System

Abstract

An object of a system according to an embodiment is to propose an optimal store on the basis of a behavior pattern or a preference of a user and to easily make a reservation.SOLUTION: A system according to an embodiment includes a data integration unit, an action analysis unit, a proposal unit, and a reservation unit. The data integration unit integrates data collected from the user's smartphone. The behavior analysis unit analyzes the data integrated by the data integration unit and specifies a behavior pattern or a preference of the user. The suggestion unit suggests an optimal restaurant or shop on the basis of the behavior pattern or the preference specified by the behavior analysis unit. The reservation unit easily reserves the store 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 do not adequately suggest optimal stores based on a user's behavioral patterns and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest the most suitable store based on the user's behavioral patterns and preferences, and to allow easy reservation. [Means for solving the problem]

[0006] The system according to the embodiment includes a data integration unit, a behavior analysis unit, a suggestion unit, and a reservation unit. The data integration unit integrates data collected from the user's smartphone. The behavior analysis unit analyzes the data integrated by the data integration unit and identifies the user's behavioral patterns and preferences. The suggestion unit suggests optimal restaurants and shops based on the behavioral patterns and preferences identified by the behavior analysis unit. The reservation unit easily makes reservations at stores suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment suggests the most suitable store based on the user's behavioral patterns and preferences, and allows for easy reservation. [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 matching suggestion system according to an embodiment of the present invention integrates data collected from a user's smartphone, analyzes it using a generation AI, and suggests optimal restaurants and shops based on the user's behavioral patterns and preferences, allowing for easy reservations. This allows the matching suggestion system to easily find new favorite restaurants and make reservations. Furthermore, by accumulating customer data, stores can conduct highly accurate targeted marketing, thereby improving their ability to attract customers and increasing revenue and customer satisfaction. Furthermore, with the user's permission, the system can match data from other users with similar preferences to realize even more perfect suggestions.

[0029] A matching suggestion system according to an embodiment includes a data integration unit, a behavior analysis unit, a suggestion unit, and a reservation unit. The data integration unit integrates data collected from a user's smartphone. For example, the data integration unit integrates location information, app usage history, search history, purchase history, and social media postings. The data integration unit can also convert data formats and eliminate duplicate data. The behavior analysis unit analyzes the data integrated by the data integration unit to identify the user's behavioral patterns and preferences. For example, the behavior analysis unit identifies the user's behavioral patterns using frequency analysis or time series analysis. The behavior analysis unit can also identify preferences based on past selection history and user feedback. The suggestion unit suggests optimal restaurants and shops based on the behavioral patterns and preferences identified by the behavior analysis unit. For example, the suggestion unit can suggest optimal stores to the user using a recommendation algorithm. The suggestion unit can also make suggestions taking into account the user's evaluation criteria. The reservation unit easily reserves the stores suggested by the suggestion unit. For example, the reservation unit can link with a reservation system to confirm the reservation. The reservation unit can also provide the user with a method for confirming the reservation. As a result, the matching suggestion system according to the embodiment can suggest the most suitable store based on the user's behavioral patterns and preferences, and allow for easy reservation.

[0030] The data integration unit can integrate the user's health data and make suggestions based on their health condition. For example, the data integration unit uses a generation AI to analyze health data such as the number of steps and heart rate collected from the user's smartphone and make suggestions based on their health condition. For example, it can suggest restaurants that serve healthy meals. The data integration unit can also analyze the user's exercise habits and lifestyle based on the health data and suggest stores that suit their health condition. For example, it can suggest fitness gyms and health food stores. The data integration unit can also analyze the user's health data over time and make suggestions based on fluctuations in their health condition. For example, it can suggest stores that encourage activity during periods when exercise is low. This allows for suggestions based on the user's health condition, enabling them to make healthier choices.

[0031] The data integration unit can analyze the user's past behavioral data over time to identify changes in preferences by season or time of day. For example, the data integration unit analyzes the user's past behavioral data over time to identify changes in preferences by season. For example, in the summer, it suggests stores that serve cold drinks. The data integration unit also analyzes the behavioral data by time of day to identify changes in preferences during specific time periods. For example, it suggests restaurants with extensive lunch menus at lunchtime. The data integration unit also analyzes the user's behavioral data over a long period of time to identify patterns of preferences by season or time of day. For example, it suggests cafes that serve hot drinks in the winter. This makes it possible to identify changes in preferences by season or time of day, thereby enabling more appropriate suggestions.

[0032] The data integration unit can also integrate data from other wearable devices, such as smartwatches and fitness trackers, to analyze behavioral patterns in more detail. The data integration unit integrates data collected from, for example, smartwatches and fitness trackers, and the generative AI analyzes the user's behavioral patterns in detail. For example, it makes suggestions based on exercise volume and sleep patterns. The data integration unit also analyzes the user's health and activity level based on data from wearable devices and suggests appropriate stores. For example, it might suggest sporting goods stores for active users. The data integration unit also integrates data from other wearable devices to analyze the user's behavioral patterns from multiple angles. For example, it might suggest stores where the user can relax based on heart rate and stress level. This makes it possible to analyze behavioral patterns in more detail by integrating data from other wearable devices.

[0033] The behavior analysis unit can analyze the user's social network data and make suggestions that also take into account the preferences of friends and followers. The behavior analysis unit, for example, analyzes the user's social network data and makes suggestions that also take into account the preferences of friends and followers. For example, it can suggest restaurants that friends often visit. The behavior analysis unit also identifies the user's preferences based on the social network data and makes suggestions that also reflect the preferences of friends and followers. For example, it can suggest stores that share common interests. The behavior analysis unit also analyzes the user's social network data and builds a system that makes suggestions that also take into account the preferences of friends and followers. For example, it can select stores based on friends' reviews and ratings. This allows for more appropriate suggestions by also taking into account the preferences of friends and followers.

[0034] The behavior analysis unit can analyze the user's music streaming data and suggest stores based on the user's music preferences. The behavior analysis unit, for example, analyzes the user's music streaming data and suggests stores based on the user's music preferences. For example, to a user who likes a particular genre of music, the behavior analysis unit suggests cafes that play that genre of music. The behavior analysis unit also identifies the user's music preferences based on the music streaming data and suggests stores related to music. For example, it suggests restaurants where live music can be enjoyed. The behavior analysis unit also analyzes the user's music streaming data and builds a system that makes suggestions based on the user's music preferences. For example, it selects stores based on the music genre or artist. This makes it possible to suggest stores based on the user's music preferences.

[0035] The behavior analysis unit can analyze the user's travel history and make suggestions based on preferences at the travel destination. The behavior analysis unit, for example, analyzes the user's travel history and makes suggestions based on preferences at the travel destination. For example, it suggests stores with similar preferences based on behavioral data from travel destinations visited in the past. The behavior analysis unit also identifies the user's preferences based on the travel history and suggests the most suitable store at the travel destination. For example, it analyzes data related to meals and activities at the travel destination. The behavior analysis unit also analyzes the user's travel history and builds a system that makes suggestions based on preferences at the travel destination. For example, it selects stores based on reviews and ratings at the travel destination. This makes it possible to make suggestions based on preferences at the travel destination.

[0036] The reservation unit can analyze the user's calendar data and suggest the optimal reservation time based on the schedule. The reservation unit, for example, analyzes the user's calendar data and suggests the optimal reservation time based on the schedule. For example, it suggests making a reservation at a restaurant that matches an available time slot. The reservation unit also suggests the optimal reservation time taking the user's schedule into consideration based on the calendar data. For example, it suggests making a reservation at a cafe where you can relax after an important meeting. The reservation unit also builds a system that analyzes the user's calendar data and suggests reservation times based on the schedule. For example, it suggests the optimal reservation time between schedules. This makes it possible to make reservations that fit the user's schedule by suggesting the optimal reservation time based on the schedule.

[0037] The reservation unit can analyze the user's past reservation history and prioritize suggestions for reservations at frequently used restaurants. The reservation unit, for example, analyzes the user's past reservation history and prioritize suggestions for reservations at frequently used restaurants. For example, it suggests reservations at restaurants that the user frequently visits. The reservation unit also identifies the user's preferences based on the past reservation history and suggests frequently used restaurants. For example, it prioritizes suggestions for restaurants of a particular genre. The reservation unit also analyzes the user's past reservation history and builds a system that selects frequently used restaurants. For example, it suggests the most suitable restaurant based on reservation history data. In this way, by analyzing the past reservation history, it is possible to prioritize suggestions for reservations at frequently used restaurants.

[0038] The reservation unit can analyze data from a user's email or messaging app and automate communication regarding reservations. The reservation unit, for example, analyzes data from a user's email or messaging app and automates communication regarding reservations. For example, it automatically sends a reservation confirmation email. The reservation unit also builds a system that automates communication regarding reservations based on data from emails or messaging apps. For example, it automatically processes requests to change reservations. The reservation unit also analyzes data from a user's email or messaging app and automatically sends notifications and reminders regarding reservations. For example, it sends a reminder the day before the reservation. This automates communication regarding reservations, reducing the burden on users.

[0039] The reservation unit can analyze the user's location information and suggest a reservation at the store closest to the current location. The reservation unit, for example, analyzes the user's location information and suggests a reservation at the store closest to the current location. For example, it suggests restaurants within walking distance of the current location. The reservation unit also builds a system that suggests the most suitable store based on the user's current location based on the location information. For example, it suggests the nearest cafe to the current location. The reservation unit also analyzes the user's location information in real time and suggests a reservation based on the current location. For example, it checks the availability of the store closest to the current location and suggests a reservation. This improves user convenience by suggesting a reservation at the store closest to the current location.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The behavior analysis unit can analyze a user's hobbies and interests and suggest events and activities based on those hobbies. For example, if a user likes outdoor activities, nearby hiking trails and campsites can be suggested. The behavior analysis unit can also collect event information related to the user's hobbies and notify them at the appropriate time. For example, for a user who likes music, it can suggest nearby concerts and live events. The behavior analysis unit can also suggest related products and services based on the user's hobbies. For example, for a user who enjoys cooking, it can suggest cooking classes or new recipe books. This makes it possible to make suggestions based on the user's hobbies and interests, providing a more personalized experience.

[0042] The data integration unit can analyze a user's purchasing history and make suggestions based on purchasing patterns. For example, it can identify products and services that a user frequently purchases and suggest related stores. The data integration unit can also make suggestions related to specific seasons or events based on the user's purchasing history. For example, it can suggest gift shops and decoration supply stores during the Christmas season. The data integration unit can also analyze a user's purchasing history and suggest new products and services based on past purchase history. For example, it can suggest new products that go well with products purchased in the past. This makes it possible to provide suggestions based on the user's purchasing patterns, thereby providing more appropriate products and services.

[0043] The suggestion unit can analyze the user's travel history and make suggestions based on preferences at the travel destination. For example, it can suggest stores with similar preferences based on behavioral data from travel destinations visited in the past. The suggestion unit can also identify the user's preferences based on the travel history and suggest the most suitable store at the travel destination. For example, it can analyze data related to meals and activities at the travel destination. The suggestion unit can also analyze the user's travel history and build a system that makes suggestions based on preferences at the travel destination. For example, it can select stores based on reviews and ratings at the travel destination. This makes it possible to make suggestions based on preferences at the travel destination.

[0044] The data integration unit can analyze a user's music streaming data and suggest stores based on their music preferences. For example, it can suggest cafes that play music of a particular genre to a user who likes that genre. The data integration unit can also identify a user's music preferences based on the music streaming data and suggest stores related to music. For example, it can suggest restaurants where you can enjoy live music. The data integration unit can also analyze a user's music streaming data and build a system that makes suggestions based on their music preferences. For example, it can select stores based on music genres and artists. This makes it possible to suggest stores based on music preferences.

[0045] The reservation unit can analyze the user's calendar data and suggest the optimal reservation time based on the schedule. For example, it can suggest a restaurant reservation based on an available time slot. The reservation unit can also use the calendar data to suggest the optimal reservation time taking the user's schedule into consideration. For example, it can suggest a reservation at a cafe where you can relax after an important meeting. The reservation unit can also analyze the user's calendar data and build a system that suggests reservation times based on the schedule. For example, it can suggest the optimal reservation time between schedules. This allows the system to suggest the optimal reservation time based on the schedule, making it possible to make reservations that fit the user's schedule.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The data integration unit integrates data collected from the user's smartphone. For example, it integrates location information, app usage history, search history, purchase history, and social media postings. The data integration unit can also convert data formats and eliminate duplicate data. Step 2: The behavior analysis unit analyzes the data integrated by the data integration unit to identify the user's behavioral patterns and preferences. For example, the behavior analysis unit may use frequency analysis or time series analysis to identify the user's behavioral patterns. The behavior analysis unit may also identify preferences based on past selection history and user feedback. Step 3: The suggestion unit suggests optimal restaurants and shops based on the behavioral patterns and preferences identified by the behavior analysis unit. For example, it uses a recommendation algorithm to suggest optimal stores for the user. The suggestion unit can also make suggestions taking into account the user's evaluation criteria. Step 4: The reservation unit easily reserves the restaurant proposed by the proposal unit. For example, the reservation unit may link with a reservation system and confirm the reservation. The reservation unit may also provide the user with a method for confirming the reservation.

[0048] (Example 2) The matching suggestion system according to an embodiment of the present invention integrates data collected from a user's smartphone, analyzes it using a generation AI, and suggests optimal restaurants and shops based on the user's behavioral patterns and preferences, allowing for easy reservations. This allows the matching suggestion system to easily find new favorite restaurants and make reservations. Furthermore, by accumulating customer data, stores can conduct highly accurate targeted marketing, thereby improving their ability to attract customers and increasing revenue and customer satisfaction. Furthermore, with the user's permission, the system can match data from other users with similar preferences to realize even more perfect suggestions.

[0049] A matching suggestion system according to an embodiment includes a data integration unit, a behavior analysis unit, a suggestion unit, and a reservation unit. The data integration unit integrates data collected from a user's smartphone. For example, the data integration unit integrates location information, app usage history, search history, purchase history, and social media postings. The data integration unit can also convert data formats and eliminate duplicate data. The behavior analysis unit analyzes the data integrated by the data integration unit to identify the user's behavioral patterns and preferences. For example, the behavior analysis unit identifies the user's behavioral patterns using frequency analysis or time series analysis. The behavior analysis unit can also identify preferences based on past selection history and user feedback. The suggestion unit suggests optimal restaurants and shops based on the behavioral patterns and preferences identified by the behavior analysis unit. For example, the suggestion unit can suggest optimal stores to the user using a recommendation algorithm. The suggestion unit can also make suggestions taking into account the user's evaluation criteria. The reservation unit easily reserves the stores suggested by the suggestion unit. For example, the reservation unit can link with a reservation system to confirm the reservation. The reservation unit can also provide the user with a method for confirming the reservation. As a result, the matching suggestion system according to the embodiment can suggest the most suitable store based on the user's behavioral patterns and preferences, and allow for easy reservation.

[0050] The data integration unit can estimate the user's emotional state and weight the data based on emotional fluctuations. For example, the data integration unit uses a generation AI to estimate the user's emotional state in real time based on data collected from the user's smartphone. For example, it analyzes the content of social media posts and the context of messages to calculate an emotional score. The data integration unit also weights the data according to the user's emotional fluctuations based on the emotion estimation results. For example, it prioritizes analysis of data with strong positive emotions. The data integration unit also analyzes the user's emotional state over time to identify patterns of emotional fluctuation. For example, it analyzes emotional fluctuations during specific time periods or events and reflects this in the weighting of the data. This allows for more accurate suggestions by weighting the data based on the user's emotional state.

[0051] The data integration unit can integrate the user's health data and make suggestions based on their health condition. For example, the data integration unit uses a generation AI to analyze health data such as the number of steps and heart rate collected from the user's smartphone and make suggestions based on their health condition. For example, it can suggest restaurants that serve healthy meals. The data integration unit can also analyze the user's exercise habits and lifestyle based on the health data and suggest stores that suit their health condition. For example, it can suggest fitness gyms and health food stores. The data integration unit can also analyze the user's health data over time and make suggestions based on fluctuations in their health condition. For example, it can suggest stores that encourage activity during periods when exercise is low. This allows for suggestions based on the user's health condition, enabling them to make healthier choices.

[0052] The data integration unit can analyze the user's past behavioral data over time to identify changes in preferences by season or time of day. For example, the data integration unit analyzes the user's past behavioral data over time to identify changes in preferences by season. For example, in the summer, it suggests stores that serve cold drinks. The data integration unit also analyzes the behavioral data by time of day to identify changes in preferences during specific time periods. For example, it suggests restaurants with extensive lunch menus at lunchtime. The data integration unit also analyzes the user's behavioral data over a long period of time to identify patterns of preferences by season or time of day. For example, it suggests cafes that serve hot drinks in the winter. This makes it possible to identify changes in preferences by season or time of day, thereby enabling more appropriate suggestions.

[0053] The data integration unit can also integrate data from other wearable devices, such as smartwatches and fitness trackers, to analyze behavioral patterns in more detail. The data integration unit integrates data collected from, for example, smartwatches and fitness trackers, and the generative AI analyzes the user's behavioral patterns in detail. For example, it makes suggestions based on exercise volume and sleep patterns. The data integration unit also analyzes the user's health and activity level based on data from wearable devices and suggests appropriate stores. For example, it might suggest sporting goods stores for active users. The data integration unit also integrates data from other wearable devices to analyze the user's behavioral patterns from multiple angles. For example, it might suggest stores where the user can relax based on heart rate and stress level. This makes it possible to analyze behavioral patterns in more detail by integrating data from other wearable devices.

[0054] The data integration unit can analyze the user's voice data, infer emotions and interests from the voice, and reflect these in suggestions. For example, the data integration unit analyzes the user's voice data, and the generation AI infers emotions and interests. For example, it calculates an emotion score based on the tone and content of the conversation and reflects this in suggestions. The data integration unit also identifies the user's interests and concerns based on the voice data and suggests appropriate stores. For example, if the user is interested in a particular topic, it will suggest stores related to that topic. The data integration unit also analyzes the user's voice data over time to identify fluctuations in emotions and interests. For example, it makes suggestions based on emotional fluctuations during a specific time period. This makes it possible to make suggestions based on emotions and interests by analyzing voice data.

[0055] The data integration unit can use the emotion estimation function to analyze the emotions a user feels at a specific location or time of day and make suggestions based on the emotions. For example, the data integration unit uses the emotion estimation function to analyze the emotions a user feels at a specific location. For example, if the user is relaxing at a specific cafe, the data integration unit will suggest that cafe. The data integration unit also analyzes the emotions a user feels at a specific time of day and makes suggestions based on the emotions. For example, if the user wants to relax in the evening, the data integration unit will suggest a store where the user can relax. The data integration unit also identifies patterns of emotions a user feels at a specific location or time of day based on the emotion estimation data. For example, if the user wants to relax on the weekend, the data integration unit will suggest a store that is suitable for that time of day. In this way, by analyzing the emotions felt at a specific location or time of day, it becomes possible to make suggestions based on emotions.

[0056] The behavioral analysis unit can analyze the user's emotional state in real time and make store suggestions based on their emotions. For example, the behavioral analysis unit uses a generative AI to analyze the user's emotional state in real time and make store suggestions based on their emotions. For example, if the user is feeling stressed, the system can suggest a cafe where they can relax. The behavioral analysis unit also analyzes the user's emotional state and suggests stores based on their emotions. For example, if they are feeling very positive, the system can suggest a restaurant with a fun atmosphere. The behavioral analysis unit also analyzes the user's emotional state in real time and builds a system that makes suggestions based on their emotions. For example, the system can select the most suitable store based on their emotional score. This makes it possible to suggest stores based on their emotions by analyzing the user's emotional state in real time.

[0057] The behavior analysis unit can analyze the user's social network data and make suggestions that also take into account the preferences of friends and followers. The behavior analysis unit, for example, analyzes the user's social network data and makes suggestions that also take into account the preferences of friends and followers. For example, it can suggest restaurants that friends often visit. The behavior analysis unit also identifies the user's preferences based on the social network data and makes suggestions that also reflect the preferences of friends and followers. For example, it can suggest stores that share common interests. The behavior analysis unit also analyzes the user's social network data and builds a system that makes suggestions that also take into account the preferences of friends and followers. For example, it can select stores based on friends' reviews and ratings. This allows for more appropriate suggestions by also taking into account the preferences of friends and followers.

[0058] The behavior analysis unit can analyze the user's music streaming data and suggest stores based on the user's music preferences. The behavior analysis unit, for example, analyzes the user's music streaming data and suggests stores based on the user's music preferences. For example, to a user who likes a particular genre of music, the behavior analysis unit suggests cafes that play that genre of music. The behavior analysis unit also identifies the user's music preferences based on the music streaming data and suggests stores related to music. For example, it suggests restaurants where live music can be enjoyed. The behavior analysis unit also analyzes the user's music streaming data and builds a system that makes suggestions based on the user's music preferences. For example, it selects stores based on the music genre or artist. This makes it possible to suggest stores based on the user's music preferences.

[0059] The behavior analysis unit can analyze the user's travel history and make suggestions based on preferences at the travel destination. The behavior analysis unit, for example, analyzes the user's travel history and makes suggestions based on preferences at the travel destination. For example, it suggests stores with similar preferences based on behavioral data from travel destinations visited in the past. The behavior analysis unit also identifies the user's preferences based on the travel history and suggests the most suitable store at the travel destination. For example, it analyzes data related to meals and activities at the travel destination. The behavior analysis unit also analyzes the user's travel history and builds a system that makes suggestions based on preferences at the travel destination. For example, it selects stores based on reviews and ratings at the travel destination. This makes it possible to make suggestions based on preferences at the travel destination.

[0060] The behavior analysis unit uses the emotion estimation function to analyze the emotion a user feels when listening to specific music, and can suggest stores that match that emotion. For example, the behavior analysis unit uses the emotion estimation function to analyze the emotion a user feels when listening to specific music. For example, if the user feels relaxed, it will suggest a relaxing cafe. The behavior analysis unit also analyzes the emotion a user feels when listening to specific music, and suggests stores that match that emotion. For example, if the user is listening to energetic music, it will suggest a lively restaurant. The behavior analysis unit also identifies the emotional pattern a user feels when listening to specific music, based on the emotion estimation data, and suggests stores that match that emotion. For example, it selects the optimal store based on the emotion score. This makes it possible to suggest stores based on the emotion a user feels when listening to specific music.

[0061] The reservation unit can analyze the user's emotional state and send reservation reminders based on the emotion. For example, the reservation unit uses a generation AI to analyze the user's emotional state and send reservation reminders based on the emotion. For example, if the user is feeling stressed, the reservation unit sends a reservation reminder for a store where they can relax. The reservation unit also analyzes the user's emotional state and sends reservation reminders based on the emotion. For example, if the user has strong positive emotions, the reservation unit sends a reservation reminder for a fun event. The reservation unit also builds a system that analyzes the emotional state in real time and sends reservation reminders based on the emotion. For example, the reservation unit sends the optimal reservation reminder based on the emotion score. This makes it possible to make reservations that meet the user's needs by sending reservation reminders based on the emotion.

[0062] The reservation unit can analyze the user's calendar data and suggest the optimal reservation time based on the schedule. The reservation unit, for example, analyzes the user's calendar data and suggests the optimal reservation time based on the schedule. For example, it suggests making a reservation at a restaurant that matches an available time slot. The reservation unit also suggests the optimal reservation time taking the user's schedule into consideration based on the calendar data. For example, it suggests making a reservation at a cafe where you can relax after an important meeting. The reservation unit also builds a system that analyzes the user's calendar data and suggests reservation times based on the schedule. For example, it suggests the optimal reservation time between schedules. This makes it possible to make reservations that fit the user's schedule by suggesting the optimal reservation time based on the schedule.

[0063] The reservation unit can analyze the user's past reservation history and prioritize suggestions for reservations at frequently used restaurants. The reservation unit, for example, analyzes the user's past reservation history and prioritize suggestions for reservations at frequently used restaurants. For example, it suggests reservations at restaurants that the user frequently visits. The reservation unit also identifies the user's preferences based on the past reservation history and suggests frequently used restaurants. For example, it prioritizes suggestions for restaurants of a particular genre. The reservation unit also analyzes the user's past reservation history and builds a system that selects frequently used restaurants. For example, it suggests the most suitable restaurant based on reservation history data. In this way, by analyzing the past reservation history, it is possible to prioritize suggestions for reservations at frequently used restaurants.

[0064] The reservation unit can analyze data from a user's email or messaging app and automate communication regarding reservations. The reservation unit, for example, analyzes data from a user's email or messaging app and automates communication regarding reservations. For example, it automatically sends a reservation confirmation email. The reservation unit also builds a system that automates communication regarding reservations based on data from emails or messaging apps. For example, it automatically processes requests to change reservations. The reservation unit also analyzes data from a user's email or messaging app and automatically sends notifications and reminders regarding reservations. For example, it sends a reminder the day before the reservation. This automates communication regarding reservations, reducing the burden on users.

[0065] The reservation unit can analyze the user's location information and suggest a reservation at the store closest to the current location. The reservation unit, for example, analyzes the user's location information and suggests a reservation at the store closest to the current location. For example, it suggests restaurants within walking distance of the current location. The reservation unit also builds a system that suggests the most suitable store based on the user's current location based on the location information. For example, it suggests the nearest cafe to the current location. The reservation unit also analyzes the user's location information in real time and suggests a reservation based on the current location. For example, it checks the availability of the store closest to the current location and suggests a reservation. This improves user convenience by suggesting a reservation at the store closest to the current location.

[0066] The reservation unit can use the emotion estimation function to analyze the emotions a user feels during a specific time period and suggest a reservation time based on those emotions. The reservation unit, for example, uses the emotion estimation function to analyze the emotions a user feels during a specific time period. For example, if a user wants to relax in the evening, the reservation unit suggests making a reservation at a store where they can relax during that time period. The reservation unit also builds a system that analyzes the emotions a user feels during a specific time period and suggests a reservation time based on those emotions. For example, the reservation unit suggests an optimal reservation time based on an emotion score. The reservation unit also identifies a pattern of emotions a user feels during a specific time period based on the emotion estimation data and suggests a reservation time based on those emotions. For example, the reservation unit selects an optimal reservation time based on the emotion score. This makes it possible to make reservations that meet the user's needs by suggesting a reservation time based on the emotions felt during a specific time period.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The behavior analysis unit can analyze a user's hobbies and interests and suggest events and activities based on those hobbies. For example, if a user likes outdoor activities, nearby hiking trails and campsites can be suggested. The behavior analysis unit can also collect event information related to the user's hobbies and notify them at the appropriate time. For example, for a user who likes music, it can suggest nearby concerts and live events. The behavior analysis unit can also suggest related products and services based on the user's hobbies. For example, for a user who enjoys cooking, it can suggest cooking classes or new recipe books. This makes it possible to make suggestions based on the user's hobbies and interests, providing a more personalized experience.

[0069] The data integration unit can analyze a user's purchasing history and make suggestions based on purchasing patterns. For example, it can identify products and services that a user frequently purchases and suggest related stores. The data integration unit can also make suggestions related to specific seasons or events based on the user's purchasing history. For example, it can suggest gift shops and decoration supply stores during the Christmas season. The data integration unit can also analyze a user's purchasing history and suggest new products and services based on past purchase history. For example, it can suggest new products that go well with products purchased in the past. This makes it possible to provide suggestions based on the user's purchasing patterns, thereby providing more appropriate products and services.

[0070] The suggestion unit can analyze the user's travel history and make suggestions based on preferences at the travel destination. For example, it can suggest stores with similar preferences based on behavioral data from travel destinations visited in the past. The suggestion unit can also identify the user's preferences based on the travel history and suggest the most suitable store at the travel destination. For example, it can analyze data related to meals and activities at the travel destination. The suggestion unit can also analyze the user's travel history and build a system that makes suggestions based on preferences at the travel destination. For example, it can select stores based on reviews and ratings at the travel destination. This makes it possible to make suggestions based on preferences at the travel destination.

[0071] The data integration unit can analyze a user's music streaming data and suggest stores based on their music preferences. For example, it can suggest cafes that play music of a particular genre to a user who likes that genre. The data integration unit can also identify a user's music preferences based on the music streaming data and suggest stores related to music. For example, it can suggest restaurants where you can enjoy live music. The data integration unit can also analyze a user's music streaming data and build a system that makes suggestions based on their music preferences. For example, it can select stores based on music genres and artists. This makes it possible to suggest stores based on music preferences.

[0072] The reservation unit can analyze the user's calendar data and suggest the optimal reservation time based on the schedule. For example, it can suggest a restaurant reservation based on an available time slot. The reservation unit can also use the calendar data to suggest the optimal reservation time taking the user's schedule into consideration. For example, it can suggest a reservation at a cafe where you can relax after an important meeting. The reservation unit can also analyze the user's calendar data and build a system that suggests reservation times based on the schedule. For example, it can suggest the optimal reservation time between schedules. This allows the system to suggest the optimal reservation time based on the schedule, making it possible to make reservations that fit the user's schedule.

[0073] The behavioral analysis unit can analyze the user's emotional state in real time and make store suggestions based on the emotion. For example, if the user is feeling stressed, it can suggest a relaxing cafe. The behavioral analysis unit also analyzes the user's emotional state and suggests stores that correspond to the emotion. For example, if the user is feeling very positive, it can suggest a restaurant with a fun atmosphere. The behavioral analysis unit also analyzes the emotional state in real time and builds a system that makes suggestions based on the emotion. For example, it selects the most suitable store based on the emotion score. This makes it possible to suggest stores based on the emotion by analyzing the user's emotional state in real time.

[0074] The data integration unit can analyze the user's voice data, infer emotions and interests from the voice, and reflect these in suggestions. For example, it can calculate an emotion score based on the tone and content of the conversation and reflect this in suggestions. The data integration unit can also identify the user's interests and concerns based on the voice data and suggest appropriate stores. For example, if the user is interested in a particular topic, it can suggest stores related to that topic. The data integration unit can also analyze the user's voice data over time to identify fluctuations in emotions and interests. For example, it can make suggestions based on emotional fluctuations during specific time periods. This makes it possible to make suggestions based on emotions and interests by analyzing voice data.

[0075] The data integration unit can use the emotion estimation function to analyze the emotions a user feels in a specific place or time period and make suggestions based on the emotions. For example, the emotion estimation function is used to analyze the emotions a user feels in a specific place. For example, if the user is relaxing at a specific cafe, the data integration unit will suggest that cafe. The data integration unit also analyzes the emotions a user feels in a specific time period and makes suggestions based on the emotions. For example, if the user wants to relax in the evening, the data integration unit will suggest a store where the user can relax. The data integration unit also identifies patterns of emotions a user feels in a specific place or time period based on the emotion estimation data. For example, if the user wants to relax on the weekend, the data integration unit will suggest a store that is suitable for that time period. In this way, by analyzing the emotions felt in a specific place or time period, suggestions based on emotions become possible.

[0076] The reservation unit can analyze a user's emotional state and send reservation reminders based on the emotion. For example, the generation AI analyzes a user's emotional state and sends reservation reminders based on the emotion. For example, if the user is feeling stressed, it sends a reservation reminder for a relaxing establishment. The reservation unit also analyzes a user's emotional state and sends reservation reminders based on the emotion. For example, if the user has strong positive emotions, it sends a reservation reminder for a fun event. The reservation unit also builds a system that analyzes the emotional state in real time and sends reservation reminders based on the emotion. For example, it sends the optimal reservation reminder based on the emotion score. This makes it possible to make reservations that meet the user's needs by sending reservation reminders based on the emotion.

[0077] The behavior analysis unit can use the emotion estimation function to analyze the emotions a user feels when listening to specific music and suggest stores that match those emotions. For example, the emotion estimation function is used to analyze the emotions a user feels when listening to specific music. For example, if the user feels relaxed, a relaxing cafe is suggested. The behavior analysis unit also analyzes the emotions a user feels when listening to specific music and suggests stores that match those emotions. For example, if the user is listening to energetic music, a lively restaurant is suggested. The behavior analysis unit also identifies the emotional pattern a user feels when listening to specific music based on the emotion estimation data and suggests stores that match those emotions. For example, the optimal store is selected based on the emotion score. This makes it possible to suggest stores based on the emotions felt when listening to specific music.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The data integration unit integrates data collected from the user's smartphone. For example, it integrates location information, app usage history, search history, purchase history, and social media postings. The data integration unit can also convert data formats and eliminate duplicate data. Step 2: The behavior analysis unit analyzes the data integrated by the data integration unit to identify the user's behavioral patterns and preferences. For example, the behavior analysis unit may use frequency analysis or time series analysis to identify the user's behavioral patterns. The behavior analysis unit may also identify preferences based on past selection history and user feedback. Step 3: The suggestion unit suggests optimal restaurants and shops based on the behavioral patterns and preferences identified by the behavior analysis unit. For example, it uses a recommendation algorithm to suggest optimal stores for the user. The suggestion unit can also make suggestions taking into account the user's evaluation criteria. Step 4: The reservation unit easily reserves the restaurant proposed by the proposal unit. For example, the reservation unit may link with a reservation system and confirm the reservation. The reservation unit may also provide the user with a method for confirming the reservation.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 data integration unit that integrates data collected from users' smartphones; a behavior analysis unit that analyzes the data integrated by the data integration unit and identifies user behavior patterns and preferences; a suggestion unit that suggests optimal restaurants and shops based on the behavioral patterns and preferences identified by the behavior analysis unit; a reservation unit for easily reserving the store proposed by the proposal unit; A system characterized by:

2. The data integration unit Estimating the user's emotional state and weighting the data based on emotional fluctuations.

2. The system of claim 1.

3. The data integration unit Integrates the user's health data and makes recommendations based on their health status 2. The system of claim 1.

4. The data integration unit Analyzing the user's past behavioral data in chronological order to identify changes in preferences by season or time of day 2. The system of claim 1.

5. The data integration unit It will also integrate data from other wearable devices, such as smartwatches and fitness trackers, to analyze more detailed behavioral patterns.

2. The system of claim 1.

6. The data integration unit Analyze the user's voice data, infer emotions and interests from the voice, and reflect them in the suggestions.

2. The system of claim 1.

7. The data integration unit Analyzing the emotions felt by the user at a specific location or time of day and making suggestions based on those emotions 2. The system of claim 1.

8. The behavior analysis unit Analyzing the emotional state of the user in real time and making store recommendations based on the user's emotions 2. The system of claim 1.

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