System

Abstract

An object of a system according to an embodiment is to effectively control a flow of people by analyzing behavior data of a user and proposing an appropriate passage route and timing.SOLUTION: A system includes a behavior data collection unit, an analysis unit, a proposal unit, and an incentive provision unit. The action data collection unit collects action data of a user. The analysis unit analyzes the data collected by the action data collection unit. The suggestion section suggests an appropriate travel route or timing based on the data analyzed by the analysis section. The incentive providing unit provides an incentive to the user who follows the route or the timing proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to effectively control people flow by utilizing user behavior data to suggest appropriate routes and timings.

[0005] The system according to the embodiment aims to effectively control the flow of people by analyzing user behavior data and proposing appropriate travel routes and timings. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral data collection unit, an analysis unit, a suggestion unit, and an incentive provision unit. The behavioral data collection unit collects behavioral data of users. The analysis unit analyzes the data collected by the behavioral data collection unit. The suggestion unit suggests an appropriate travel route or timing based on the data analyzed by the analysis unit. The incentive provision unit provides an incentive to users who follow the route or timing suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively control the flow of people by analyzing user behavior data and proposing appropriate travel routes and timings. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The travel motivation control app according to an embodiment of the present invention is a system that collects and analyzes user behavior data and suggests appropriate timing and routes to ensure smooth pedestrian flow. This allows the travel motivation control app to efficiently manage people's movements within a specific area and avoid congestion.

[0029] A travel motivation control app according to an embodiment includes a behavioral data collection unit, an analysis unit, a suggestion unit, and an incentive provision unit. The behavioral data collection unit collects user behavioral data, such as location information, movement history, and activity log. The behavioral data collection unit can also collect data using a smartphone sensor. The analysis unit analyzes the data collected by the behavioral data collection unit. For example, the analysis unit can analyze user behavioral trends using data mining technology. The analysis unit can also predict congestion using statistical analysis techniques. The suggestion unit suggests appropriate travel routes and timings based on the data analyzed by the analysis unit. For example, the suggestion unit can suggest routes that avoid areas where congestion is expected. The suggestion unit can also suggest the optimal means of transportation based on the user's destination and current location. The incentive provision unit provides incentives to users who follow the route and timing suggested by the suggestion unit. For example, points can be awarded for traveling a specific route. The incentive provision unit can also provide discount coupons for traveling during times when congestion is avoided. This allows the travel motivation control app to suggest appropriate travel routes and timings based on user behavior data and provide incentives, thereby ensuring smooth pedestrian flow.

[0030] The behavioral data collection unit analyzes a user's social media activity and combines it with the behavioral data to understand behavioral trends. The behavioral data collection unit, for example, analyzes the content and frequency of a user's social media posts and combines it with the behavioral data to identify behavioral trends. For example, it identifies the frequency of participation in specific events and topics of interest. The behavioral data collection unit also analyzes friendships and follower trends on social media to identify the user's behavioral trends. For example, it infers the user's interests based on places visited by friends and events attended. The behavioral data collection unit also analyzes social media check-in data to identify the user's movement patterns. For example, if a user frequently checks in to a specific location, it determines that the location is an important spot for the user. This allows for more detailed behavioral trends to be identified by analyzing social media activity.

[0031] The behavioral data collection unit collects the user's health data, integrates it with the behavioral data, and analyzes it to suggest travel routes based on the user's health status. The behavioral data collection unit, for example, collects heart rate and step count data from the user's smartwatch or fitness tracker, and integrates it with the behavioral data for analysis. For example, if the heart rate is high, it suggests a relaxing route. The behavioral data collection unit also collects sleep data from the user's health app, integrates it with the behavioral data, and analyzes it. For example, if the user is sleep deprived, it suggests a route that can be traveled in a short amount of time. The behavioral data collection unit also collects the user's food record data, integrates it with the behavioral data, and analyzes it. For example, it suggests a route that allows for light exercise to aid digestion after a meal. This makes it possible to suggest travel routes based on health data.

[0032] The behavioral data collection unit analyzes a user's purchasing history, identifies the relationship between purchasing behavior and movement patterns, and can make travel suggestions based on the purchasing behavior. The behavioral data collection unit, for example, analyzes a user's online shopping history and identifies the relationship between purchasing behavior and movement patterns. For example, it predicts the places the user will visit after purchasing a specific product. The behavioral data collection unit also analyzes the user's store purchasing history and identifies the relationship between purchasing behavior and movement patterns. For example, it predicts other stores the user will visit after making a purchase at a specific store. The behavioral data collection unit also analyzes the user's purchasing history in combination with location information to make travel suggestions based on the purchasing behavior. For example, it suggests the optimal route for purchasing a specific product. This makes it possible to make travel suggestions based on purchasing behavior.

[0033] The behavioral data collection unit anonymizes the user's movement data and analyzes movement trends across an entire region, which can be useful for urban planning or traffic management. The behavioral data collection unit, for example, anonymizes the user's location information data and analyzes movement trends across an entire region. For example, it identifies areas that are congested during specific time periods and uses this information for traffic management. The behavioral data collection unit also performs simulations for urban planning based on the anonymized movement data. For example, it considers locations for new roads and public transportation. The behavioral data collection unit also analyzes movement trends across an entire region and uses this information to help control people flow when holding events. For example, it suggests routes to avoid congestion during large-scale events. This allows analysis of movement trends across an entire region to be useful for urban planning and traffic management.

[0034] The suggestion unit can propose individually customized routes based on the user's past movement history. For example, the suggestion unit analyzes the user's past movement history to identify frequently used routes and places visited. For example, the suggestion unit proposes an optimal route based on a commute route or a favorite cafe. The suggestion unit also learns the user's movement patterns based on the past movement history and proposes individually customized routes. For example, it proposes routes according to specific days of the week or time periods. The suggestion unit also analyzes a combination of the user's past movement history and current location information to propose an optimal route in real time. For example, it proposes the shortest route from the current location to the destination. This makes it possible to propose individually customized routes based on the user's past movement history.

[0035] The suggestion unit can analyze weather data and suggest the optimal route or timing depending on the weather conditions. The suggestion unit, for example, collects weather data in real time and reflects it in the user's travel route. For example, it suggests a route with a roof when it rains. The suggestion unit also makes suggestions to adjust the timing of travel based on weather forecast data. For example, it urges the user to complete the travel before it rains. The suggestion unit also analyzes weather data in combination with the user's health data to suggest the optimal route or timing. For example, it suggests a route with a lot of shade on a hot day. This makes it possible to suggest the optimal route and timing depending on the weather conditions.

[0036] The suggestion unit can suggest the optimal route depending on the means of transportation. For example, the suggestion unit automatically detects the user's means of transportation and suggests the optimal route depending on that means of transportation. For example, if the user is walking, pedestrian-only roads are given priority. The suggestion unit also analyzes the user's past movement data to suggest routes depending on the means of transportation. For example, bicycle-only roads are suggested for a user who frequently uses bicycles. The suggestion unit also analyzes the user's means of transportation in combination with current traffic conditions to suggest the optimal route. For example, if the user is using a car, a route that avoids traffic jams is suggested. This makes it possible to suggest the optimal route depending on the user's means of transportation.

[0037] The suggestion unit can suggest a route that combines information about tourist spots or restaurants according to the destination. For example, the suggestion unit suggests a route that combines information about tourist spots and restaurants according to the user's destination. For example, it suggests a route that includes restaurants that are recommended for visiting tourist spots. The suggestion unit also suggests a route that combines event information related to the destination. For example, it suggests a route that includes events that are being held near the destination. The suggestion unit also suggests a route that combines spots related to the destination based on the user's interests and concerns. For example, it suggests a route that includes historical spots for a user who is interested in history. This makes it possible to suggest a route that combines information about tourist spots and restaurants according to the user's destination.

[0038] The incentive providing unit can analyze a user's past incentive usage history and provide individually customized incentives. For example, the incentive providing unit analyzes a user's past incentive usage history and provides discount coupons for specific stores or services. For example, it may provide coupons for stores that the user has used in the past with priority. The incentive providing unit also provides incentives tailored to the user's preferences based on the incentive usage history. For example, it may provide coupons for specific genres. The incentive providing unit also combines and analyzes the user's incentive usage history with current behavioral data to provide optimal incentives in real time. For example, it may provide coupons for stores near the user's current location. This makes it possible to provide individually customized incentives based on the user's past incentive usage history.

[0039] The incentive providing unit can provide discount coupons for specific stores in conjunction with the user's purchase history. The incentive providing unit, for example, analyzes the user's purchase history and provides discount coupons for specific stores. For example, it provides coupons related to products purchased in the past. The incentive providing unit also analyzes a combination of the purchase history and location information and provides discount coupons that can be used in stores near the user's current location. For example, it provides coupons for stores close to the user's current location. The incentive providing unit also provides information about specific campaigns and sales based on the user's purchase history. For example, if a product purchased in the past is on sale, it notifies the user of this information. This makes it possible to provide discount coupons for specific stores based on the user's purchase history.

[0040] The incentive providing unit can provide points that can be earned by traveling a specific route based on the user's movement history. The incentive providing unit, for example, analyzes the user's movement history and provides a system in which points can be accumulated by traveling a specific route. For example, points can be awarded for traveling a route that avoids congestion. The incentive providing unit also analyzes a combination of the movement history and current location information to suggest a route that will earn points in real time. For example, points can be awarded for traveling the optimal route from the current location to the destination. The incentive providing unit also provides points that can be earned by traveling through a specific area based on the user's movement history. For example, points can be awarded for traveling through tourist spots or commercial facilities. This makes it possible to provide points that can be earned by traveling a specific route based on the user's movement history.

[0041] The incentive providing unit can cooperate with the user's friends or family to provide incentives for group travel. The incentive providing unit, for example, cooperates with the user's friends or family to provide incentives for group travel. For example, points are awarded for traveling a specific route as a group. The incentive providing unit also analyzes the group travel history and provides incentives that can be obtained by traveling through a specific area. For example, a discount coupon is provided for visiting a tourist spot with family. The incentive providing unit also cooperates with the user's friends or family to provide special incentives for group travel. For example, special events or services are provided for group travel. This makes it possible to cooperate with the user's friends or family to provide incentives for group travel.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The behavioral data collection unit can collect user voice data and identify the user's behavioral patterns using voice analysis technology. For example, it can analyze the voice patterns emitted by the user in a specific location and infer the user's behavior in that location. The behavioral data collection unit can also identify behavioral patterns during specific time periods based on the user's voice data. For example, it can analyze voice data during the morning commute to infer the user's commuting route. The behavioral data collection unit can also combine and analyze voice data with other behavioral data to identify behavioral patterns with greater accuracy. For example, it can combine voice data with location information to identify the user's tendency to visit certain places. This makes it possible to identify behavioral patterns using voice data.

[0044] The analysis unit can combine and analyze user behavior data and weather data to identify behavior patterns that correspond to the weather. For example, the analysis unit can analyze the tendency of routes chosen by users on rainy days and suggest the optimal route for rainy weather. The analysis unit can also identify behavior patterns under specific weather conditions based on weather data and behavior data. For example, the analysis unit can analyze the tendency of routes chosen by users on hot days and suggest routes with more shade. The analysis unit can also combine and analyze weather data with other behavior data to identify behavior patterns with greater accuracy. For example, the analysis unit can combine weather data with location information to identify the tendency of places visited by users. This makes it possible to identify behavior patterns that correspond to the weather.

[0045] The behavioral data collection unit can analyze a user's purchasing history, identify the relationship between purchasing behavior and movement patterns, and make travel suggestions based on the purchasing behavior. For example, the behavioral data collection unit can analyze a user's online shopping history and identify the relationship between purchasing behavior and movement patterns. For example, it can predict the places the user will visit after purchasing a specific product. The behavioral data collection unit can also analyze a user's store purchasing history and identify the relationship between purchasing behavior and movement patterns. For example, it can predict other stores the user will visit after purchasing at a specific store. The behavioral data collection unit can also analyze a user's purchasing history in combination with location information to make travel suggestions based on the user's purchasing behavior. For example, it can suggest the optimal route for purchasing a specific product. This makes it possible to make travel suggestions based on purchasing behavior.

[0046] The suggestion unit can propose individually customized routes based on the user's past movement history. For example, it can analyze the user's past movement history to identify frequently used routes and places visited. For example, it can propose an optimal route based on a user's commute route or favorite cafe. The suggestion unit can also learn the user's movement patterns based on the user's past movement history and propose individually customized routes. For example, it can propose routes according to specific days of the week or time periods. The suggestion unit can also combine and analyze the user's past movement history and current location information to propose an optimal route in real time. For example, it can propose the shortest route from the user's current location to the destination. This makes it possible to propose individually customized routes based on the user's past movement history.

[0047] The suggestion unit can analyze weather data and suggest the optimal route or timing depending on the weather conditions. For example, it can collect weather data in real time and reflect it in the user's travel route. For example, it can suggest a route with a roof when it is raining. The suggestion unit can also make suggestions to adjust the timing of travel based on weather forecast data. For example, it can urge the user to complete the travel before it rains. The suggestion unit can also analyze weather data in combination with the user's health data and suggest the optimal route or timing. For example, it can suggest a route with a lot of shade on a hot day. This makes it possible to suggest the optimal route and timing depending on the weather conditions.

[0048] The incentive providing unit can provide discount coupons for specific stores in conjunction with the user's purchase history. For example, it can analyze the user's purchase history and provide discount coupons for specific stores. For example, it can provide coupons related to products purchased in the past. The incentive providing unit can also combine and analyze the purchase history and location information to provide discount coupons that can be used in stores near the user's current location. For example, it can provide coupons for stores close to the user's current location. The incentive providing unit can also provide information about specific campaigns and sales based on the user's purchase history. For example, if a product purchased in the past is on sale, it can notify the user of this information. This makes it possible to provide discount coupons for specific stores based on the user's purchase history.

[0049] The incentive providing unit can cooperate with the user's friends or family to provide incentives for group travel. For example, it can cooperate with the user's friends or family to provide incentives for group travel. For example, points can be awarded for traveling a specific route as a group. The incentive providing unit can also analyze the group travel history and provide incentives that can be obtained by traveling through a specific area. For example, a discount coupon can be provided for visiting a tourist spot with family. The incentive providing unit can also cooperate with the user's friends or family to provide special incentives for group travel. For example, special events or services can be provided for group travel. This makes it possible to cooperate with the user's friends or family to provide incentives for group travel.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The behavioral data collection unit collects user behavioral data. For example, it collects location information, movement history, activity log, etc. The behavioral data collection unit can also collect data using sensors in the smartphone. Step 2: The analysis unit analyzes the data collected by the behavioral data collection unit. For example, the analysis unit analyzes user behavioral trends using data mining techniques. The analysis unit can also predict congestion levels using statistical analysis techniques. Step 3: The suggestion unit suggests appropriate routes and timings based on the data analyzed by the analysis unit. For example, it suggests routes that avoid areas where congestion is expected. The suggestion unit can also suggest the optimal means of transportation based on the user's destination and current location. Step 4: The incentive providing unit provides an incentive to the user who follows the route and timing suggested by the suggestion unit. For example, points may be awarded for traveling a specific route. The incentive providing unit may also provide a discount coupon for traveling during a less crowded time slot.

[0052] (Example 2) The travel motivation control app according to an embodiment of the present invention is a system that collects and analyzes user behavior data and suggests appropriate timing and routes to ensure smooth pedestrian flow. This allows the travel motivation control app to efficiently manage people's movements within a specific area and avoid congestion.

[0053] A travel motivation control app according to an embodiment includes a behavioral data collection unit, an analysis unit, a suggestion unit, and an incentive provision unit. The behavioral data collection unit collects user behavioral data, such as location information, movement history, and activity log. The behavioral data collection unit can also collect data using a smartphone sensor. The analysis unit analyzes the data collected by the behavioral data collection unit. For example, the analysis unit can analyze user behavioral trends using data mining technology. The analysis unit can also predict congestion using statistical analysis techniques. The suggestion unit suggests appropriate travel routes and timings based on the data analyzed by the analysis unit. For example, the suggestion unit can suggest routes that avoid areas where congestion is expected. The suggestion unit can also suggest the optimal means of transportation based on the user's destination and current location. The incentive provision unit provides incentives to users who follow the route and timing suggested by the suggestion unit. For example, points can be awarded for traveling a specific route. The incentive provision unit can also provide discount coupons for traveling during times when congestion is avoided. This allows the travel motivation control app to suggest appropriate travel routes and timings based on user behavior data and provide incentives, thereby ensuring smooth pedestrian flow.

[0054] The behavioral data collection unit collects emotional data from the user, analyzes the emotional state using an emotion estimation function, and identifies a behavioral pattern based on the emotional state. The behavioral data collection unit, for example, uses sensors on the user's smartphone to collect heart rate and facial expression data and analyzes the emotional state using an emotion estimation algorithm. For example, it estimates stress or relaxation from heart rate fluctuations and facial expressions. The behavioral data collection unit also analyzes the user's touch patterns and input speed when using an app and identifies the emotional state using the emotion estimation function. For example, if the user types quickly, it determines that the user is impatient, and if the user types slowly, it determines that the user is relaxed. The behavioral data collection unit also analyzes the content of the user's social media posts and identifies the emotional state using the emotion estimation function. For example, if there are many positive posts, it determines that the user is feeling happy, and if there are many negative posts, it determines that the user is feeling stressed. This allows the system to identify behavioral patterns based on the user's emotional state, enabling more accurate suggestions of travel routes and timings.

[0055] The behavioral data collection unit analyzes a user's social media activity and combines it with the behavioral data to understand behavioral trends. The behavioral data collection unit, for example, analyzes the content and frequency of a user's social media posts and combines it with the behavioral data to identify behavioral trends. For example, it identifies the frequency of participation in specific events and topics of interest. The behavioral data collection unit also analyzes friendships and follower trends on social media to identify the user's behavioral trends. For example, it infers the user's interests based on places visited by friends and events attended. The behavioral data collection unit also analyzes social media check-in data to identify the user's movement patterns. For example, if a user frequently checks in to a specific location, it determines that the location is an important spot for the user. This allows for more detailed behavioral trends to be identified by analyzing social media activity.

[0056] The behavioral data collection unit collects the user's health data, integrates it with the behavioral data, and analyzes it to suggest travel routes based on the user's health status. The behavioral data collection unit, for example, collects heart rate and step count data from the user's smartwatch or fitness tracker, and integrates it with the behavioral data for analysis. For example, if the heart rate is high, it suggests a relaxing route. The behavioral data collection unit also collects sleep data from the user's health app, integrates it with the behavioral data, and analyzes it. For example, if the user is sleep deprived, it suggests a route that can be traveled in a short amount of time. The behavioral data collection unit also collects the user's food record data, integrates it with the behavioral data, and analyzes it. For example, it suggests a route that allows for light exercise to aid digestion after a meal. This makes it possible to suggest travel routes based on health data.

[0057] The behavioral data collection unit analyzes a user's purchasing history, identifies the relationship between purchasing behavior and movement patterns, and can make travel suggestions based on the purchasing behavior. The behavioral data collection unit, for example, analyzes a user's online shopping history and identifies the relationship between purchasing behavior and movement patterns. For example, it predicts the places the user will visit after purchasing a specific product. The behavioral data collection unit also analyzes the user's store purchasing history and identifies the relationship between purchasing behavior and movement patterns. For example, it predicts other stores the user will visit after making a purchase at a specific store. The behavioral data collection unit also analyzes the user's purchasing history in combination with location information to make travel suggestions based on the purchasing behavior. For example, it suggests the optimal route for purchasing a specific product. This makes it possible to make travel suggestions based on purchasing behavior.

[0058] The behavioral data collection unit anonymizes the user's movement data and analyzes movement trends across an entire region, which can be useful for urban planning or traffic management. The behavioral data collection unit, for example, anonymizes the user's location information data and analyzes movement trends across an entire region. For example, it identifies areas that are congested during specific time periods and uses this information for traffic management. The behavioral data collection unit also performs simulations for urban planning based on the anonymized movement data. For example, it considers locations for new roads and public transportation. The behavioral data collection unit also analyzes movement trends across an entire region and uses this information to help control people flow when holding events. For example, it suggests routes to avoid congestion during large-scale events. This allows analysis of movement trends across an entire region to be useful for urban planning and traffic management.

[0059] The behavioral data collection unit can use the emotion estimation function to analyze the emotions a user has toward a specific area and suggest a route that will elicit positive emotions. The behavioral data collection unit, for example, combines and analyzes the user's location information and emotion data to identify emotions toward a specific area. For example, it suggests a route that will elicit positive emotions based on emotion data from past visits. The behavioral data collection unit also uses the emotion estimation function to analyze the emotions a user has toward a specific area in real time. For example, it suggests a relaxing route based on the user's current emotional state. The behavioral data collection unit also creates an emotion map for a specific area based on the user's emotion data and suggests a route that will elicit positive emotions. For example, it suggests a route that includes spots that the user can enjoy based on the emotion map. This improves user satisfaction by suggesting a route that will elicit positive emotions toward a specific area.

[0060] The suggestion unit can propose individually customized routes based on the user's past movement history. For example, the suggestion unit analyzes the user's past movement history to identify frequently used routes and places visited. For example, the suggestion unit proposes an optimal route based on a commute route or a favorite cafe. The suggestion unit also learns the user's movement patterns based on the past movement history and proposes individually customized routes. For example, it proposes routes according to specific days of the week or time periods. The suggestion unit also analyzes a combination of the user's past movement history and current location information to propose an optimal route in real time. For example, it proposes the shortest route from the current location to the destination. This makes it possible to propose individually customized routes based on the user's past movement history.

[0061] The suggestion unit can analyze weather data and suggest the optimal route or timing depending on the weather conditions. The suggestion unit, for example, collects weather data in real time and reflects it in the user's travel route. For example, it suggests a route with a roof when it rains. The suggestion unit also makes suggestions to adjust the timing of travel based on weather forecast data. For example, it urges the user to complete the travel before it rains. The suggestion unit also analyzes weather data in combination with the user's health data to suggest the optimal route or timing. For example, it suggests a route with a lot of shade on a hot day. This makes it possible to suggest the optimal route and timing depending on the weather conditions.

[0062] The suggestion unit can analyze the emotional state in real time and suggest a route or timing according to the emotion. For example, the suggestion unit analyzes the user's emotional state in real time and suggests a relaxing route if the user is highly stressed. For example, it may select a route that includes parks and lots of nature. The suggestion unit also suggests adjusting the timing of travel according to the emotional state. For example, if the user is tired, it may suggest a route that includes breaks. The suggestion unit also suggests a route that elicits positive emotions based on the user's emotional data. For example, it may suggest a route that includes places the user has visited in the past and enjoyed. This makes it possible to suggest routes and timing according to the user's emotional state.

[0063] The suggestion unit can suggest the optimal route depending on the means of transportation. For example, the suggestion unit automatically detects the user's means of transportation and suggests the optimal route depending on that means of transportation. For example, if the user is walking, pedestrian-only roads are given priority. The suggestion unit also analyzes the user's past movement data to suggest routes depending on the means of transportation. For example, bicycle-only roads are suggested for a user who frequently uses bicycles. The suggestion unit also analyzes the user's means of transportation in combination with current traffic conditions to suggest the optimal route. For example, if the user is using a car, a route that avoids traffic jams is suggested. This makes it possible to suggest the optimal route depending on the user's means of transportation.

[0064] The suggestion unit can suggest a route that combines information about tourist spots or restaurants according to the destination. For example, the suggestion unit suggests a route that combines information about tourist spots and restaurants according to the user's destination. For example, it suggests a route that includes restaurants that are recommended for visiting tourist spots. The suggestion unit also suggests a route that combines event information related to the destination. For example, it suggests a route that includes events that are being held near the destination. The suggestion unit also suggests a route that combines spots related to the destination based on the user's interests and concerns. For example, it suggests a route that includes historical spots for a user who is interested in history. This makes it possible to suggest a route that combines information about tourist spots and restaurants according to the user's destination.

[0065] The suggestion unit can use the emotion estimation function to suggest a route or timing where the user can relax. For example, the suggestion unit uses the emotion estimation function to suggest a route where the user can relax. For example, it may select a park with lots of nature or a quiet road. The suggestion unit also analyzes the user's emotional state in real time to suggest a timing where the user can relax. For example, it may select a time period with less congestion. The suggestion unit also suggests a route or timing where the user can relax based on the user's past emotion data. For example, it may suggest a route that includes places that the user has visited in the past where they were able to relax. This makes it possible to suggest a route or timing where the user can relax.

[0066] The incentive providing unit can analyze a user's past incentive usage history and provide individually customized incentives. For example, the incentive providing unit analyzes a user's past incentive usage history and provides discount coupons for specific stores or services. For example, it may provide coupons for stores that the user has used in the past with priority. The incentive providing unit also provides incentives tailored to the user's preferences based on the incentive usage history. For example, it may provide coupons for specific genres. The incentive providing unit also combines and analyzes the user's incentive usage history with current behavioral data to provide optimal incentives in real time. For example, it may provide coupons for stores near the user's current location. This makes it possible to provide individually customized incentives based on the user's past incentive usage history.

[0067] The incentive providing unit can provide discount coupons for specific stores in conjunction with the user's purchase history. The incentive providing unit, for example, analyzes the user's purchase history and provides discount coupons for specific stores. For example, it provides coupons related to products purchased in the past. The incentive providing unit also analyzes a combination of the purchase history and location information and provides discount coupons that can be used in stores near the user's current location. For example, it provides coupons for stores close to the user's current location. The incentive providing unit also provides information about specific campaigns and sales based on the user's purchase history. For example, if a product purchased in the past is on sale, it notifies the user of this information. This makes it possible to provide discount coupons for specific stores based on the user's purchase history.

[0068] The incentive providing unit can analyze the emotional state of the user and provide incentives that elicit positive emotions. The incentive providing unit, for example, analyzes the emotional state of the user in real time and provides incentives that elicit positive emotions. For example, it provides coupons for places where the user can relax when stress is high. The incentive providing unit also uses the emotion estimation function to identify and provide incentives that will make the user feel positive emotions. For example, it provides coupons for services that the user has used in the past and that the user was highly satisfied with. The incentive providing unit also customizes incentives to elicit positive emotions based on the user's emotional data. For example, it provides coupons for places or services with high emotion scores. This makes it possible to provide incentives that elicit positive emotions based on the user's emotional state.

[0069] The incentive providing unit can provide points that can be earned by traveling a specific route based on the user's movement history. The incentive providing unit, for example, analyzes the user's movement history and provides a system in which points can be accumulated by traveling a specific route. For example, points can be awarded for traveling a route that avoids congestion. The incentive providing unit also analyzes a combination of the movement history and current location information to suggest a route that will earn points in real time. For example, points can be awarded for traveling the optimal route from the current location to the destination. The incentive providing unit also provides points that can be earned by traveling through a specific area based on the user's movement history. For example, points can be awarded for traveling through tourist spots or commercial facilities. This makes it possible to provide points that can be earned by traveling a specific route based on the user's movement history.

[0070] The incentive providing unit can cooperate with the user's friends or family to provide incentives for group travel. The incentive providing unit, for example, cooperates with the user's friends or family to provide incentives for group travel. For example, points are awarded for traveling a specific route as a group. The incentive providing unit also analyzes the group travel history and provides incentives that can be obtained by traveling through a specific area. For example, a discount coupon is provided for visiting a tourist spot with family. The incentive providing unit also cooperates with the user's friends or family to provide special incentives for group travel. For example, special events or services are provided for group travel. This makes it possible to cooperate with the user's friends or family to provide incentives for group travel.

[0071] The incentive providing unit can use the emotion estimation function to identify and provide the incentive that the user will be most pleased with. For example, the incentive providing unit uses the emotion estimation function to identify and provide the incentive that the user will be most pleased with. For example, the incentive providing unit provides a coupon for a service that the user has used in the past and that the user has been very satisfied with. The incentive providing unit also analyzes the user's emotional state in real time to provide the incentive that the user will be most pleased with. For example, the incentive providing unit provides a coupon for a place where the user can relax when the user is under a lot of stress. The incentive providing unit also customizes the incentive that the user will be most pleased with based on the emotion data. For example, the incentive providing unit provides a coupon for a place or service with a high emotion score. This makes it possible to provide the incentive that the user will be most pleased with.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The behavioral data collection unit can collect user voice data and identify the user's behavioral patterns using voice analysis technology. For example, it can analyze the voice patterns emitted by the user in a specific location and infer the user's behavior in that location. The behavioral data collection unit can also identify behavioral patterns during specific time periods based on the user's voice data. For example, it can analyze voice data during the morning commute to infer the user's commuting route. The behavioral data collection unit can also combine and analyze voice data with other behavioral data to identify behavioral patterns with greater accuracy. For example, it can combine voice data with location information to identify the user's tendency to visit certain places. This makes it possible to identify behavioral patterns using voice data.

[0074] The analysis unit can combine and analyze user behavior data and weather data to identify behavior patterns that correspond to the weather. For example, the analysis unit can analyze the tendency of routes chosen by users on rainy days and suggest the optimal route for rainy weather. The analysis unit can also identify behavior patterns under specific weather conditions based on weather data and behavior data. For example, the analysis unit can analyze the tendency of routes chosen by users on hot days and suggest routes with more shade. The analysis unit can also combine and analyze weather data with other behavior data to identify behavior patterns with greater accuracy. For example, the analysis unit can combine weather data with location information to identify the tendency of places visited by users. This makes it possible to identify behavior patterns that correspond to the weather.

[0075] The suggestion unit can estimate the user's emotional state and suggest music that helps the user relax based on the estimated emotion. For example, if the user is feeling stressed, it can suggest relaxing music. The suggestion unit can also suggest music of a specific genre depending on the user's emotional state. For example, if the user is tired, it can suggest relaxing classical music. The suggestion unit can also suggest music that has been relaxing in the past based on the user's emotional data. For example, it can suggest a playlist of music that has been relaxing in the past. This makes it possible to suggest music that suits the user's emotional state.

[0076] The incentive providing unit can analyze the user's emotional state and provide incentives to elicit positive emotions. For example, if the user is feeling stressed, the incentive providing unit can provide coupons for places where the user can relax. The incentive providing unit can also use the emotion estimation function to identify and provide incentives that will evoke positive emotions in the user. For example, the incentive providing unit can provide coupons for services that the user has used in the past and that the user was highly satisfied with. The incentive providing unit can also customize incentives to elicit positive emotions based on the user's emotional data. For example, the incentive providing unit can provide coupons for places or services with high emotion scores. This makes it possible to provide incentives that elicit positive emotions based on the user's emotional state.

[0077] The suggestion unit can analyze the user's emotional state in real time and suggest a route or timing that matches the emotion. For example, if the user is feeling stressed, it can suggest a relaxing route. For example, it can select a route that goes through parks and lots of nature. The suggestion unit can also make suggestions to adjust the timing of travel according to the emotional state. For example, if the user is tired, it can suggest a route that includes breaks. The suggestion unit can also suggest a route that brings out positive emotions based on the user's emotional data. For example, it can suggest a route that includes places that the user has visited and enjoyed in the past. This makes it possible to suggest routes and timing that match the user's emotional state.

[0078] The behavioral data collection unit can analyze a user's purchasing history, identify the relationship between purchasing behavior and movement patterns, and make travel suggestions based on the purchasing behavior. For example, the behavioral data collection unit can analyze a user's online shopping history and identify the relationship between purchasing behavior and movement patterns. For example, it can predict the places the user will visit after purchasing a specific product. The behavioral data collection unit can also analyze a user's store purchasing history and identify the relationship between purchasing behavior and movement patterns. For example, it can predict other stores the user will visit after purchasing at a specific store. The behavioral data collection unit can also analyze a user's purchasing history in combination with location information to make travel suggestions based on the user's purchasing behavior. For example, it can suggest the optimal route for purchasing a specific product. This makes it possible to make travel suggestions based on purchasing behavior.

[0079] The suggestion unit can propose individually customized routes based on the user's past movement history. For example, it can analyze the user's past movement history to identify frequently used routes and places visited. For example, it can propose an optimal route based on a user's commute route or favorite cafe. The suggestion unit can also learn the user's movement patterns based on the user's past movement history and propose individually customized routes. For example, it can propose routes according to specific days of the week or time periods. The suggestion unit can also combine and analyze the user's past movement history and current location information to propose an optimal route in real time. For example, it can propose the shortest route from the user's current location to the destination. This makes it possible to propose individually customized routes based on the user's past movement history.

[0080] The suggestion unit can analyze weather data and suggest the optimal route or timing depending on the weather conditions. For example, it can collect weather data in real time and reflect it in the user's travel route. For example, it can suggest a route with a roof when it is raining. The suggestion unit can also make suggestions to adjust the timing of travel based on weather forecast data. For example, it can urge the user to complete the travel before it rains. The suggestion unit can also analyze weather data in combination with the user's health data and suggest the optimal route or timing. For example, it can suggest a route with a lot of shade on a hot day. This makes it possible to suggest the optimal route and timing depending on the weather conditions.

[0081] The incentive providing unit can provide discount coupons for specific stores in conjunction with the user's purchase history. For example, it can analyze the user's purchase history and provide discount coupons for specific stores. For example, it can provide coupons related to products purchased in the past. The incentive providing unit can also combine and analyze the purchase history and location information to provide discount coupons that can be used in stores near the user's current location. For example, it can provide coupons for stores close to the user's current location. The incentive providing unit can also provide information about specific campaigns and sales based on the user's purchase history. For example, if a product purchased in the past is on sale, it can notify the user of this information. This makes it possible to provide discount coupons for specific stores based on the user's purchase history.

[0082] The incentive providing unit can cooperate with the user's friends or family to provide incentives for group travel. For example, it can cooperate with the user's friends or family to provide incentives for group travel. For example, points can be awarded for traveling a specific route as a group. The incentive providing unit can also analyze the group travel history and provide incentives that can be obtained by traveling through a specific area. For example, a discount coupon can be provided for visiting a tourist spot with family. The incentive providing unit can also cooperate with the user's friends or family to provide special incentives for group travel. For example, special events or services can be provided for group travel. This makes it possible to cooperate with the user's friends or family to provide incentives for group travel.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The behavioral data collection unit collects user behavioral data. For example, it collects location information, movement history, activity log, etc. The behavioral data collection unit can also collect data using sensors in the smartphone. Step 2: The analysis unit analyzes the data collected by the behavioral data collection unit. For example, the analysis unit analyzes user behavioral trends using data mining techniques. The analysis unit can also predict congestion levels using statistical analysis techniques. Step 3: The suggestion unit suggests appropriate routes and timings based on the data analyzed by the analysis unit. For example, it suggests routes that avoid areas where congestion is expected. The suggestion unit can also suggest the optimal means of transportation based on the user's destination and current location. Step 4: The incentive providing unit provides an incentive to the user who follows the route and timing suggested by the suggestion unit. For example, points may be awarded for traveling a specific route. The incentive providing unit may also provide a discount coupon for traveling during a less crowded time slot.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a behavioral data collection unit that collects user behavioral data; an analysis unit that analyzes the data collected by the behavioral data collection unit; a suggestion unit that suggests an appropriate travel route or timing based on the data analyzed by the analysis unit; an incentive providing unit that provides an incentive to a user who follows the route or timing suggested by the suggestion unit. A system characterized by:

2. The behavioral data collection unit Collecting user emotional data, analyzing emotional states, and identifying behavioral patterns based on emotional states 2. The system of claim 1.

3. The behavioral data collection unit Analyze users' social media activity and combine it with behavioral data to understand behavioral trends 2. The system of claim 1.

4. The behavioral data collection unit Collects user health data, integrates it with behavioral data, and analyzes it to suggest travel options based on health status.

2. The system of claim 1.

5. The behavioral data collection unit Analyze users' purchasing history, identify the relationship between purchasing behavior and travel patterns, and make travel suggestions based on purchasing behavior.

2. The system of claim 1.

6. The behavioral data collection unit Anonymize user movement data and analyze movement trends across regions to aid in urban planning or traffic management.

2. The system of claim 1.

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