answered (step S205; No), the notification unit 136 stands by until a response is made. On the other hand, when the notification unit 136 responds with the second content (step S205; Yes), the third content is the content for which the prediction information predicted based on the input information input to the second content is displayed (Step S206).

  For example, it is assumed that the target user U52 selects the content information as shown in FIG. 2B and presses the "next" button. In such a case, the notification unit 136 receives the input information input (selected) by the target user U52 in the content C2. Then, the notification unit 136 transmits the received input information to the prediction unit 133. Then, on the basis of the input information, the prediction unit 133 finally predicts a disease that the target user U52 is afflicted / or possibly afflicted. Here, it is assumed that the prediction unit 133 predicts that the target user U 52 may suffer from the disease A. In addition, it is assumed that the prediction unit 133 predicts that the symptom related to the disease A appears in the target user U52 in the future. In such a case, the prediction unit 133 transmits the predicted information to the generation unit 135.

  Then, the generation unit 135 generates the content C3 (an example of the third content) in which the information on the disease A is displayed. For example, the generation unit 135 generates the content C3 in which proposal information that proposes to visit a specialized medical institution of “alcoholism” is displayed together with the disease name “alcoholism” of the disease A.

  The communication unit 136 distributes the content C3 generated by the generation unit 135 to the terminal device 10. Thereby, the terminal device 10 displays the content C3 as shown in FIG. 2 (c).

  Although not shown, the information processing apparatus 100 displays, for example, the location information of the target user U52 when the button indicating “Guide to a medical institution from here” is pressed in FIG. 2C. A medical institution having a medical department corresponding to the disease A is searched based on (for example, the current address or the current position). For example, the information processing apparatus 100 searches for a medical institution closer to the location of the target user U52. Then, the information processing apparatus 100 distributes the content including the searched search result to the terminal device 10.

[6. Modified example]
The information processing apparatus 100 according to the embodiment may be implemented in various different forms other than the above example. Therefore, another embodiment will be described below.

[6-1. Information notification to risk users]
In the above embodiment, an example in which the information processing apparatus 100 notifies the target user of information has been described. However, the information processing apparatus 100 may also notify information to the risk user. For example, the notification unit 136 notifies the risk user of predetermined information based on a disease at risk of developing the risk user.

  The notification unit 136 can perform the same information notification as the information notification to the target user U52 described with reference to FIG. 2 also to the risk users U11 to U13. It is estimated that the risk users U11 to U13 have a risk of developing the disease A. Therefore, as described in FIG. 2, the notification unit 136 uses, as information based on the disease A, the content C1 (first content), the content C (second content), and the content C3 (third content) as the risk user U11. ~ U13 notify in stages, depending on its operation.

  As a result, the information processing apparatus 100 according to the embodiment can recognize that there is a possibility of developing a disease, and thus can contribute to early detection and early treatment of a disease.

6-2. Another Example of Information Notification (1)]
In the above embodiment, as shown in FIG. 2, the notification unit 136 has shown an example in which the first content to the third content are notified in stages. However, the notification unit 136 may notify the fourth content and the fifth content in order.

  The fourth content is content in which a disease name is displayed, and is content displayed in a predetermined page in a manner such as advertisement content (for example, banner advertisement). The fifth content is content that proposes a medical examination at a medical institution corresponding to a disease when the fourth content is selected. The target user U52 will be described as an example.

  When the notification unit 136 receives, from the terminal device 10 of the risk user U52, a distribution request for content to be displayed in the display frame F1 included in the portal site P1, the notification unit 136 instructs the generation unit 135 to generate the fourth content.

  The generation unit 135 generates the fourth content in accordance with the instruction from the notification unit 136. For example, the generation unit 135 generates a content C4 (an example of a fourth content) in which content information of a symptom represented by the disease A and the disease A corresponding to the risk user U52 is displayed.

  The notification unit 136 distributes the content C4 generated by the generation unit 135 to the terminal device 10. Thereby, the terminal device 10 displays the portal site P1 so that the content C4 is displayed in the display frame F1.

  In such a state, when the fourth content is selected, the notification unit 136 notifies the fifth content, which is content that proposes a medical examination at a medical institution corresponding to the disease. For example, it is assumed that the risk user U52 selects the content C4. In such a case, the notification unit 136 instructs the generation unit 135 to generate the content 5 (an example of the fifth content). The generation unit 135 generates content C5 that proposes consultation at a medical institution corresponding to the disease A corresponding to the risk user U52.

  The notification unit 136 distributes the content C5 generated by the generation unit 135 to the terminal device 10. Thereby, the terminal device 10 displays the content C5. The content C4 is a content corresponding to the content C1 shown in FIG. 2, but is different from the content C1 in that the disease name is displayed. The content C5 is a content corresponding to the content C3.

6-3. Another example of information value position (2)]
In the above embodiment, an example has been shown in which the notification unit 136 notifies the first content in a state in which the user can not identify the disease name of the disease caused by the symptom. However, the notification unit 136 may notify the name of the disease caused by the condition in a state where the user can specify the disease. Specifically, the notification unit 136 may notify, as the first content, content in which a disease name of a disease caused by a symptom is displayed.

  In this case, the notification unit 136 notifies the user of the disease name in the form of a question asking the user about the presence or absence of the disease rather than notifying the disease name in such a manner as to determine that the user suffers from the disease. It is preferable to do. Using the previous example, the notification unit 136 displays the disease name “alcoholism” in a question form in the form “Do you know alcoholism?” For the target user U52. The first content may be notified. The notification unit 136 may notify the fourth content in the same format.

  For example, depending on how the disease name is displayed, there are users who seem to be declared the disease name and do not feel comfortable with this. In such a case, with the above question format, the user does not receive an impression that he / she is informed of the name of the disease. As such, the information processing apparatus 100 according to the embodiment can notify the user of the contents without deteriorating the feeling to the user and without raising anxiety.

[7. Hardware configuration]
The information processing apparatus 100 according to the embodiment described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 11 is a hardware configuration diagram showing an example of a computer 1000 for realizing the functions of the information processing apparatus 100. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 to control each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from another device via the communication network 50, sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to the other device via the communication network 50.

  The CPU 1100 controls an output device such as a display or a printer and an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 acquires data from an input device via the input / output interface 1600. The CPU 1100 also outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the CPU 1100 with the program via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a digital versatile disc (DVD) or a phase change rewritable disc (PD), a magneto-optical recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the information processing apparatus 100, the CPU 1100 of the computer 1000 realizes the function of the control unit 130 by executing a program loaded on the RAM 1200. Further, data in the storage unit 120 is stored in the HDD 1400. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from the other device via the communication network 50.

[8. Other]
Further, among the processes described in the above embodiment, all or part of the process described as being automatically performed may be manually performed, or the process described as being manually performed. All or part of them can be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above-mentioned documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

  Further, each component of each device illustrated is functionally conceptual, and does not necessarily have to be physically configured as illustrated. That is, the specific form of the distribution and integration of each device is not limited to the illustrated one, and all or a part thereof may be functionally or physically dispersed in any unit depending on various loads, usage conditions, etc. It can be integrated and configured.

  Moreover, it is possible to combine suitably each embodiment mentioned above in the range which does not contradict process content.

[9. effect〕
The information processing apparatus 100 according to the embodiment includes an acquisition unit 131 and a notification unit 136. The acquisition unit 131 acquires behavior information indicating behavior on a network of a risk user who is a user who is estimated to have a risk of developing a predetermined disease based on an analysis result by gene analysis. The notification unit 136 notifies a target user who is a user having action information associated with the action information acquired by the acquisition unit 131, with predetermined information based on a predetermined disease.

  Thereby, the information processing apparatus 100 according to the embodiment can appropriately provide the information on the disease to the user whose information on the gene is unknown.

  In addition, the acquisition unit 131 may use, as the action information, search information that is information on the search action of the risk user, purchase information that is information on the purchase action of the risk user, browsing information that is information on the view action of the risk user, or the risk The action information indicated by the content of the user answering the questionnaire is acquired.

  As a result, the information processing apparatus 100 according to the embodiment can acquire various types of action information, and therefore can analyze with high accuracy what action the risk user tends to take.

  Further, the information processing apparatus 100 according to the embodiment includes an analysis unit 134. The analysis unit 134 analyzes the relationship based on the tendency between the behavior information acquired by the acquisition unit 131 and the information on the disease corresponding to the risk user. Further, the notification unit 136 is based on the tendency for the target user having action information related to the action information related to the disease and the tendency corresponding to the risk user among the action information acquired by the acquisition unit 131. The predetermined information based on the disease corresponding to the acquisition source risk user from which the relevant action information is acquired is notified.

  Thus, the information processing apparatus 100 according to the embodiment can specify with high accuracy a user who is highly likely to develop a disease caused by a gene type even if the genetic information is unknown. Can effectively recognize the possibility of developing a disease. Further, as a result, since the information processing apparatus 100 can contribute to, for example, early detection of a disease, it can contribute to the comfortable daily life of the user.

  The notification unit 131 also notifies the risk user of predetermined information based on the disease corresponding to the risk user.

  Thereby, the information processing apparatus 100 according to the embodiment can effectively cause the risk user to recognize that there is a possibility of developing a disease.

  Further, the notification unit 136 notifies, as the predetermined information, the first content, which is the content in which the information on the disease is indicated, in a state where the user can not specify the disease name of the disease.

  Thereby, the information processing apparatus 100 according to the embodiment can enhance the appeal of the first content.

  In addition, when the first content is selected, the notification unit 136 notifies, as the predetermined information, the second content which is a content for causing the user to input the subjective symptom.

  As a result, the information processing apparatus 100 according to the embodiment can directly obtain information on symptoms from the user side, and therefore can predict a possible disease of the user with high accuracy.

  In addition, the notification unit 136 notifies, as the predetermined information, the third content in which the prediction information predicted based on the input information input to the second content is displayed.

  Thereby, the information processing apparatus 100 according to the embodiment can effectively cause the user to recognize that there is a possibility of a disease.

  In addition, the notification unit 136 notifies, as the third content, content in which the disease name of the disease predicted based on the input information is displayed, and proposes content that proposes consultation at a medical institution corresponding to the disease.

  Thus, the information processing apparatus 100 according to the embodiment can contribute to early detection of a disease, and thus can contribute to a user's comfortable daily life.

  Further, the information processing apparatus 100 according to the embodiment includes the updating unit 137. Among the users who are estimated to have no risk of developing a predetermined disease based on the analysis result by genetic analysis, the updating unit 137 indicates that there is a symptom related to a disease based on the action information indicating the action of the user on the network. Information on an analysis result of a conscious user who is a user estimated to be aware is updated based on the symptom.

  Thereby, the information processing apparatus 100 according to the embodiment can compare the gene information (gene type) indicated by the gene test with the gene type corresponding to the disease predicted from the user's action information. Whether the analysis result of the inspection is correct or not can be determined with high accuracy.

  Although some of the embodiments of the present application have been described above in detail based on the drawings, these are only examples, and various modifications can be made based on the knowledge of those skilled in the art, including the aspects described in the section of the description of the invention. It is possible to implement the invention in other improved forms.

  In addition, the "section (module, unit)" described above can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition unit or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 information processing system 10 terminal device 30 examination organization server 60 external server 100 information processing device 121 behavior information storage unit 121 disease information storage unit 121 tendency information storage unit 121 content information storage unit 131 acquisition unit 132 estimation unit 133 prediction unit 134 analysis unit 135 generation unit 136 notification unit 136 update unit

Claims (11)

An acquisition unit for acquiring action information indicating an action on a network of a risk user who is a user estimated to have a risk of developing a predetermined disease based on an analysis result by genetic analysis;
It is a user who has action information related to the action information acquired by the acquisition unit, and the user who is the user whose gene analysis has not been performed and whose gene information is unknown based on the analysis result is the predetermined user. have a notification unit that notifies the predetermined information based on the disease,
The notification unit notifies the target user of, as the predetermined information, first content which is content in which the content of symptoms related to the predetermined disease is displayed, and the first content is selected by the target user. Content that is notified of the second content, which is content for causing the target user to input a subjective symptom, and displayed a disease name of a disease predicted based on the input information input to the second content An information processing apparatus characterized by notifying third content which is content suggesting a medical examination at a medical institution corresponding to the disease . The acquisition unit may, as the action information, search information that is information related to a search action of the risk user, purchase information that is information related to a purchase action of the risk user, browsing information that is information related to a browsing action of the risk user, The information processing apparatus according to claim 1, further comprising: action information indicated by contents of the risk user answering a questionnaire. The information processing apparatus further includes an analysis unit that analyzes a relationship based on a tendency between the behavior information acquired by the acquisition unit and information on a disease corresponding to the risk user.
The said notification part is the said tendency with respect to the target user who has action information related to the disease corresponding to the said risk user and the action information related to the said tendency among the action information acquired by the said acquisition part The information processing apparatus according to claim 1 or 2, wherein predetermined information based on the disease corresponding to the acquisition source risk user from which the action information in the based relationship is acquired is notified. The information processing apparatus according to any one of claims 1 to 3, wherein the notification unit notifies the risk user of predetermined information based on a disease corresponding to the risk user. The information processing apparatus according to claim 4 , wherein the notification unit notifies, as the predetermined information, first content which is content in which information on the disease is indicated. The said notification part notifies, when the said 1st content is selected, the 2nd content which is a content for making a user input about a subjective symptom as said predetermined | prescribed information. Information processing equipment. The information according to claim 6, wherein the notification unit notifies, as the predetermined information, a third content in which prediction information predicted based on input information input to the second content is displayed. Processing unit. The notification unit is configured to notify, as the third content, content in which a disease name of a disease predicted based on the input information is displayed, and which proposes a consultation at a medical institution corresponding to the disease. The information processing apparatus according to claim 7, characterized in that Among users who are presumed to have no risk of developing a predetermined disease based on analysis results by genetic analysis, they are aware that they have symptoms related to illness based on behavioral information indicating their behavior on the network The information processing apparatus according to any one of claims 1 to 8, further comprising: an update unit configured to update, based on the symptom, information on the analysis result of the conscious user who is the estimated user. An information processing method executed by a computer,
An acquisition step of acquiring behavior information indicating behavior on a network of a risk user who is a user estimated to have a risk of developing a predetermined disease based on an analysis result by genetic analysis;
It is a user who has action information related to the action information acquired by the acquisition step, and is the user who is the user whose gene analysis has not been performed and whose gene information is unknown based on the analysis result. look including a notifying step for notifying predetermined information based on the disease,
The notification step notifies the target user of the first content which is the content on which the content of the symptom related to the predetermined disease is displayed as the predetermined information, and the first content is selected by the target user Content that is notified of the second content, which is content for causing the target user to input a subjective symptom, and displayed a disease name of a disease predicted based on the input information input to the second content An information processing method comprising: notifying third content which is content suggesting a medical examination at a medical institution corresponding to the disease . An acquisition procedure for acquiring behavior information indicating behavior on a network of a risk user who is a user estimated to have a risk of developing a predetermined disease based on an analysis result by genetic analysis;
For a target user who is a user who has action information related to action information acquired by the acquisition procedure and is a user whose gene analysis has not been performed and whose gene information is unknown based on the analysis result. to execute a notification procedure for notifying predetermined information based on the disease to the computer,
The notification procedure notifies the target user of the first content, which is content in which the content of the symptom related to the predetermined disease is displayed, as the predetermined information, and the first content is selected by the target user. Content that is notified of the second content, which is content for causing the target user to input a subjective symptom, and displayed a disease name of a disease predicted based on the input information input to the second content And an information processing program for notifying of third content which is content for proposing a consultation at a medical institution corresponding to the disease .

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