Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device capable of detecting differences in contexts of many users, an information processing method, and an information processing program.SOLUTION: An information processing device comprises an acquisition unit, a first extraction unit, an estimation unit, and a detection unit. The acquisition unit acquires action information showing information on an action of a user and sensor information showing measurement data of a sensor unit of a user terminal. The first extraction unit extracts a first group showing a group of users whose actions at a prescribed time are common on the basis of at least the action information or the sensor information. The estimation unit estimates contexts of users included in the first group extracted by the first extraction unit on the basis of at least the action information of the users or the sensor information. The detection unit detects differences in the contexts of the users estimated by the estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] There are known techniques for estimating a user's context. For example, Patent Document 1 below discloses a program, device, and method for estimating a context representing a person's behavior from a captured video. However, it has not been possible to detect differences in the contexts of a large number of users. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-96252 Summary of the Invention [Problem to be solved by the invention]

[0004] In view of the above-described problems, the present disclosure aims to provide an information processing device, an information processing method, and an information processing program that are capable of detecting differences in the contexts of multiple users. [Means for solving the problem]

[0005] In order to solve the above-described problems and achieve the object, an information processing device according to the present disclosure includes: behavior information indicating information related to a user's behavior; The user carries it when engaging in the activity Sensor information indicating measurement data from the sensor unit installed in the user terminal and an acquisition unit that acquires the before Behavioral Information and The sensor information and a first extraction unit that extracts a first group indicating a group of users who have a common behavior during a predetermined period based on the above; Based on the relationship between behavioral information, sensor information, and user context, Included in the first group extracted by the first extraction unit Based on the behavioral information and the sensor information of the user, User Context Recommendedan estimation unit that determines the Included in the first group User Context A distribution unit that distributes content to users based on the and equipped with The estimation unit estimates, as one of the contexts of the users included in the first group, whether the users included in the first group use a predetermined object during a common action based on image information as the sensor information of the users included in the first group, and the distribution unit distributes content related to the predetermined object to users included in the first group who do not use the predetermined object during the common action. . [Effects of the Invention]

[0006] According to the present disclosure, it is possible to provide an information processing device, an information processing method, and an information processing program that are capable of detecting differences in the contexts of multiple users. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of information stored in a behavior information storage unit according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of information stored in a sensor information storage unit according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of information stored in a model storage unit according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of information stored in a content storage unit according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of the configuration of a user terminal according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of information stored in a sensor information storage unit according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the configuration of a carrier terminal according to the embodiment. [Figure 11] FIG. 11 is a flowchart illustrating an example of information processing according to the embodiment. [Figure 12] FIG. 12 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, modes for implementing an information processing device, an information processing method, and an information processing program according to the present disclosure (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present disclosure are not limited to these embodiments.

[0009] (Embodiment) 1-1. Example of information processing according to the embodiment First, an example of information processing according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of information processing according to the embodiment. Fig. 1 shows an example in which information processing according to the embodiment is executed by an information processing device 100, a user terminal 200, and a provider terminal 300.

[0010] First, the information processing device 100 receives content input by a business operator from the business operator terminal 300 (step S1). For example, the information processing device 100 receives content input from the business operator terminal 300 by the business operator M1 for the purpose of distribution to a user for whom a specific context has been estimated. Note that the content here refers to the content of information that the business operator intends to distribute to users, and may be, for example, advertisements, movies, music, photos, comics, animations, computer games, text messages, etc.

[0011] Next, the information processing device 100 acquires behavioral information and sensor information from multiple user terminals 200 (step S2). For example, as shown in Fig. 1, the information processing device 100 acquires behavioral information and sensor information from user terminals 200A to 200F of users U1 to U6. Note that users U1 to U6 and user terminals 200A to 200F shown in Fig. 1 are merely examples, and the information processing device 100 may acquire behavioral information and sensor information from user terminals 200 of more users.

[0012] Next, the information processing device 100 extracts a first group indicating a group of users who have a common behavior during a predetermined period of time, based on at least one of the behavior information or the sensor information (step S3). For example, based on location information and acceleration information as sensor information of the users, the information processing device 100 may extract, as the first group indicating a group of users who have a common behavior during a predetermined period of time, users who are located within a predetermined range (for example, a range within a radius of 100 m) during a predetermined period of time and whose accelerations are maintained within a predetermined range of values ​​over a predetermined period of time, as "running a marathon."

[0013] Next, the information processing device 100 estimates the context of the users included in the first group based on at least one of the behavioral information and sensor information of the users (step S4). For example, the information processing device 100 estimates the context that a user who is running a marathon and who has been extracted as the first group is "wearing running shoes of manufacturer N" based on image information as sensor information. Note that the information processing device 100 estimates whether or not all users extracted as users belonging to the first group fall into the context of "wearing running shoes of manufacturer N" based on image information as sensor information.

[0014] Next, the information processing device 100 distributes content based on the estimated context (step S5). For example, among the users belonging to the first group, content related to running shoes of manufacturer N is distributed to users who are estimated not to fit the context "wearing running shoes of manufacturer N."

[0015] This makes it possible to deliver content according to the differences in the contexts of a large number of users.

[0016] 1-2. Another Example 1 of Information Processing According to Embodiment (Detecting Differences in Context) The information processing device 100 extracts a second group representing a group of users among the users included in the extracted first group, the group including users whose estimated context matches the estimated context and accounts for a predetermined percentage or more of the users included in the first group, and delivers content to users included in the first group who are not included in the second group, according to the difference in context between the first group and the second group.

[0017] This information processing will be explained step by step. First, the information processing device 100 executes the same processes as steps S1 to S4 shown in FIG.

[0018] Next, the information processing device 100 extracts a second group representing a group of users whose estimated context matches the estimated context of the extracted first group, where the number of users whose estimated context matches the estimated context of the first group accounts for a predetermined percentage or more of the users in the first group (step S5-1). For example, as in step S3 described above, in this information processing, the information processing device 100 extracts users who are deemed to be "running a marathon" based on the user's location information and acceleration information in step S3 as users belonging to the first group. Then, the information processing device 100 estimates the user's context based on the search history, purchase history, etc., which are behavioral information of the users who are deemed to be "running a marathon," and estimates the context "I like manufacturer N" for a predetermined percentage or more of the users belonging to the first group. In this case, the information processing device 100 extracts users whose estimated context "I like manufacturer N" is estimated as users belonging to the second group.

[0019] Next, the information processing device 100 distributes content to users who are included in the first group but are not included in the second group, according to the difference in context between the first group and the second group (step S6-1). For example, as described above, it is assumed that the information processing device 100 extracts users whose context "I like manufacturer N" is estimated as users who belong to the second group in step S5-1. In this case, the information processing device 100 distributes content related to manufacturer N to users who belong to the first group, which is a group of users who are considered to be "running a marathon," but do not belong to the second group.

[0020] This makes it possible to detect differences in the contexts of many users and deliver content according to the differences in the contexts.

[0021] 1-3. Another Example 2 of Information Processing According to an Embodiment (Evaluating Common Behavioral Results) The information processing device 100 evaluates the results of common behavior for users included in the first group, detects the difference in context between users whose evaluated results of common behavior are below a predetermined value and users whose results of common behavior are above the predetermined value, and delivers content to users whose results of common behavior are below the predetermined value according to the detected difference in context.

[0022] This information processing will be explained step by step. First, the information processing device 100 executes the same processes as steps S1 to S4 shown in FIG.

[0023] Next, the information processing device 100 evaluates the results of the common behavior of the users included in the first group (step S5-2). For example, as in step S3 described above, in this information processing, the information processing device 100 extracts users who are deemed to be "running a marathon" based on the user's location information and acceleration information in step S3 as users belonging to the first group, which indicates a group of users who share common behavior during a predetermined period. In this case, the information processing device 100 estimates the ranking of the user deemed to be "running a marathon" in the marathon based on the user's location information, acceleration information, etc. Then, the information processing device 100 evaluates the results of the common behavior of the users based on the estimated ranking of the user. For example, a user who ranks first in the marathon may be given an evaluation value of 100 points, and a user who ranks second in the marathon may be given an evaluation value of 80 points.

[0024] Next, the information processing device 100 detects a difference in context between users whose evaluated common behavioral outcomes are below a predetermined value and users whose evaluated common behavioral outcomes are above a predetermined value (step S6-2). For example, the information processing device 100 compares the context of users whose evaluation values ​​for the common behavioral outcomes based on the rankings in the marathon estimated in the above-mentioned step S5-2 are 50 points or more with the context of users whose evaluation values ​​are less than 50 points. The information processing device 100 then detects a difference in context between users whose evaluation values ​​are 50 points or more and users whose evaluation values ​​are less than 50 points. For example, it is assumed that the proportion of users whose evaluation values ​​are 50 points or more whose context is estimated as "wearing running shoes from manufacturer N" is significantly higher than the proportion of users whose evaluation values ​​are less than 50 points whose context is estimated as "wearing running shoes from manufacturer N." In this case, the information processing device 100 detects the context "wearing running shoes from manufacturer N" as a difference in context between users whose evaluation values ​​are below a predetermined value and users whose common behavioral outcomes are above a predetermined value.

[0025] Next, the information processing device 100 distributes content to users whose common behavioral outcomes are below a predetermined value, according to the detected context difference (step S7-2). For example, in the above-mentioned step S6-2, the information processing device 100 detects the context "wearing running shoes from manufacturer N" as the difference between the contexts of users whose common behavioral outcomes are below a predetermined value and users whose common behavioral outcomes are above a predetermined value. In this case, the information processing device 100 distributes content related to running shoes from manufacturer N to users whose common behavioral outcomes have an evaluation value of 50 points or less.

[0026] This makes it possible to detect differences in the contexts of many users and deliver content according to the differences in the contexts.

[0027] [2. Information Processing System Configuration] Next, the configuration of the information processing system according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing system according to the embodiment.

[0028] The information processing device 100 may be, for example, a personal computer (PC), a workstation (WS), a computer with server functions, etc. The information processing device 100 performs processing based on information transmitted from a user terminal 200 or a business terminal 300 via a network N.

[0029] The user terminal 200 is an information processing device used by a user. The user terminal 200 may be, for example, an information processing device such as a smartphone, a tablet terminal, a desktop PC, a notebook PC, a mobile phone, or a PDA (Personal Digital Assistant). In the example shown in FIG. 1, the user terminal 200 is a smartphone.

[0030] The business operator terminal 300 is an information processing device used by a business operator. The business operator terminal 300 may be an information processing device such as a PC, a WS, or a computer equipped with server functions. For example, the business operator terminal 300 performs processing based on information transmitted from the information processing device 100 or the user terminal 200 via the network N.

[0031] 3. Configuration of Information Processing Device Next, the configuration of the information processing device 100 will be described with reference to FIG.

[0032] 3 is a diagram showing an example of the configuration of an information processing device according to an embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit .

[0033] (Regarding the communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), a wireless local area network (LAN) card, etc. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the user terminal 200, the operator terminal 300, etc.

[0034] (Regarding the storage unit 120) The storage unit 120 includes a main storage device and an external storage device. The main storage device stores programs executed by the control unit 130 or data processed by the control unit 130. The main storage device may be realized by, for example, semiconductor memory elements such as RAM (Random Access Memory), ROM (Read Only Memory), or flash memory. The external storage device saves data processed by the control unit 130. The external storage device may be realized by, for example, a hard disk, SSD (Solid State Drive), magnetic tape, optical disk, or the like.

[0035] As shown in FIG. 3, the storage unit 120 includes a behavior information storage unit 121, a sensor information storage unit 122, a model storage unit 123, and a content storage unit .

[0036] (Regarding the behavioral information storage unit 121) The behavioral information storage unit 121 stores information indicating the behavior of a user, i.e., behavioral information. The behavioral information is information indicating the user's use of a predetermined information service or the user's behavior resulting from the user's behavior. Here, an example of information stored in the behavioral information storage unit 121 will be described with reference to FIG. 4. FIG. 4 is a diagram showing an example of information stored in the behavioral information storage unit according to the embodiment.

[0037] In the example shown in FIG. 4, the behavior information storage unit 121 stores information relating to the items "user ID," "search history," "browsing history," "purchase history," "route search history," and "bulletin board posting history" in association with each other.

[0038] "User ID" is an identifier that identifies a user and is represented by a string of characters, a number, etc. "Search history" is information that includes the search query used by a user linked to a "User ID" for a search and the time of entry. "Browsing history" is information that includes the sites viewed by a user linked to a "User ID" and the time of viewing. "Purchase history" is information that includes the products or services purchased by a user linked to a "User ID" on an Internet mail order site or a specified information and communication service contract site, etc., and the time of purchase. "Route search history" is information that includes the route search results and the search time of a user linked to a "User ID". "Bulletin board posting history" is information that includes the bulletin board posts by a user linked to a "User ID" and the time of posting.

[0039] That is, in Figure 4, the search history of the user identified by the user ID "UID#1" is "Search History #U1", the user's browsing history is "Browse History #U1", the purchase history is "Purchase History #U1", the route search history is "Route Search History #U1", and the message board posting history is "Message board posting history #U1".

[0040] The information stored in the behavioral information storage unit 121 is not limited to information relating to the items "user ID," "search history," "browsing history," "purchase history," "route search history," and "bulletin board posting history," but may also store any other information relating to the user's behavior.

[0041] (Regarding the sensor information storage unit 122) The sensor information storage unit 122 stores, for each user, the sensor information measured by the sensor unit 250 included in the user terminal 200. Fig. 5 is a diagram illustrating an example of information stored in the sensor information storage unit according to the embodiment.

[0042] In the example shown in FIG. 5, the sensor information storage unit 122 stores information relating to the items "user ID," "user terminal ID," "sensor type," and "sensor information" in association with each other.

[0043] "User ID" is an identifier that identifies a user and is represented by a character string, a number, etc. "User terminal ID" is an identifier that identifies the user terminal 200 owned by the user identified by the user ID and is represented by a character string, a number, etc. "Sensor type" is information that indicates the type of sensor of the sensor unit 250 provided in the user terminal 200. "Sensor information" is information that indicates the measurement data measured by the sensor indicated in "sensor type".

[0044] That is, in Figure 5, the user terminal ID of the user identified by the user ID "UID#1" is "200A", and measurement data indicated by sensor information "SD#U1-1-1" measured by a sensor of the sensor type indicated by the sensor type "ST#1" of the sensor unit 250 provided in the user terminal 200 is stored.

[0045] The sensor information storage unit 122 is not limited to information relating to the items "user ID," "user terminal ID," "sensor type," and "sensor information," and may store information relating to any other sensor.

[0046] (Regarding the model storage unit 123) The model storage unit 123 stores a model for inferring the context of a user when behavior information and sensor information are input. Fig. 6 is a diagram illustrating an example of information stored in the model storage unit according to the embodiment.

[0047] In the example shown in FIG. 6, the model storage unit 123 stores information relating to the items "model ID" and "model data" in association with each other.

[0048] "Model ID" is an identifier that identifies the machine learning model and is represented by a string or number. "Model data" indicates the model data of the machine learning model. For example, "model data" stores data for a model that infers the user's context when user behavior information and sensor information are input. The machine learning model may be a neural network, etc.

[0049] That is, in FIG. 6, the model identified by the model ID "M#1" indicates the machine learning model M#1, and the model data "MDT#1" indicates the model data of the machine learning model M#1.

[0050] Here, if the machine learning model M#1 is a neural network, the model data "MDT#1" includes various information for the machine learning model, such as connection information on how the nodes included in each of the multiple layers that make up the neural network are connected to each other, and connection coefficients that are multiplied by the numerical values ​​input and output between the connected nodes.

[0051] The model storage unit 123 is not limited to storing information related to the items "model ID" and "model data," and may store information related to any other machine learning model.

[0052] (Regarding the content storage unit 124) The content storage unit 124 stores the content input by the provider received from the provider terminal 300. Fig. 7 is a diagram showing an example of information stored in the content storage unit according to the embodiment.

[0053] In the example shown in FIG. 7, the content storage unit 124 stores information relating to the items "business operator ID," "content ID," "content data," and "context" in association with each other.

[0054] "Provider ID" is an identifier that identifies a provider and is expressed as a string or number. "Content ID" is an identifier that identifies content received from a provider and is expressed as a string or number. "Content data" is the data of the content that the provider wishes to distribute. "Context" is information that indicates the user's context, which serves as the criteria for selecting users to whom the provider wishes to distribute content.

[0055] 7 shows that the content data indicated by the content data "CD#1" as the content identified by the content ID "CT#1" was input to the provider terminal 300 by the provider indicated by the provider ID "M1" with the user context indicated by the context "CT#1" specified. The information processing device 100 is then received by the receiving unit 131 and stored in the content storage unit 124.

[0056] The content storage unit 124 is not limited to storing information relating to the items "Provider ID," "Content ID," "Content Data," and "Context," and may store any other content-related information.

[0057] (Regarding the control unit 130) 3, the control unit 130 will be described. The control unit 130 is a controller that controls the information processing device 100, and is realized, for example, by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) reading out various programs stored in the storage unit 120 of the information processing device 100 and executing them using RAM as a working area. The control unit 130 is also a controller, and may be realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0058] 3, the control unit 130 has a reception unit 131, an acquisition unit 132, a first extraction unit 133, an estimation unit 134, a second extraction unit 135, an evaluation unit 136, a detection unit 137, and a distribution unit 138. The control unit 130 reads a program (software) from the storage unit 120 and executes it using the RAM as a working area, thereby realizing the reception unit 131, the acquisition unit 132, the first extraction unit 133, the estimation unit 134, the second extraction unit 135, the evaluation unit 136, the detection unit 137, and the distribution unit 138, and realizing or executing the functions and actions of the information processing described below.

[0059] (Regarding the reception unit 131) The reception unit 131 receives content input by a business operator from the business operator terminal 300. When the reception unit 131 receives content from the business operator terminal 300, it stores the received content in the content storage unit 124. That is, the reception unit 131 associates the "business operator ID," "content ID," "content data," and "context" received from the business operator terminal 300 with each other and stores them in the content storage unit 124.

[0060] (Regarding the acquisition unit 132) The acquisition unit 132 acquires behavior information indicating information related to the user's behavior and sensor information indicating measurement data from the sensor unit 250 included in the user terminal 200. Here, the behavior information is information indicating the user's behavior using a predetermined information service, which is generated by the user's use of the predetermined information service. Also, the sensor information is sensor information obtained by measuring the user's behavior using the sensor unit 250 included in the user terminal 200.

[0061] When the acquiring unit 132 acquires the behavioral information, it stores the acquired behavioral information in the behavioral information storage unit 121. When the acquiring unit 132 acquires the sensor information, it stores the acquired sensor information in the sensor information storage unit 122. Note that the acquiring unit 132 may acquire either or both of the behavioral information and the sensor information at predetermined time intervals, or may acquire either or both of the behavioral information and the sensor information every time the user accesses the information processing device 100.

[0062] (Regarding the first extraction unit 133) The first extraction unit 133 extracts a first group indicating a group of users who have a common behavior during a predetermined period of time, based on at least one of the behavior information and the sensor information. That is, the first extraction unit 133 extracts a first group indicating a group of users who have a common behavior during a predetermined period of time, based on information stored in either the behavior information storage unit 121 or the sensor information storage unit 122.

[0063] Here, a specific example will be used to explain the processing of the first extraction unit 133. For example, based on the location information and acceleration information as sensor information of the users, the first extraction unit 133 may extract, as a first group indicating a group of users who have a common behavior during a predetermined period, users who are located within a predetermined range (for example, a range within a radius of 100 m) at a predetermined time and whose accelerations are maintained within a predetermined range for a predetermined period of time, as "running a marathon."

[0064] (Regarding the estimation unit 134) The estimation unit 134 estimates the context of the users included in the first group extracted by the first extraction unit 133 based on at least one of the user's behavioral information and sensor information. The estimation unit 134 may estimate the context of a specific user by inputting at least one of the user's behavioral information and sensor information into a context estimation model. For example, the estimation unit 134 may estimate the user's context using a trained model that has learned the relationship between the user's context and browsing history, purchase history, route search history, bulletin board posting history, etc. included in the user's behavioral information.

[0065] Furthermore, the estimation unit 134 may estimate the context of a specific user by inputting the user's sensor information into a context estimation model. For example, the estimation unit 134 may estimate the user's context using a trained model that has learned the relationship between the user's sensor information, such as acceleration information, angular velocity information, position information, and illuminance information around the user, and the user's context.

[0066] (Regarding the second extraction unit 135) The second extraction unit 135 extracts a second group indicating a group of users in which the number of users with a matching context estimated by the estimation unit 134 accounts for a predetermined percentage or more of the users in the first group, from among the users included in the first group extracted by the first extraction unit 133. In other words, it can be said that the second extraction unit 135 classifies the users included in the first group according to the context estimated by the estimation unit 134. Here, the predetermined percentage may be set arbitrarily, for example, to 50% or 30%.

[0067] Here, the processing of the second extraction unit 135 will be described using a specific example. For example, it is assumed that the first extraction unit 133 extracts users who are deemed to be "running a marathon" based on the user's location information and acceleration information as users belonging to the first group. Then, it is assumed that the estimation unit 134 estimates the user's context based on the search history, purchase history, etc., which are behavioral information of the users estimated to be "running a marathon," and estimates the context "I like manufacturer N" for more than half of the users belonging to the first group. In this case, the second extraction unit 135 extracts the users whose context "I like manufacturer N" has been estimated as belonging to the second group.

[0068] (Regarding the evaluation unit 136) The evaluation unit 136 evaluates the achievement of the common behavior of the users included in the first group. That is, the evaluation unit 136 evaluates the achievement of the common behavior based on a preset evaluation criterion regarding the achievement of the common behavior of the users included in the first group extracted by the first extraction unit 133, or an evaluation criterion set by the evaluation unit 136. The evaluation unit 136 may express the evaluation result of the achievement of the common behavior of the users included in the first group, for example, using points expressed by the magnitude of a numerical value.

[0069] (Regarding the detection unit 137) The detection unit 137 detects a difference in the context of the user estimated by the estimation unit 134. That is, the detection unit 137 detects a difference in the context of the user based on the context estimated by the estimation unit 134 for the users included in the first group extracted by the first extraction unit 133.

[0070] Furthermore, the detection unit 137 detects a difference between the contexts of users whose common behavioral outcomes evaluated by the evaluation unit 136 are below a predetermined value and users whose common behavioral outcomes are above a predetermined value. That is, the detection unit 137 refers to the contexts of users whose evaluation values ​​evaluated by the evaluation unit 136 are above a predetermined value, and calculates, for each specific context, the proportion of users whose specific contexts are estimated among users whose evaluation values ​​are above a predetermined value. Similarly, the detection unit 137 also calculates, for each specific context, the proportion of users whose specific contexts are estimated among users whose evaluation values ​​are below a predetermined value. Then, the detection unit 137 detects, as a context difference, a context in which the proportion of users whose specific contexts are estimated among users whose evaluation values ​​are above a predetermined value and users whose evaluation values ​​are below a predetermined value is significantly different.

[0071] (About distribution unit 138) The distribution unit 138 distributes content to users based on the context of the users included in the first group estimated by the estimation unit 134. That is, the distribution unit 138 reads out content that has been input by a provider and that has been stored in the content storage unit 124, in accordance with the context of the users included in the first group estimated by the estimation unit 134, and distributes the content that has been input by a provider and that has been specified in the context to the user terminal 200 of the user whose context has been estimated.

[0072] Furthermore, the distribution unit 138 distributes content to users who are included in the first group but are not included in the second group, according to the difference in context between the first group and the second group. That is, it can be said that the distribution unit 138 distributes content to users who are included in the first group but do not have the context of users who belong to the second group, according to the context of users who belong to the second group.

[0073] Furthermore, the distribution unit 138 distributes content to users whose common behavioral achievements are equal to or less than a predetermined value, in accordance with the difference in context detected by the detection unit 137. That is, the distribution unit 138 distributes content to users whose evaluation values ​​of the common behavioral achievements of the users included in the first group evaluated by the evaluation unit 136 are equal to or less than a predetermined value, in accordance with the difference in context between users whose common behavioral achievements are equal to or less than the predetermined value, as detected by the detection unit 137, and users whose common behavioral achievements are equal to or greater than the predetermined value.

[0074] [4. User terminal configuration] Next, the configuration of the user terminal 200 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram showing an example of the configuration of the user terminal according to the embodiment. As shown in Fig. 8, the user terminal 200 has a communication unit 210, an input unit 220, an output unit 230, a control unit 240, a sensor unit 250, and a storage unit 260.

[0075] The communication unit 210 is realized by, for example, a NIC, a wireless LAN card, etc. The communication unit 210 is connected to a network N by wire or wirelessly, and transmits and receives various information to and from the information processing device 100 via the network N.

[0076] Various types of operation information are input from the user to the input unit 220. For example, the input unit 220 may accept various operations from the user via a display surface (e.g., the output unit 230) using a touch panel. The input unit 220 may also accept various operations from buttons provided on the user terminal 200 or a keyboard or mouse connected to the user terminal 200.

[0077] The output unit 230 is a display screen of a tablet terminal or the like realized by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display, and is a display device for displaying various information. In other words, if the input unit 220 of the user terminal 200 is a touch panel, the display screen of the output unit 230 accepts input from the user and also outputs the input to the user. The output unit 230 may also be a speaker, and may output sound from the speaker.

[0078] The control unit 240 is realized, for example, by a CPU, an MPU, or the like executing various programs stored in the user terminal 200 using RAM as a work area. The control unit 240 may also be realized, for example, by an integrated circuit such as an ASIC or an FPGA.

[0079] As shown in FIG. 8, the control unit 240 includes a providing unit 241.

[0080] The providing unit 241 provides the content distributed from the information processing device 100 to the user. For example, if the content distributed from the information processing device 100 is a video, the providing unit 241 provides the content to the user by causing the output unit 230 to output the video. Furthermore, if the content distributed from the information processing device 100 is audio, the providing unit 241 may provide the content to the user by causing the output unit 230 to output the audio of the content. Furthermore, if the content distributed from the information processing device 100 is text data, the providing unit 241 may provide the content to the user by causing the output unit 230 to display the text.

[0081] The sensor unit 250 is a measuring instrument that measures various types of sensor information. The sensor unit 250 may be an acceleration sensor that measures acceleration information. The acceleration sensor measures the acceleration of the user terminal 200. The acceleration sensor may be, for example, a capacitance-type acceleration sensor that uses a MEMS (Micro Electro Mechanical Systems) to create a movable electrode and a fixed electrode and measures acceleration using the relationship between acceleration and a change in capacitance due to the movement of a movable electrode.

[0082] The sensor unit 250 may also be a gyro sensor that measures angular velocity information. The gyro sensor measures the angular velocity of the user terminal 200. The gyro sensor may be a capacitance-type MEMS gyro sensor that generates a primary vibration in one direction in the movable electrode, and when rotation is applied to the movable electrode, a secondary vibration occurs due to the Coriolis force acting in a direction 90° from the vibration direction, causing a change in capacitance, which is detected. The angular velocity can be determined from the change in capacitance and the vibration phase of the movable electrode.

[0083] Furthermore, the sensor unit 250 may be a location information sensor that measures location information. The location information sensor measures the location information of the user terminal 200. The location information sensor may be, for example, a GPS (Global Positioning System) sensor. The GPS sensor has a receiver that receives radio waves transmitted from GPS satellites, receives radio waves transmitted from multiple GPS satellites, and measures the current location (e.g., latitude and longitude) of the user terminal 200 by calculating the distance from the GPS satellite to the user terminal 200 using the difference between the time the radio waves were received and the time the GPS satellite transmitted the radio waves.

[0084] The sensor unit 250 may also be an illuminance sensor that measures illuminance information. The illuminance sensor may be a phototransistor, a photodiode, or a photodiode with an amplifier circuit added. A photodiode has a structure in which an electrode is attached to a PN junction semiconductor. A built-in potential is generated in a depletion layer near the PN junction where there is a shortage of electrons and holes, resulting in a photovoltaic effect in which an output current flows when light hits the photodiode. An illuminance sensor measures illuminance by detecting this output current. A phototransistor has a structure in which a photodiode and a transistor are integrated, and the output current of the photodiode is amplified by the transistor before being output. The phototransistor can increase sensitivity by amplifying the minute current of the photodiode before being output.

[0085] The sensor unit 250 may also be a camera that captures image information. The camera includes an optical element and an image sensor. The optical element is an element that constitutes an optical system, such as a lens, a mirror, a prism, or a filter. The image sensor is an element that converts light that has entered through the optical element into an image signal, which is an electrical signal. The image sensor is, for example, a CCD (Charge Coupled Device) sensor or a CMOS (Complementary Metal Oxide Semiconductor) sensor.

[0086] The storage unit 260 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk, an SSD, an optical disk, etc. As shown in FIG.

[0087] (Regarding the sensor information storage unit 261) The sensor information storage unit 261 stores the sensor information measured by the sensor unit 250. Fig. 9 is a diagram showing an example of information stored in the sensor information storage unit according to the embodiment.

[0088] In the example shown in FIG. 9, the sensor information storage unit 261 stores information relating to the items "sensor type" and "sensor information" in association with each other.

[0089] "Sensor type" is information indicating the type of sensor of the sensor unit 250. "Sensor information" is information indicating measurement data measured by the sensor indicated in "Sensor type." Note that the sensor information is time-series data, and the sensor information is stored in association with the time at which the sensor information was measured.

[0090] That is, FIG. 9 shows that measurement data indicated by sensor information "SD#1" measured by a sensor of the sensor type indicated by sensor type "ST#1" is stored.

[0091] The sensor information storage unit 261 is not limited to storing information relating to the items "sensor type" and "sensor information," but may store any other information relating to any sensor.

[0092] [5. Configuration of operator terminal] Next, the configuration of the operator terminal 300 according to the embodiment will be described with reference to Fig. 10. Fig. 10 is a diagram showing an example of the configuration of the operator terminal according to the embodiment. As shown in Fig. 10, the operator terminal 300 includes a communication unit 310, an input unit 320, an output unit 330, and a control unit 340.

[0093] The communication unit 310 is realized by, for example, a NIC etc. The communication unit 310 is connected to a network N by wire or wirelessly, and transmits and receives various information to and from the information processing device 100 via the network N.

[0094] Various types of operation information are input from the business operator to the input unit 320. For example, the input unit 320 may accept various operations from the business operator via a keyboard or mouse connected to the business operator terminal 300. Alternatively, the input unit 320 may accept various operations from the business operator via a display surface (e.g., the output unit 330) of a touch panel.

[0095] The output unit 330 is a display screen realized by, for example, a liquid crystal display, an organic EL display, etc., and is a display device for displaying various information. When the input unit 320 of the business operator terminal 300 accepts various operations from the business operator via a touch panel, the display screen of the output unit 330 accepts input from the user and also outputs the information to the user.

[0096] The control unit 340 is realized, for example, by a CPU, an MPU, or the like executing various programs stored in the operator terminal 300 using RAM as a work area. The control unit 340 may also be realized, for example, by an integrated circuit such as an ASIC or an FPGA.

[0097] As shown in FIG. 10, the control unit 340 includes a receiving unit 341.

[0098] The reception unit 341 receives content from a business operator. The content refers to the content of information that the business operator intends to distribute to users, and may be, for example, advertisements, movies, music, photos, comics, animations, computer games, text messages, etc. For example, the reception unit 341 receives content from the business operator in association with the user's context. That is, the reception unit 341 receives a "business operator ID," "content data," and "context" from the business operator.

[0099] [6. Information Processing Flow] Next, the procedure of information processing according to the embodiment will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an example of information processing according to the embodiment.

[0100] First, the information processing device 100 acquires behavioral information indicating information related to user behavior and sensor information indicating measurement data from the sensor unit 250 included in the user terminal 200 (step S101). Next, the information processing device 100 extracts a first group indicating a group of users who share common behavior during a predetermined period of time based on at least one of the behavioral information and the sensor information (step S102). Then, the information processing device 100 estimates content for users included in the first group based on at least one of the behavioral information and the sensor information of the users (step S103). Then, the information processing device 100 delivers content to users based on the context of the estimated users included in the first group (step S104).

[0101] [7. Hardware Configuration] The information processing device 100 according to the above-described embodiment is realized by a computer 1000 having a configuration as shown in Fig. 12, for example. Fig. 12 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected via a bus 1090.

[0102] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The primary storage device 1040 is a memory device, such as a RAM, that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device that stores data used by the arithmetic device 1030 for various calculations and various databases, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), a flash memory, or the like.

[0103] The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a monitor or a printer, and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface), etc. The input IF 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, scanner, etc., and is realized by a USB, etc.

[0104] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. The input device 1020 may also be an external storage medium such as a USB memory.

[0105] The network IF 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0106] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0107] For example, when the computer 1000 functions as the information processing device 100, the arithmetic unit 1030 of the computer 1000 realizes the functions of the control unit 130 of the information processing device 100 by executing a program loaded onto the primary storage device 1040.

[0108] [8. Composition and Effects] The information processing device 100 according to the present disclosure includes an acquisition unit 132 that acquires behavioral information indicating information related to user behavior and sensor information indicating measurement data of a sensor unit 250 provided in a user terminal 200, a first extraction unit 133 that extracts a first group indicating a group of users who have common behavior during a specified period based on at least one of the behavioral information or the sensor information, an estimation unit 134 that estimates the context of users included in the first group extracted by the first extraction unit 133 based on at least one of the user behavioral information or the sensor information, and a detection unit 137 that detects differences in the user contexts estimated by the estimation unit 134.

[0109] According to this configuration, it is possible to provide an information processing device 100 that can detect differences in the contexts of multiple users.

[0110] The information processing device 100 according to the present disclosure further includes a second extraction unit 135 that extracts a second group indicating a group of users among the users included in the first group extracted by the first extraction unit 133, the second group being a group of users in which the number of users whose context matches the context estimated by the estimation unit 134 accounts for a predetermined percentage or more of the users included in the first group, and a distribution unit 138 that distributes content to users included in the first group who are not included in the second group, according to the difference in context between the first group and the second group.

[0111] According to this configuration, it is possible to provide the information processing device 100 that can detect differences in the contexts of many users and deliver content according to the differences in the contexts.

[0112] The information processing device 100 according to the present disclosure further includes an evaluation unit 136 that evaluates the results of common behavior for users included in the first group, a detection unit 137 that detects a difference in context between users whose results of common behavior evaluated by the evaluation unit 136 are below a predetermined value and users whose results of common behavior are above the predetermined value, and a distribution unit 138 that distributes content to users whose results of common behavior are below the predetermined value according to the difference in context detected by the detection unit 137.

[0113] According to this configuration, it is possible to provide the information processing device 100 that can detect differences in the contexts of many users and deliver content according to the differences in the contexts.

[0114] The information processing device 100 according to the present disclosure further includes a delivery unit 138 that delivers content to users based on the context of the users included in the first group estimated by the estimation unit 134.

[0115] According to this configuration, it is possible to provide the information processing device 100 that can detect differences in the contexts of many users and deliver content according to the differences in the contexts.

[0116] The information processing method of the present disclosure includes the steps of acquiring behavioral information indicating information related to user behavior and sensor information indicating measurement data from a sensor unit 250 provided in a user terminal 200, extracting a first group indicating a group of users who have common behavior at a specified time period based on at least one of the behavioral information or the sensor information, estimating the context of users included in the first group based on at least one of the user behavioral information or the sensor information, and detecting differences in the estimated user contexts.

[0117] According to this configuration, it is possible to provide an information processing method that can detect differences in the contexts of multiple users.

[0118] The information processing program according to the present disclosure includes the steps of acquiring behavioral information indicating information related to user behavior and sensor information indicating measurement data from a sensor unit 250 provided in a user terminal 200, extracting a first group indicating a group of users who share common behavior during a specified period based on at least one of the behavioral information or the sensor information, estimating the context of users included in the first group based on at least one of the user behavioral information or the sensor information, and detecting differences in the estimated user contexts.

[0119] According to this configuration, it is possible to provide an information processing program that can detect differences in the contexts of multiple users.

[0120] [9. Notes] The above describes the embodiments of the present application in detail based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art.

[0121] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, the acquisition unit 132 can be read as acquisition means or acquisition circuit. [Explanation of symbols]

[0122] 100 Information processing device 110 Communications Department 120 Storage section 121 Behavior information storage unit 122 Sensor information storage unit 123 Model Memory Unit 124 Content storage unit 130 control section 131 Reception 132 Acquisition Department 133 1st extraction part 134 Estimation Department 135 Second extraction part 136 Evaluation Department 137 Detector 138 Distribution Department 200 User terminals 300 Operator terminal N Network

Claims

1. an acquisition unit that acquires behavior information indicating information related to a user's behavior and sensor information indicating measurement data of a sensor unit provided in a user terminal carried by the user during the behavior; a first extraction unit that extracts a first group indicating a group of users who have a common behavior during a predetermined period based on the behavior information and the sensor information; an estimation unit that estimates the context of the users included in the first group based on the behavioral information and the sensor information of the users included in the first group extracted by the first extraction unit from the relationship between behavioral information, sensor information, and user context; a distribution unit that distributes content to users based on the context of the users included in the first group estimated by the estimation unit, the estimation unit estimates, as one of the contexts of the users included in the first group, whether the users include a predetermined object during a common action based on image information as the sensor information of the users included in the first group; the distribution unit distributes content related to the predetermined object to users included in the first group who do not use the predetermined object during a common action. Information processing device.

2. a second extraction unit that extracts a second group indicating a group of users whose number of users whose contexts match the context estimated by the estimation unit accounts for a predetermined percentage or more of the users included in the first group, from among the users included in the first group extracted by the first extraction unit; the distribution unit distributes content to users included in the first group who are not included in the second group, according to a difference in context between the first group and the second group. The information processing device according to claim 1 .

3. an evaluation unit that evaluates the results of common actions of the users included in the first group by ranking or quantifying them; a detection unit that detects a difference in context between a user whose common behavioral outcome evaluated by the evaluation unit is equal to or less than a predetermined value and a user whose common behavioral outcome is equal to or greater than the predetermined value; the distribution unit distributes content to users whose results of common behavior are equal to or less than a predetermined value, in accordance with the difference in context detected by the detection unit. The information processing device according to claim 1 .

4. the estimation unit inputs the behavioral information and the sensor information of the users included in the first group extracted by the first extraction unit into a model that infers a user's context when the behavioral information and the sensor information are input, and estimates the context of the users included in the first group. The information processing device according to claim 1 .

5. An information processing method executed by an information processing device, comprising: an acquisition step of acquiring behavioral information indicating information related to the user's behavior and sensor information indicating measurement data of a sensor unit provided in a user terminal carried by the user during the behavior; a first extraction step of extracting a first group representing a group of users who share a common behavior during a predetermined period of time based on the behavior information and the sensor information; an estimation step of estimating the context of the users included in the first group based on the behavioral information and the sensor information of the users included in the first group from the relationship between the behavioral information, the sensor information, and the user's context; a delivery step of delivering content to users based on the estimated context of the users included in the first group; Including, In the estimation step, as one of the contexts of the users included in the first group, it is estimated whether or not the users included in the first group use a predetermined object during a common action based on image information as the sensor information of the users included in the first group; In the distributing step, content related to the predetermined object is distributed to users who are included in the first group and who do not use the predetermined object during a common activity. Information processing methods.

6. an acquisition procedure for acquiring behavior information indicating information related to a user's behavior and sensor information indicating measurement data of a sensor unit provided in a user terminal carried by the user during the behavior; a first extraction step of extracting a first group representing a group of users who share a common behavior during a predetermined period of time based on the behavior information and the sensor information; an estimation step of estimating the context of the users included in the first group based on the behavioral information and the sensor information of the users included in the first group from the relationship between the behavioral information, the sensor information, and the user's context; a delivery step of delivering content to users based on the estimated context of the users included in the first group; An information processing program that causes a computer to execute the following: In the estimation step, as one of the contexts of the users included in the first group, it is estimated whether or not the users included in the first group use a predetermined object during a common action based on image information as the sensor information of the users included in the first group; In the delivery step, content related to the predetermined object is delivered to users included in the first group who do not use the predetermined object during a common action. Information processing program.

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