Information processing device

Abstract

Under the hypothesis that users having a certain specific thinking tendency will gather in a certain location in response to a property or characteristic of said location, thinking tendency data (referred to as correct answer data) indicating a comparatively accurate thinking tendency is acquired regarding a small number of users and using the method of a questionnaire, test, diagnosis, or similar, and the thinking tendency data (referred to as assignment data) corresponding to such correct answer data is assigned to a location that has been visited by a user possessing this correct answer data. Then, when a user who does not possess correct answer data visits the location to which the assignment data has been assigned, thinking tendency data (referred to as inference data) corresponding to the assignment data that was assigned to said location is allocated to said user. It thus becomes possible to infer the thinking tendencies of a large number of users, even without implementing a questionnaire or the like among said users.

Description

Information processing device

[0001] The present invention relates to a technique for estimating a user's tendency to think.

[0002] It is known that when humans make decisions, they rely on their own preconceptions, rules of thumb, intuition, and the like. In other words, each individual has different thinking or psychological tendencies, characteristics, or inclinations (hereinafter collectively referred to as "thinking tendencies" or "thinking tendencies"), which are also known as cognitive biases. For example, Patent Literature 1 discloses a technology that, in a situation where a user has set a goal of training to improve their health, but has internal characteristics such as a lack of patience and a tendency to give up easily, making it difficult for them to continue taking actions to achieve the goal, presents the user with quiz content, etc., regarding the health benefits of training, thereby guiding the user to rationally consider their own health.

[0003] Japanese Patent Application Laid-Open No. 2022-20942

[0004] Machine learning can be used to accurately estimate human thought patterns. To perform this machine learning, training data is required, which is a set of correct answer data that relatively accurately represents the thought patterns of a large number of users. This correct answer data can be obtained by methods such as questionnaires, tests, or diagnoses of users (see, for example, https: / / www.jstage.jst.go.jp / article / jjpsy / 82 / 5 / 82#5#450 / #pdf).

[0005] However, conducting such questionnaires, tests, diagnoses, etc. one by one for each of a large number of users would be a huge burden in terms of time and cost.

[0006] Therefore, an object of the present invention is to estimate the tendency of thinking of each user in a simple manner.

[0007] In order to solve the above problem, the present invention provides an information processing device comprising: an acquisition unit that acquires user location information; an assignment unit that assigns assigned data regarding thinking tendencies to locations determined to have been visited by multiple registered users for whom correct answer data regarding thinking tendencies has been registered, based on the acquired location information, in accordance with the correct answer data registered for each of the multiple registered users; and a registration unit that, when it is determined, based on the acquired location information, that an unregistered user for whom correct answer data regarding thinking tendencies has not been registered has visited a location to which assigned data has been assigned, registers estimated data regarding thinking tendencies for the unregistered user in accordance with the assigned data assigned to the location.

[0008] According to the present invention, the tendency of thinking of each user can be estimated in a simple manner.

[0009] 1 is a block diagram showing an example of the configuration of an information processing system 1 according to an embodiment of the present invention. FIG. 2 is a block diagram showing an example of the hardware configuration of an information processing device 20 according to the embodiment. FIG. 3 is a block diagram showing an example of the functional configuration of the information processing device 20. FIG. 4 is a diagram illustrating location information history data according to the embodiment. FIG. 5 is a diagram illustrating thinking tendency data according to the embodiment. FIG. 6 is a diagram illustrating a location table according to the embodiment. FIG. 7 is a diagram illustrating thinking tendency data according to the embodiment. A flowchart showing an example of an assigned data assigning operation of the information processing device 20. A diagram illustrating an example of the relationship between correct answer data of a registered user and assigned data assigned to locations in the embodiment. A flowchart showing an example of the estimated data registration operation of the information processing device 20. A diagram illustrating an example of the relationship between correct answer data of an unregistered user and assigned data assigned to locations in the embodiment. A diagram illustrating an example of a location table according to a modified example. A diagram illustrating an example of a location table according to a modified example. A diagram illustrating an example of a location table according to a modified example.

[0010] 1 is a diagram showing an example of the configuration of an information processing system 1 according to an embodiment of the present invention. The information processing system 1 includes user terminals 10 each portable by a plurality of users, an information processing device 20 corresponding to the information processing device of the present invention, and a communication network 2 including a wireless communication network or a wired communication network that communicatively connects these. The information processing device 20 may be configured by a single computer or may be configured by multiple computers.

[0011] The inventor hypothesized that users with a certain tendency to think in a certain way gather at a certain location depending on the characteristics or features of that location. For example, users with a high tendency to think in terms of loss aversion tend to gather at a supermarket during evening sales, users with a high tendency to think in terms of time selection tend to gather at a taxi stand, and users with a low tendency to think in terms of time selection tend to gather at a store that is so crowded that there are long lines to get in.

[0012] Therefore, in this embodiment, under this hypothesis, thinking tendency data (hereinafter referred to as correct data) that indicates relatively accurate thinking tendencies is obtained from a small number of users through methods such as questionnaires, tests, or diagnoses, and thinking tendency data corresponding to the correct data (hereinafter referred to as assigned data) is assigned to locations visited by users who possess such correct data. Then, when a user who does not possess correct data visits a location to which assigned data has been assigned, thinking tendency data (hereinafter referred to as estimated data) corresponding to the assigned data assigned to that location is assigned to that user. This makes it possible to estimate the thinking tendencies of these users without conducting questionnaires or the like on a large number of users.

[0013] FIG. 2 is a diagram showing the hardware configuration of the information processing device 20. The information processing device 20 is physically configured as a computer including a processor 2001, a memory 2002, a storage 2003, a communication device 2004, an input device 2005, an output device 2006, and a bus connecting these devices. Each of these devices operates using power supplied from a battery (not shown). In the following description, the term "device" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the information processing device 20 may be configured to include one or more of the devices shown in FIG. 2, or may be configured without including some of the devices. Furthermore, the information processing device 20 may be configured by communicating with multiple devices each having a different housing.

[0014] Each function in the information processing device 20 is realized by loading specified software (programs) onto hardware such as the processor 2001 and memory 2002, causing the processor 2001 to perform calculations, control communication via the communication device 2004, and control at least one of reading and writing data in the memory 2002 and storage 2003.

[0015] The processor 2001 controls the entire computer by running, for example, an operating system. The processor 2001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. Furthermore, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 2001.

[0016] The processor 2001 reads programs (program codes), software modules, data, etc. from at least one of the storage 2003 and the communication device 2004 into the memory 2002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the information processing device 20 may be implemented by a control program stored in the memory 2002 and running on the processor 2001. Various processes may be executed by one processor 2001, or may be executed simultaneously or sequentially by two or more processors 2001. The processor 2001 may be implemented by one or more chips. The programs may be transmitted to the information processing device 20 via a telecommunications line.

[0017] The memory 2002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 2002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 2002 can store executable programs (program codes), software modules, etc. for implementing the method according to this embodiment.

[0018] Storage 2003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 2003 may also be called an auxiliary storage device.

[0019] The communication device 2004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0020] Each device, such as the processor 2001 and the memory 2002, is connected by a bus for communicating information. The bus may be configured using a single bus, or may be configured using different buses for each device.

[0021] The information processing device 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 2001 may be implemented using at least one of these pieces of hardware.

[0022] 3 is a block diagram showing the functional configuration of the information processing device 20. In the information processing device 20, a processor 2001 reads a program or the like from a storage 2003 into a memory 2002 and executes the program, thereby realizing the functions of a location information acquisition unit 21, a correct answer data acquisition unit 22, a memory unit 23, an assignment unit 24, a registration unit 25, and an output unit 26.

[0023] The location information acquisition unit 21 acquires location information indicating the location of each user. In this embodiment, the location of the user terminal 10 carried by each user is used as the user's location. The location information of the user terminal 10 may be obtained by any known positioning method, such as location information measured by a positioning device such as a GPS unit provided in the user terminal 10. The location information of the user terminal 10 may also be location information acquired from a positioning system that measures the location of the user terminal 10 using identification information and the location of a wireless device such as a cellular base station, wireless access point, or beacon device that wirelessly communicates with the user terminal 10. The location information acquisition unit 21 acquires the location information of the user terminal 10 from the user terminal 10 or the positioning system, for example, via the communication network 2. As shown in FIG. 4 , the storage unit 23 stores a user ID, which is identification information for identifying each user, and a location information history, which is a history of location information indicating the location of the user (here, each user terminal 10), in association with each other. The location information history includes location information indicating the user's location and a positioning date and time indicating the date and time when the positioning was performed.

[0024] Returning to the explanation of Fig. 3, the correct answer data acquisition unit 22 acquires correct answer data relating to the thinking tendencies of a portion of all users. This correct answer data is obtained by methods such as questionnaires, tests, or diagnoses of these users, and relatively accurately indicates the thinking tendencies of these users. The correct answer data acquisition unit 22 acquires the results obtained by the above-mentioned methods as correct answer data from a storage device or the like that stores the results, for example, via the communication network 2.

[0025] As shown in FIG. 5 , the storage unit 23 stores the correct answer data in association with the user ID of the user from whom the correct answer data was obtained. In this embodiment, the correct answer data is represented, for example, by scores (hereinafter referred to as thinking tendency scores) calculated for each of a plurality of thought tendency categories. In the example of FIG. 5 , the thinking tendencies are categorized into categories handled by so-called nudge theory, such as "loss aversion," "conformity effect," and "time preference." The thinking tendency score takes a value between 0 and 1, with "1" representing the maximum strength of the thinking tendency and "0" representing the minimum strength of the thinking tendency. In this embodiment, a user for whom correct answer data is registered is referred to as a "registered user," and a user for whom correct answer data is not registered is referred to as an "unregistered user." In the example of FIG. 5 , a classification, such as whether the user is a registered user or an unregistered user, is also stored in association with the user ID.

[0026] 3, the assigning unit 24 assigns assigned data relating to thinking tendencies to a location determined to have been visited by a registered user in accordance with the correct answer data registered for that registered user, based on the location information acquired by the location information acquiring unit 21. Specifically, for a location determined to have been visited by multiple registered users, the assigning unit 24 calculates a representative value (e.g., average, median, or mode) of the correct answer data of the multiple registered users, and assigns the representative value to the location as assigned data.

[0027] Here, assigning assigned data to a location means that a location determined to have been visited by multiple registered users is associated with a representative value of the correct answer data of the multiple registered users and stored in the storage unit 23. As shown in FIG. 6, the storage unit 23 stores a location table in which a location ID, which is identification information for identifying each location, location information indicating the location of the location, and assigned data assigned to the location are described in association with each other. In this embodiment, each location is predetermined as a location having certain characteristics or features, such as a point of interest (POI). Like the correct answer data described above, the assigned data is calculated and assigned according to the category of thinking tendency.

[0028] Returning to the explanation of Figure 3, when the registration unit 25 determines, based on the location information acquired by the location information acquisition unit 21, that an unregistered user has visited a location to which assigned data has been assigned, the registration unit 25 registers estimated data regarding the thinking tendency of the unregistered user in accordance with the assigned data assigned to that location.

[0029] Here, registering estimated data means storing estimated data identified for an unregistered user in association with the unregistered user in the storage unit 23. For example, if an unregistered user with user ID "U002" in FIG. 5 visits a location with location ID "P001" in FIG. 6, the thinking tendency scores (loss aversion: 0.6, conformity effect: 0.2, time preference: 0.1...) assigned to location ID "P001" are registered as estimated data of the thinking tendency score of the unregistered user with user ID "U002," as shown in FIG. 7. In other words, in this embodiment, the assigned data (thinking tendency scores) assigned to locations visited by the unregistered user are registered as estimated data for the unregistered user.

[0030] 3, the output unit 26 outputs to the outside the contents stored in the storage unit 23 (particularly the estimated data registered for the unregistered users exemplified in FIG. 7 ), which are used, for example, as training data for machine learning to accurately estimate human thinking tendencies.

[0031] [Operation] Next, the operation of this embodiment will be described. Fig. 8 is a flowchart showing an example of the operation of assigning assigned data by the information processing device 20. Before the process shown in Fig. 8 is started, the location information acquisition unit 21 acquires location information of each user, for example, periodically or intermittently, and the storage unit 23 stores this location information as location information history in association with the user ID of the user.

[0032] 8, the correct answer data acquisition unit 22 acquires correct answer data relating to the thinking tendencies of some users among all users (step S11). The storage unit 23 stores the correct answer data in association with the user ID of the user from whom the correct answer data was obtained, i.e., the registered user.

[0033] Next, the assigning unit 24 compares the registered user's location information history acquired by the location information acquisition unit 21 and stored in the storage unit 23 with the location information of each location described in the location table of the storage unit 23 (step S12) and determines the locations visited by the registered user (step S13). Specifically, the assigning unit 24 determines that the registered user has visited a location when a predetermined range centered on the location information of each location described in the location table overlaps with the registered user's location information for a predetermined period of time or more. By appropriately setting the predetermined period of time, it is possible to avoid erroneous determinations that the registered user intentionally visited a location that the registered user simply passed through. The assigning unit 24 performs the processes of steps S12 and S13 for the location information history of all registered users.

[0034] Next, for a location determined to have been visited by a registered user, the assigning unit 24 calculates a representative value (e.g., an average value, a median value, or a mode value) of the correct answer data stored for that registered user (step S14), and assigns the representative value to that location as assigned data (step S15). As a result, the storage unit 23 stores the assigned data assigned to that location in association with the location ID of each location.

[0035] The above-described processing of steps S11 to S15 is executed repeatedly, for example, periodically. Here, FIG. 9 is a diagram illustrating the relationship between the correct answer data of registered users and the assigned data assigned to locations. For example, assume that the processing of steps S11 to S15 is executed after registered user U1, whose synchronization effect thinking tendency score is "0.4," and registered user U2, whose synchronization effect thinking tendency score is "0.6," visit location P1. As a result, location P1 is assigned a value of "0.5," which is the representative value (here, the average value) of the thinking tendency scores "0.4" and "0.6."

[0036] After that, for example, assume that the processing of steps S11 to S15 is executed after registered user U3, who has a synchrony effect thinking tendency score of "0.8," visits point P1. In this case, point P1 is assigned a representative value (here, an average value) of the thinking tendency scores "0.4," "0.6," and "0.8."

[0037] 10 is a flowchart showing an example of an estimated data registration operation of the information processing device 20. Before the process shown in FIG. 10 is started, the location information acquisition unit 21 acquires location information of each user, for example, periodically or intermittently, and the storage unit 23 stores this location information as location information history in association with the user ID of the user.

[0038] 10, the registration unit 25 compares the location information history of the unregistered user acquired by the location information acquisition unit 21 and stored in the storage unit 23 with the location information of each location described in the location table of the storage unit 23 (step S21) and determines the locations visited by the unregistered user (step S22). Specifically, the registration unit 25 determines that the unregistered user has visited a location when a predetermined range centered on the location information of each location described in the location table overlaps with the location information of the unregistered user for a predetermined period of time or more. By appropriately setting the predetermined period of time, it is possible to avoid erroneous determinations that the unregistered user intentionally visited a location that they simply passed through. The registration unit 25 performs the processes of steps S21 and S22 for the location information history of all unregistered users.

[0039] Next, the registration unit 25 registers estimated data for the unregistered user according to the assigned data assigned to the location determined to have been visited by the unregistered user (step S23). As a result, the storage unit 23 stores estimated data indicating the thinking tendency of the unregistered user in association with the user ID of each unregistered user.

[0040] The above-described processing of steps S21 to S23 is executed repeatedly, for example, periodically. Here, FIG. 11 is a diagram illustrating the relationship between the estimated data of an unregistered user and the assigned data assigned to a location. For example, assume that the processing of steps S21 to S23 is executed after an unregistered user U4, who has no assigned estimated data, visits location P2, which has been assigned a synchronization effect thinking tendency score of "0.6." As a result, the synchronization effect thinking tendency score of "0.6" assigned to location P2 is registered as estimated data for the unregistered user U4.

[0041] For example, suppose the processing of steps S21 to S23 is performed after the unregistered user U4 visits location P3, which has been assigned a synchronization effect thinking tendency score of "0.8." In this case, a thinking tendency score of "0.7" (here, the average of the two) corresponding to the synchronization effect thinking tendency score "0.6" previously registered for the unregistered user U4 and the synchronization effect thinking tendency score "0.8" assigned to location P3 is registered as estimated data for the unregistered user U4. In other words, when the registration unit 25 determines that an unregistered user whose estimated data is registered has visited a location to which assigned data is assigned, it registers new estimated data for the unregistered user that corresponds to the estimated data registered for the unregistered user and the assigned data assigned to the location.

[0042] According to the embodiment described above, it is possible to obtain a large amount of estimated data from a small amount of correct answer data without conducting time- or cost-intensive tasks such as questionnaires, tests, or diagnoses on a large number of users.

[0043] [Modifications] The present invention is not limited to the above-described embodiment. The above-described embodiment may be modified as follows. Furthermore, two or more of the following modifications may be combined and implemented.

[0044] [Variation 1] The assigning unit 24 may assign assigned data to a location when the thinking tendency of multiple registered users determined to have visited the location is at or above a certain level. Here, "thinking tendency at or above a certain level" refers to a case where the thinking tendency is high or low, such as when the thinking tendency score for each category of thinking tendency is at or above a certain threshold (e.g., 0.7) or below a certain threshold (e.g., 0.3). In this case, the assigning unit 24 extracts registered users from the multiple registered users determined to have visited the location whose thinking tendency score is at or above a certain threshold (e.g., 0.7) or below a certain threshold (e.g., 0.3), and assigns a representative value of the correct answer data stored for the extracted registered users to the location as assigned data. This makes it possible to assign assigned data using only the correct answer data of registered users whose thinking tendency is recognized to have a certain degree of characteristic.

[0045] [Variation 2] The assigning unit 24 may assign assigned data to a location when the variance in the thinking tendencies of multiple registered users determined to have visited the location is at or below a certain level. Here, the variance in the thinking tendencies is at or above a certain level when, when the variance of the thinking tendencies scores is calculated for each category of thinking tendencies, the variance value is at or below a certain threshold. In this case, only when the variance in the thinking tendencies of multiple registered users determined to have visited the location is at or below a certain level, the assigning unit 24 assigns the location to the representative value of the correct answer data stored for those registered users as assigned data. In this way, only when there are many registered users whose thinking tendencies are recognized to be distinctive can the assigned data be assigned using the correct answer data of those registered users.

[0046] [Variation 3] The assigning unit 24 may assign, to a certain location, assigned data corresponding to the correct answer data of multiple registered users who meet the conditions when visiting the location, and the registration unit 25 may register estimated data corresponding to the assigned data for unregistered users who meet the conditions when visiting the location. The conditions referred to here are conditions other than those related to location, and examples such as the following are possible. [Variation 3-1] The conditions are, for example, conditions related to the day or time when the location was visited. The assigning unit 24 assigns, to the location, assigned data corresponding to the correct answer data of multiple registered users who meet the conditions related to the day or time when the location was visited. In this case, as shown in FIG. 12 , the location table stored in the storage unit 23 describes date and time conditions related to the day or time at each location in association with the location ID of each location. For example, it may be hypothesized that users with a high tendency toward loss aversion tend to gather during evening sales at supermarkets. Based on this hypothesis, for locations where limited-time sales are held, the time period during which the sale is likely to be held is described in the location table as a date and time condition. The assignment unit 24 extracts, from among the registered users who visited a certain location, those who visited the location on a day or time that matches the date and time conditions corresponding to the location, and assigns assigned data corresponding to the correct answer data of the extracted registered users to the location.Then, the registration unit 25 registers estimated data corresponding to the assigned data for unregistered users who visited the location on a day or time that matches the date and time conditions corresponding to the location.This makes it possible to estimate the thinking tendency of unregistered users, taking into account the day or time when they visited the location.

[0047] [Variation 3-2] The conditions are also conditions related to the weather when the location is visited. The assigning unit 24 assigns to the location assignment data corresponding to the correct answer data of multiple registered users who match the weather-related conditions when the location is visited. In this case, as shown in FIG. 13 , the location table stored in the storage unit 23 describes date and time conditions related to the weather at each location in association with the location ID of the location. For example, it can be hypothesized that users who gather at a taxi stand even on a sunny day have a higher tendency to consider time selection compared to users who gather at a taxi stand on a rainy day. Based on this hypothesis, for locations where the weather affects their thinking tendency, weather conditions related to the weather are described in the location table. The assigning unit 24 extracts, from among the registered users who visited a certain location, registered users who visited the location when the weather matched the weather conditions corresponding to the location, and assigns to the location assignment data corresponding to the correct answer data of the extracted registered users. The registration unit 25 then registers estimated data corresponding to the assigned data for unregistered users who visited the location when the weather matched the weather conditions corresponding to the location, thereby making it possible to estimate the thinking tendency of the unregistered users, taking into account the weather when they visited the location.

[0048] [Variation 3-3] The condition is a condition related to the duration of visit and stay at a location. The assigning unit 24 assigns assigned data to the location according to the correct answer data of multiple registered users who meet the condition related to the duration of visit and stay at the location. In this case, as shown in FIG. 14 , the location table stored in the storage unit 23 describes the stay conditions related to the duration of visit and stay at each location, in association with the location ID of the location. For example, a hypothesis can be made that stores that are so crowded that there are lines to enter tend to attract users with a low time preference mindset. Based on this hypothesis, for locations where stay time affects thought patterns, stay conditions related to the stay time are described in the location table. The assigning unit 24 extracts registered users who have visited a certain location and whose stay time meets the stay condition corresponding to the location, and assigns assigned data to the location according to the correct answer data of the extracted registered users. Then, the registration unit 25 registers estimated data corresponding to the assigned data for non-registered users who have visited the location and whose stay time meets the stay condition corresponding to the location. This makes it possible to estimate the thinking tendency of unregistered users, taking into account the length of time spent at locations when visiting them.

[0049] [Variation 4] Repeated generation and updating of estimated data by performing the process of FIG. 8 multiple times may result in a decrease in the accuracy of the estimated data. Therefore, when correct data is acquired for an unregistered user whose estimated data is registered (i.e., when the correct data is acquired through a method such as a questionnaire, test, or diagnosis for the unregistered user), the registration unit 25 calculates the difference between the estimated data registered for the unregistered user and the correct data acquired for the unregistered user. If the difference is greater than or equal to a threshold, the accuracy of the estimated data is reduced. Therefore, the assigned data assigned to locations visited by the unregistered user is initialized, and the process of FIG. 8 is performed again for those locations. This makes it possible to suppress deterioration of the estimated data.

[0050] [Variation 5] In the above embodiment, each location was predetermined as a location with certain characteristics or features. However, in the present invention, each location may be identified from each user's location information history. For example, by analyzing each user's location information history over a sufficient period of time to create a location information heat map, it is possible to identify locations that each user visits frequently or frequently by referring to this heat map. Then, such locations that are visited by each user with a frequency or number of visits above a threshold or relatively high may be registered in the location table as locations to which assigned data is to be assigned. This eliminates the need to predetermine locations to which assigned data is to be assigned.

[0051] [Other Modifications] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining software with the single device or the multiple devices.

[0052] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0053] For example, the information processing device 20 according to an embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0054] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (New Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (ULtra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (ULtra-WIDE Band), Bluetooth (registered trademark), or other suitable systems, and next-generation systems extended based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G, etc.) may also be applied.

[0055] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0056] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be transmitted to another device.

[0057] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0058] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0059] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, should be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Additionally, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then such wired and / or wireless technologies are included within the definition of a transmission medium.

[0060] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0061] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0062] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0063] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0064] The "unit" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," etc.

[0065] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0066] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0067] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0068] 1: Information processing system, 2: Communication network, 20: Information processing device, 21: Location information acquisition unit, 22: Correct answer data acquisition unit, 23: Storage unit, 24: Assignment unit, 25: Registration unit, 26: Output unit, 2001: Processor, 2002: Memory, 2003: Storage, 2004: Communication device, P1 to P3: Locations, U1 to U3: Registered users, U4: Unregistered user.

Claims

1. An information processing device comprising: an acquisition unit that acquires user location information; an assignment unit that assigns attribute data related to thinking tendencies to locations determined to have been visited by multiple registered users for whom correct answer data related to thinking tendencies is registered, based on the acquired location information, in accordance with the correct answer data registered for each of the multiple registered users; and a registration unit that, when it is determined based on the acquired location information that an unregistered user for whom correct answer data related to thinking tendencies is not registered has visited a location to which attribute data is assigned, registers estimated data related to thinking tendencies for the unregistered user in accordance with the attribute data assigned to the location.

2. The information processing device according to claim 1, characterized in that the assigning unit assigns the assigned data to the location when the tendency of thought of multiple registered users who are determined to have visited the location is at or above a certain level.

3. The information processing device according to claim 1, characterized in that the assigning unit assigns the assigned data to the location when the variance in the thought trends of multiple registered users who are determined to have visited the location is below a certain level.

4. The information processing device described in claim 1, characterized in that the assignment unit assigns to the location assignment data corresponding to correct data of multiple registered users who match the conditions when visiting the location, and the registration unit registers estimated data corresponding to the assignment data for non-registered users who match the conditions when visiting the location.

5. The information processing device according to claim 4, wherein the condition is a condition relating to the day or time when the location is visited.

6. The information processing device according to claim 4, wherein the condition is a condition relating to the weather when the location is visited.

7. The information processing device according to claim 4, wherein the condition is a condition relating to the period of time during which the user visits and stays at the location.

8. The information processing device according to claim 1, wherein the assigning unit assigns assigned data based on a representative value of the correct answer data registered for each of the plurality of registered users.

9. The information processing device of claim 1, wherein when the correct answer data is acquired for an unregistered user whose estimated data is registered, and the difference between the registered estimated data and the acquired correct answer data is equal to or greater than a threshold, the registration unit initializes the assigned data assigned to locations visited by the unregistered user.

10. The information processing device of claim 1, characterized in that when it is determined that the unregistered user for whom the estimated data is registered has visited a location to which the assigned data is assigned, the registration unit registers new estimated data for the unregistered user that corresponds to the estimated data registered for the unregistered user and the assigned data assigned to the location.

Images