System

Abstract

An object of a system according to an embodiment is to propose an optimal activity for effectively utilizing a user's free time.SOLUTION: A system includes an idle time setting unit, a preference analysis unit, and an activity suggestion unit. The idle-time setting unit sets an idle time of the user. The preference analysis section analyzes the user's preference and interest based on the vacant time set by the vacant time setting section. The activity proposal unit proposes an optimal activity based on the preferences and interests analyzed by the preference analysis 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 techniques do not provide appropriate suggestions for effectively utilizing users' free time, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest optimal activities for effectively utilizing a user's free time. [Means for solving the problem]

[0006] The system according to the embodiment includes a free time setting unit, a preference analysis unit, and an activity suggestion unit. The free time setting unit sets a user's free time. The preference analysis unit analyzes the user's preferences and interests based on the free time set by the free time setting unit. The activity suggestion unit suggests optimal activities based on the preferences and interests analyzed by the preference analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal activities for effectively utilizing the user's free time. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 AI ​​partner system according to the embodiment of the present invention is a system that suggests optimal activities for a user's set free time, thereby enabling the user to make effective use of their free time.

[0029] The AI ​​partner system according to the embodiment includes a free time setting unit, a preference analysis unit, and an activity suggestion unit. The free time setting unit sets the user's free time. For example, when a user voice-inputs, "I'm free for 30 minutes from 3:00 PM," the generation AI analyzes the information using speech recognition technology and sets the free time. The speech recognition technology performs highly accurate analysis taking into account the user's pronunciation and accent. The free time setting unit also works with the user's calendar app, allowing the generation AI to automatically detect free time. For example, it analyzes time periods with no scheduled events on the calendar and sets those times as free time. Furthermore, the free time setting unit uses an emotion estimation function to estimate time periods when the user feels like relaxing. For example, if the user has a tired expression, it sets those times as suggested times for relaxation activities. The preference analysis unit analyzes the user's preferences and interests based on the free time set by the free time setting unit. For example, the generation AI works with the user's social media account to analyze the content posted and liked. For example, the system identifies the genres of movies and music that the user has "liked" and suggests activities based on those preferences. The preference analysis unit also analyzes the user's history of videos watched and articles read in the past to identify trends in interests. For example, it identifies the genres and themes the user frequently watches and suggests activities based on those interests. The preference analysis unit also uses the emotion estimation function to analyze the emotional responses of the user's past enjoyed activities to identify preferences. For example, it analyzes the user's facial expressions and voice when watching a movie and identifies preferences based on those emotional responses. The activity suggestion unit suggests optimal activities based on the preferences and interests analyzed by the preference analysis unit. For example, the generation AI analyzes the user's current mood and physical condition and suggests optimal activities based on that. For example, if the user is tired, it suggests relaxing activities. The activity suggestion unit also uses the user's geographic location information to suggest nearby events and activities. For example, it suggests events held near the user's location. The activity suggestion unit also uses the emotion estimation function to suggest activities that evoke the most positive emotions in the user. For example, it analyzes positive emotions from the user's facial expressions and voice and suggests activities based on those emotions.As a result, the AI ​​partner system according to the embodiment can effectively utilize the user's free time. For example, the generation AI can suggest optimal activities for the free time set by the user, and the user can select and carry out the activities to refresh themselves or acquire new knowledge.

[0030] The free time setting unit can set the user's free time using voice input. For example, when the user vocally inputs, "I'm free for 30 minutes from 3:00 PM," the generation AI analyzes the information using voice recognition technology and sets the free time. The voice recognition technology performs highly accurate analysis taking into account the user's pronunciation and accent. This allows the user to easily set their free time using voice input.

[0031] The free time setting unit can work in conjunction with the user's calendar app to automatically detect free time. The free time setting unit can work in conjunction with, for example, a calendar app on the user's smartphone or PC, and the generation AI can automatically detect free time. For example, it can analyze time periods with no events on the calendar and set those times as free time. In this way, by working in conjunction with the calendar app, the user's free time can be automatically detected.

[0032] The preference analysis unit can link with the user's social media account and analyze their preferences and interests from the content they post and have "liked." For example, the preference analysis unit can link with the user's social media account and have the generation AI analyze the content they post and have "liked." For example, it can identify the genres of movies and music that the user has "liked" and suggest activities based on those preferences. By linking with the social media account, the user's preferences and interests can be accurately analyzed.

[0033] The preference analysis unit can identify trends in interests by analyzing the history of videos a user has watched and articles they have read in the past. For example, the preference analysis unit uses a generation AI to analyze the history of videos a user has watched in the past and identify trends in interests. For example, it can identify genres and themes that a user frequently watches and suggest activities based on those interests. In this way, by analyzing past viewing history and article history, it is possible to identify trends in a user's interests.

[0034] The activity suggestion unit can analyze the user's current mood and physical condition and suggest the optimal activity based on the results. For example, the activity suggestion unit can analyze the user's current mood and physical condition and suggest the optimal activity based on the results. For example, if the user is tired, it can suggest a relaxation activity. In this way, it is possible to suggest the optimal activity based on the user's mood and physical condition.

[0035] The activity suggestion unit can use the user's geographical location information to suggest nearby events and activities. For example, the activity suggestion unit uses the user's geographical location information to have the generation AI suggest nearby events and activities. For example, it suggests events that are held near the user's location. In this way, by using the geographical location information, it is possible to suggest events and activities that can be enjoyed near the user.

[0036] The activity suggestion unit can suggest new, never-before-experienced activities based on the user's past activity history. For example, the activity suggestion unit analyzes the user's past activity history using a generation AI and suggests new, never-before-experienced activities. For example, it suggests activities that the user has never done before. This makes it possible to suggest activities that will be new experiences for the user based on the user's past activity history.

[0037] The activity suggestion unit also takes into consideration the preferences of the user's friends and family and can suggest activities that can be enjoyed together. For example, the activity suggestion unit also takes into consideration the preferences of the user's friends and family and the generation AI suggests activities that can be enjoyed together. For example, it suggests events and activities that the whole family can enjoy. In this way, by taking into consideration the preferences of friends and family, it is possible to suggest activities that can be enjoyed together.

[0038] The activity suggestion unit can monitor the progress of an activity selected by a user in real time and provide appropriate support. For example, the activity suggestion unit can have a generation AI monitor the progress of an activity selected by a user in real time and provide appropriate support. For example, if a user has trouble during an English conversation lesson, support can be provided immediately. This makes it possible to monitor progress in real time and provide appropriate support.

[0039] The activity suggestion unit can automatically provide additional information and resources related to the activity selected by the user. For example, the activity suggestion unit automatically provides additional information and resources related to the activity selected by the user. For example, the activity suggestion unit provides a relevant word list or grammar explanation during an English conversation lesson. This allows the user's activity to be supported by automatically providing additional information and resources.

[0040] The activity suggestion unit can provide a function for sharing an activity selected by the user with other users and enjoying it together. The activity suggestion unit provides, for example, a function for sharing an activity selected by the user with other users and enjoying it together. For example, it provides a function for enjoying an online game with friends. This allows the activity selected by the user to be shared with other users and enjoyed together.

[0041] The activity suggestion unit can suggest other related activities for the activity selected by the user, allowing them to be enjoyed consecutively. For example, the activity suggestion unit uses a generation AI to suggest other related activities for the activity selected by the user, allowing them to be enjoyed consecutively. For example, the unit can suggest watching a related movie after an English conversation lesson. This allows the user to enjoy consecutively by suggesting other related activities.

[0042] The activity suggestion unit can analyze user feedback and improve the algorithm to reflect it in the next suggestion. For example, the activity suggestion unit collects user feedback, and the generation AI analyzes the data to improve the algorithm to reflect it in the next suggestion. For example, if a user evaluates an activity as "fun," a similar activity will be suggested next time. This allows the algorithm to be improved by analyzing user feedback and reflecting it in the next suggestion.

[0043] The activity suggestion unit can evaluate the effectiveness of the proposed activity based on user feedback and identify areas for improvement. For example, the activity suggestion unit evaluates the effectiveness of the activity proposed by the generation AI based on user feedback and identifies areas for improvement. For example, if the user provides feedback that "this activity was not very effective," the cause is analyzed and reflected in the next suggestion. This makes it possible to evaluate the effectiveness of the proposed activity based on user feedback and identify areas for improvement.

[0044] The activity suggestion unit can share the user's feedback with other users and identify common areas for improvement. For example, the activity suggestion unit shares the user's feedback with other users, and the generation AI identifies common areas for improvement. For example, if multiple users give feedback that "this activity takes too long," that improvement will be reflected in the next suggestion. This allows the user's feedback to be shared with other users, and common areas for improvement can be identified.

[0045] The activity suggestion unit can develop new activity suggestions based on user feedback. For example, the activity suggestion unit uses a generation AI to develop new activity suggestions based on user feedback. For example, if a user provides feedback that they would like to do more creative activities, new creative activities will be suggested. This allows new activity suggestions to be developed based on user feedback.

[0046] The free time setting unit can analyze the user's past free time usage history and automatically suggest the optimal time period. For example, the free time setting unit uses a generation AI to analyze the user's past free time usage history and automatically suggest the optimal time period. For example, it identifies a time period in which the user relaxed in the past and suggests that time period. In this way, the optimal time period can be automatically suggested by analyzing the past usage history.

[0047] The preference analysis unit can compare the user's preferences and interests with those of other users and suggest activities for users who share common interests. For example, the preference analysis unit can compare the user's preferences and interests with those of other users and suggest activities for users who share common interests. For example, it can suggest watching a movie for users who like the same movie genre. This can promote interaction between users by suggesting activities for users who share common interests.

[0048] The preference analysis unit dynamically updates the user's preferences and interests according to the season and events, and can suggest optimal activities. For example, the preference analysis unit dynamically updates the user's preferences and interests according to the season and events, and the generation AI suggests optimal activities. For example, outdoor activities are suggested in the summer, and indoor activities in the winter. This allows the user's preferences and interests to be dynamically updated according to the season and events, and optimal activities are suggested.

[0049] The activity suggestion unit can provide a function for sharing an activity selected by the user with other users and enjoying it together. The activity suggestion unit provides, for example, a function for sharing an activity selected by the user with other users and enjoying it together. For example, it provides a function for enjoying an online game with friends. This allows the activity selected by the user to be shared with other users and enjoyed together.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The AI ​​Partner System can also obtain the user's health data and suggest activities based on their health condition. For example, it can obtain heart rate and sleep data from the user's smartwatch and suggest relaxation activities if the user is highly fatigued. It can also analyze the user's food records and suggest healthy recipes if their nutritional balance is unbalanced. It can also suggest appropriate exercise programs based on the user's exercise history.

[0052] The AI ​​partner system can also suggest activities that will help improve a user's skills, taking into account their hobbies and special abilities. For example, if a user enjoys guitar, it can suggest new song practice or online lessons. If a user is good at cooking, it can suggest new recipes or cooking classes. Furthermore, if a user is interested in language learning, it can suggest suitable online courses or learning apps.

[0053] The AI ​​Partner System can also suggest new activities that the user has never experienced before based on the user's past activity history. For example, it can suggest activities that the user has never done before. This allows the user to discover new interests through new experiences. It can also suggest events that will provide more satisfaction based on feedback from events the user has previously participated in. Furthermore, it can analyze the user's past travel history and suggest tourist spots that the user has not yet visited.

[0054] The AI ​​Partner System can also take into account the preferences of the user's friends and family to suggest activities that can be enjoyed together. For example, it can suggest events and activities that the whole family can enjoy, allowing users to spend quality time together with their friends and family. It can also suggest activities for special occasions such as friends' birthdays and anniversaries. It can also take into account the schedules of the user's friends and family to suggest the best time for everyone to participate.

[0055] The AI ​​partner system can monitor the user's progress in real time and provide appropriate support for the user's selected activities. For example, if a user has difficulty during an English conversation lesson, it can provide immediate support. It can also guide the user through recipe steps in real time while cooking. It can also provide advice on maintaining proper form and pace while exercising.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The free time setting unit sets the user's free time. For example, if the user voice-inputs, "I'm free for 30 minutes from 3 p.m.", the generation AI analyzes the information using voice recognition technology and sets the free time. The voice recognition technology performs highly accurate analysis, taking into account the user's pronunciation and accent. The free time setting unit also works with the user's calendar app, allowing the generation AI to automatically detect free time. For example, it analyzes time periods with no events on the calendar and sets those times as free time. Furthermore, the free time setting unit uses an emotion estimation function to estimate time periods when the user feels like relaxing. For example, if the user looks tired, it sets those times as suggested times for relaxation activities. Step 2: The preference analysis unit analyzes the user's preferences and interests based on the free time set by the free time setting unit. For example, the generation AI connects with the user's social media account and analyzes the content posted and liked by the user. For example, it identifies the movie and music genres that the user has liked and suggests activities based on those preferences. The preference analysis unit also analyzes the history of videos the user has watched and articles read in the past to identify trends in interests. For example, it identifies the genres and themes that the user frequently watches and suggests activities based on those interests. Furthermore, the preference analysis unit uses an emotion estimation function to analyze the emotional responses of the user to activities that they have enjoyed in the past and identify their preferences. For example, it analyzes the user's facial expressions and voice when watching a movie and identifies their preferences based on their emotional responses. Step 3: The activity suggestion unit suggests optimal activities based on the preferences and interests analyzed by the preference analysis unit. For example, the generation AI analyzes the user's current mood and physical condition and suggests optimal activities based on that. For example, if the user is tired, it will suggest relaxation activities. The activity suggestion unit also uses the user's geographical location information, and the generation AI suggests nearby events and activities. For example, it suggests events held near the user's location. Furthermore, the activity suggestion unit uses an emotion estimation function to suggest activities that will evoke the most positive emotions in the user. For example, it analyzes positive emotions from the user's facial expressions and voice and suggests activities based on those emotions.

[0058] (Example 2) The AI ​​partner system according to the embodiment of the present invention is a system that suggests optimal activities for a user's set free time, thereby enabling the user to make effective use of their free time.

[0059] The AI ​​partner system according to the embodiment includes a free time setting unit, a preference analysis unit, and an activity suggestion unit. The free time setting unit sets the user's free time. For example, when a user voice-inputs, "I'm free for 30 minutes from 3:00 PM," the generation AI analyzes the information using speech recognition technology and sets the free time. The speech recognition technology performs highly accurate analysis taking into account the user's pronunciation and accent. The free time setting unit also works with the user's calendar app, allowing the generation AI to automatically detect free time. For example, it analyzes time periods with no scheduled events on the calendar and sets those times as free time. Furthermore, the free time setting unit uses an emotion estimation function to estimate time periods when the user feels like relaxing. For example, if the user has a tired expression, it sets those times as suggested times for relaxation activities. The preference analysis unit analyzes the user's preferences and interests based on the free time set by the free time setting unit. For example, the generation AI works with the user's social media account to analyze the content posted and liked. For example, the system identifies the genres of movies and music that the user has "liked" and suggests activities based on those preferences. The preference analysis unit also analyzes the user's history of videos watched and articles read in the past to identify trends in interests. For example, it identifies the genres and themes the user frequently watches and suggests activities based on those interests. The preference analysis unit also uses the emotion estimation function to analyze the emotional responses of the user's past enjoyed activities to identify preferences. For example, it analyzes the user's facial expressions and voice when watching a movie and identifies preferences based on those emotional responses. The activity suggestion unit suggests optimal activities based on the preferences and interests analyzed by the preference analysis unit. For example, the generation AI analyzes the user's current mood and physical condition and suggests optimal activities based on that. For example, if the user is tired, it suggests relaxing activities. The activity suggestion unit also uses the user's geographic location information to suggest nearby events and activities. For example, it suggests events held near the user's location. The activity suggestion unit also uses the emotion estimation function to suggest activities that evoke the most positive emotions in the user. For example, it analyzes positive emotions from the user's facial expressions and voice and suggests activities based on those emotions.As a result, the AI ​​partner system according to the embodiment can effectively utilize the user's free time. For example, the generation AI can suggest optimal activities for the free time set by the user, and the user can select and carry out the activities to refresh themselves or acquire new knowledge.

[0060] The free time setting unit can set the user's free time using voice input. For example, when the user vocally inputs, "I'm free for 30 minutes from 3:00 PM," the generation AI analyzes the information using voice recognition technology and sets the free time. The voice recognition technology performs highly accurate analysis taking into account the user's pronunciation and accent. This allows the user to easily set their free time using voice input.

[0061] The free time setting unit can work in conjunction with the user's calendar app to automatically detect free time. The free time setting unit can work in conjunction with, for example, a calendar app on the user's smartphone or PC, and the generation AI can automatically detect free time. For example, it can analyze time periods with no events on the calendar and set those times as free time. In this way, by working in conjunction with the calendar app, the user's free time can be automatically detected.

[0062] The free time setting unit can use the emotion estimation function to estimate a time period when the user wants to relax. For example, the free time setting unit uses the emotion estimation function to estimate a time period when the user wants to relax from the user's facial expression or voice. For example, if the user looks tired, the free time setting unit sets that time period as a suggested time for a relaxation activity. This allows the emotion estimation function to accurately estimate a time period when the user wants to relax.

[0063] The preference analysis unit can link with the user's social media account and analyze their preferences and interests from the content they post and have "liked." For example, the preference analysis unit can link with the user's social media account and have the generation AI analyze the content they post and have "liked." For example, it can identify the genres of movies and music that the user has "liked" and suggest activities based on those preferences. By linking with the social media account, the user's preferences and interests can be accurately analyzed.

[0064] The preference analysis unit can identify trends in interests by analyzing the history of videos a user has watched and articles they have read in the past. For example, the preference analysis unit uses a generation AI to analyze the history of videos a user has watched in the past and identify trends in interests. For example, it can identify genres and themes that a user frequently watches and suggest activities based on those interests. In this way, by analyzing past viewing history and article history, it is possible to identify trends in a user's interests.

[0065] The preference analysis unit can use the emotion estimation function to analyze the emotional responses of the user to activities that the user enjoyed in the past and identify preferences. The preference analysis unit, for example, uses the emotion estimation function to analyze the emotional responses of the user to activities that the user enjoyed in the past. For example, the preference analysis unit analyzes the user's facial expressions and voice when watching a movie and identifies preferences based on the emotional responses. In this way, the emotion estimation function can accurately identify the user's preferences.

[0066] The activity suggestion unit can analyze the user's current mood and physical condition and suggest the optimal activity based on the results. For example, the activity suggestion unit can analyze the user's current mood and physical condition and suggest the optimal activity based on the results. For example, if the user is tired, it can suggest a relaxation activity. In this way, it is possible to suggest the optimal activity based on the user's mood and physical condition.

[0067] The activity suggestion unit can use the user's geographical location information to suggest nearby events and activities. For example, the activity suggestion unit uses the user's geographical location information to have the generation AI suggest nearby events and activities. For example, it suggests events that are held near the user's location. In this way, by using the geographical location information, it is possible to suggest events and activities that can be enjoyed near the user.

[0068] The activity suggestion unit can use the emotion estimation function to suggest an activity that will make the user feel the most positive emotion. The activity suggestion unit, for example, uses the emotion estimation function to suggest an activity that will make the user feel the most positive emotion. For example, the activity suggestion unit analyzes positive emotions from the user's facial expressions and voice, and suggests an activity based on the emotions. In this way, the emotion estimation function can suggest an activity that will make the user feel the most positive emotion.

[0069] The activity suggestion unit can suggest new, never-before-experienced activities based on the user's past activity history. For example, the activity suggestion unit analyzes the user's past activity history using a generation AI and suggests new, never-before-experienced activities. For example, it suggests activities that the user has never done before. This makes it possible to suggest activities that will be new experiences for the user based on the user's past activity history.

[0070] The activity suggestion unit also takes into consideration the preferences of the user's friends and family and can suggest activities that can be enjoyed together. For example, the activity suggestion unit also takes into consideration the preferences of the user's friends and family and the generation AI suggests activities that can be enjoyed together. For example, it suggests events and activities that the whole family can enjoy. In this way, by taking into consideration the preferences of friends and family, it is possible to suggest activities that can be enjoyed together.

[0071] The activity suggestion unit can use the emotion estimation function to suggest an activity that will help the user to be most relaxed. The activity suggestion unit, for example, uses the emotion estimation function to suggest an activity that will help the user to be most relaxed. For example, the activity suggestion unit analyzes the user's level of relaxation from their facial expressions and voice, and suggests an activity based on that emotion. In this way, the emotion estimation function can suggest an activity that will help the user to be most relaxed.

[0072] The activity suggestion unit can monitor the progress of an activity selected by a user in real time and provide appropriate support. For example, the activity suggestion unit can have a generation AI monitor the progress of an activity selected by a user in real time and provide appropriate support. For example, if a user has trouble during an English conversation lesson, support can be provided immediately. This makes it possible to monitor progress in real time and provide appropriate support.

[0073] The activity suggestion unit can automatically provide additional information and resources related to the activity selected by the user. For example, the activity suggestion unit automatically provides additional information and resources related to the activity selected by the user. For example, the activity suggestion unit provides a relevant word list or grammar explanation during an English conversation lesson. This allows the user's activity to be supported by automatically providing additional information and resources.

[0074] The activity suggestion unit can use the emotion estimation function to analyze in real time the emotions felt by the user during an activity and adjust the activity content as needed. For example, the activity suggestion unit can use the emotion estimation function to analyze in real time the emotions felt by the user during an activity and adjust the activity content as needed. For example, if the user is feeling stressed, the activity suggestion unit can suggest a relaxation activity. In this way, the emotion estimation function can adjust the activity content according to the user's emotions.

[0075] The activity suggestion unit can provide a function for sharing an activity selected by the user with other users and enjoying it together. The activity suggestion unit provides, for example, a function for sharing an activity selected by the user with other users and enjoying it together. For example, it provides a function for enjoying an online game with friends. This allows the activity selected by the user to be shared with other users and enjoyed together.

[0076] The activity suggestion unit can suggest other related activities for the activity selected by the user, allowing them to be enjoyed consecutively. For example, the activity suggestion unit uses a generation AI to suggest other related activities for the activity selected by the user, allowing them to be enjoyed consecutively. For example, the unit can suggest watching a related movie after an English conversation lesson. This allows the user to enjoy consecutively by suggesting other related activities.

[0077] The activity suggestion unit can use the emotion estimation function to continuously suggest activities that the user will enjoy most. The activity suggestion unit, for example, uses the emotion estimation function to continuously suggest activities that the user will enjoy most. For example, it analyzes whether the user is enjoying the activity from their facial expressions or voice, and suggests the next activity based on their emotion. In this way, the emotion estimation function can continuously suggest activities that the user will enjoy most.

[0078] The activity suggestion unit can analyze user feedback and improve the algorithm to reflect it in the next suggestion. For example, the activity suggestion unit collects user feedback, and the generation AI analyzes the data to improve the algorithm to reflect it in the next suggestion. For example, if a user evaluates an activity as "fun," a similar activity will be suggested next time. This allows the algorithm to be improved by analyzing user feedback and reflecting it in the next suggestion.

[0079] The activity suggestion unit can evaluate the effectiveness of the proposed activity based on user feedback and identify areas for improvement. For example, the activity suggestion unit evaluates the effectiveness of the activity proposed by the generation AI based on user feedback and identifies areas for improvement. For example, if the user provides feedback that "this activity was not very effective," the cause is analyzed and reflected in the next suggestion. This makes it possible to evaluate the effectiveness of the proposed activity based on user feedback and identify areas for improvement.

[0080] The activity suggestion unit can use the emotion estimation function to evaluate the emotional satisfaction level based on the user's feedback and reflect it in the next suggestion. The activity suggestion unit, for example, uses the emotion estimation function to evaluate the emotional satisfaction level based on the user's feedback and reflect it in the next suggestion. For example, if the user gives feedback that "this activity was fun," the emotion is analyzed and a similar activity is suggested next time. In this way, the emotion estimation function can evaluate the user's emotional satisfaction level and reflect it in the next suggestion.

[0081] The activity suggestion unit can share the user's feedback with other users and identify common areas for improvement. For example, the activity suggestion unit shares the user's feedback with other users, and the generation AI identifies common areas for improvement. For example, if multiple users give feedback that "this activity takes too long," that improvement will be reflected in the next suggestion. This allows the user's feedback to be shared with other users, and common areas for improvement can be identified.

[0082] The activity suggestion unit can develop new activity suggestions based on user feedback. For example, the activity suggestion unit uses a generation AI to develop new activity suggestions based on user feedback. For example, if a user provides feedback that they would like to do more creative activities, new creative activities will be suggested. This allows new activity suggestions to be developed based on user feedback.

[0083] The activity suggestion unit can use the emotion estimation function to identify optimal improvements based on user feedback and reflect them in the next suggestion. The activity suggestion unit, for example, uses the emotion estimation function to identify optimal improvements based on user feedback and reflect them in the next suggestion. For example, if a user provides feedback such as "this activity was fun," the emotion is analyzed and a similar activity is suggested for the next time. In this way, the emotion estimation function can identify optimal improvements based on user feedback and reflect them in the next suggestion.

[0084] The free time setting unit can analyze the user's past free time usage history and automatically suggest the optimal time period. For example, the free time setting unit uses a generation AI to analyze the user's past free time usage history and automatically suggest the optimal time period. For example, it identifies a time period in which the user relaxed in the past and suggests that time period. In this way, the optimal time period can be automatically suggested by analyzing the past usage history.

[0085] The free time setting unit can use the emotion estimation function to identify a time period when the user can be most relaxed and suggest an optimal activity for that time. The free time setting unit can, for example, use the emotion estimation function to identify a time period when the user can be most relaxed. For example, the free time setting unit can analyze the user's facial expression or voice to indicate their level of relaxation and suggest that time period. In this way, the emotion estimation function can identify a time period when the user can be most relaxed and suggest an optimal activity for that time period.

[0086] The preference analysis unit can compare the user's preferences and interests with those of other users and suggest activities for users who share common interests. For example, the preference analysis unit can compare the user's preferences and interests with those of other users and suggest activities for users who share common interests. For example, it can suggest watching a movie for users who like the same movie genre. This can promote interaction between users by suggesting activities for users who share common interests.

[0087] The preference analysis unit dynamically updates the user's preferences and interests according to the season and events, and can suggest optimal activities. For example, the preference analysis unit dynamically updates the user's preferences and interests according to the season and events, and the generation AI suggests optimal activities. For example, outdoor activities are suggested in the summer, and indoor activities in the winter. This allows the user's preferences and interests to be dynamically updated according to the season and events, and optimal activities are suggested.

[0088] The preference analysis unit can use the emotion estimation function to identify new topics that the user is most interested in and suggest activities related to those topics. The preference analysis unit, for example, uses the emotion estimation function to identify new topics that the user is most interested in. For example, it analyzes the interest level from the user's facial expressions and voice and suggests activities related to those topics. In this way, the emotion estimation function can identify new topics that the user is most interested in and suggest activities related to those topics.

[0089] The activity suggestion unit can provide a function for sharing an activity selected by the user with other users and enjoying it together. The activity suggestion unit provides, for example, a function for sharing an activity selected by the user with other users and enjoying it together. For example, it provides a function for enjoying an online game with friends. This allows the activity selected by the user to be shared with other users and enjoyed together.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The AI ​​Partner System can also obtain the user's health data and suggest activities based on their health condition. For example, it can obtain heart rate and sleep data from the user's smartwatch and suggest relaxation activities if the user is highly fatigued. It can also analyze the user's food records and suggest healthy recipes if their nutritional balance is unbalanced. It can also suggest appropriate exercise programs based on the user's exercise history.

[0092] The AI ​​partner system can also suggest activities that will help improve a user's skills, taking into account their hobbies and special abilities. For example, if a user enjoys guitar, it can suggest new song practice or online lessons. If a user is good at cooking, it can suggest new recipes or cooking classes. Furthermore, if a user is interested in language learning, it can suggest suitable online courses or learning apps.

[0093] The AI ​​Partner System can also suggest new activities that the user has never experienced before based on the user's past activity history. For example, it can suggest activities that the user has never done before. This allows the user to discover new interests through new experiences. It can also suggest events that will provide more satisfaction based on feedback from events the user has previously participated in. Furthermore, it can analyze the user's past travel history and suggest tourist spots that the user has not yet visited.

[0094] The AI ​​Partner System can also take into account the preferences of the user's friends and family to suggest activities that can be enjoyed together. For example, it can suggest events and activities that the whole family can enjoy, allowing users to spend quality time together with their friends and family. It can also suggest activities for special occasions such as friends' birthdays and anniversaries. It can also take into account the schedules of the user's friends and family to suggest the best time for everyone to participate.

[0095] The AI ​​partner system can monitor the user's progress in real time and provide appropriate support for the user's selected activities. For example, if a user has difficulty during an English conversation lesson, it can provide immediate support. It can also guide the user through recipe steps in real time while cooking. It can also provide advice on maintaining proper form and pace while exercising.

[0096] The AI ​​partner system can also estimate the user's emotions and suggest activities that will help the user relax best based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing activities such as meditation or yoga. If the user is feeling sad, it can also suggest fun movies or music to lift their spirits. Furthermore, if the user is excited, it can suggest sports or activities that will help them release their energy.

[0097] The AI ​​partner system can also estimate the user's emotions and suggest activities that the user would enjoy most based on the estimated emotions. For example, if the user is happy, it can suggest events or activities that will further enhance that emotion. If the user is bored, it can also suggest activities that will help them develop new hobbies or interests. Furthermore, if the user is tense, it can suggest activities in a relaxing environment.

[0098] The AI ​​Partner System can also estimate a user's emotions and, based on the estimated emotions, suggest activities that will evoke the most positive feelings for the user. For example, it can analyze positive emotions from the user's facial expressions and voice and suggest activities based on those emotions. This allows the user to always enjoy activities in a positive mood. It can also analyze the emotional responses of activities that the user has enjoyed in the past and suggest similar activities. Furthermore, if a user tends to evoke positive feelings toward a particular activity, it can prioritize those activities.

[0099] The AI ​​Partner System can estimate the user's emotions, and based on the estimated emotions, identify the time of day when the user is most relaxed and suggest the best activity for that time. For example, it can analyze the user's level of relaxation from their facial expressions and voice and suggest the best time. This allows the user to perform the best activity during the time when they are most relaxed. It can also identify times when the user has previously been relaxed and suggest those times. It can also analyze the user's daily rhythm and suggest the best time to relax.

[0100] The AI ​​Partner System can estimate a user's emotions and, based on the estimated emotions, identify new topics that interest the user most and suggest activities related to those topics. For example, it can analyze the user's level of interest from their facial expressions and voice and suggest activities related to those topics. This allows users to constantly discover new interests. It can also identify topics that the user has been interested in in the past and suggest new activities related to those topics. It can also analyze the user's social media accounts and search history to identify new topics.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The free time setting unit sets the user's free time. For example, if the user voice-inputs, "I'm free for 30 minutes from 3 p.m.", the generation AI analyzes the information using voice recognition technology and sets the free time. The voice recognition technology performs highly accurate analysis, taking into account the user's pronunciation and accent. The free time setting unit also works with the user's calendar app, allowing the generation AI to automatically detect free time. For example, it analyzes time periods with no events on the calendar and sets those times as free time. Furthermore, the free time setting unit uses an emotion estimation function to estimate time periods when the user feels like relaxing. For example, if the user looks tired, it sets those times as suggested times for relaxation activities. Step 2: The preference analysis unit analyzes the user's preferences and interests based on the free time set by the free time setting unit. For example, the generation AI connects with the user's social media account and analyzes the content posted and liked by the user. For example, it identifies the movie and music genres that the user has liked and suggests activities based on those preferences. The preference analysis unit also analyzes the history of videos the user has watched and articles read in the past to identify trends in interests. For example, it identifies the genres and themes that the user frequently watches and suggests activities based on those interests. Furthermore, the preference analysis unit uses an emotion estimation function to analyze the emotional responses of the user to activities that they have enjoyed in the past and identify their preferences. For example, it analyzes the user's facial expressions and voice when watching a movie and identifies their preferences based on their emotional responses. Step 3: The activity suggestion unit suggests optimal activities based on the preferences and interests analyzed by the preference analysis unit. For example, the generation AI analyzes the user's current mood and physical condition and suggests optimal activities based on that. For example, if the user is tired, it will suggest relaxation activities. The activity suggestion unit also uses the user's geographical location information, and the generation AI suggests nearby events and activities. For example, it suggests events held near the user's location. Furthermore, the activity suggestion unit uses an emotion estimation function to suggest activities that will evoke the most positive emotions in the user. For example, it analyzes positive emotions from the user's facial expressions and voice and suggests activities based on those emotions.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 free time setting unit that sets free time of the user; a preference analysis unit that analyzes the preferences and interests of the user based on the free time set by the free time setting unit; an activity suggestion unit that suggests optimal activities based on the preferences and interests analyzed by the preference analysis unit; A system characterized by:

2. The free time setting unit Works with the user's calendar app to automatically detect free time 2. The system of claim 1.

3. The preference analysis unit Linking with users' social media accounts to analyze their preferences and interests based on their posts and "liked" content 2. The system of claim 1.

4. The activity suggestion unit Analyzes the user's current mood and physical condition and suggests optimal activities based on that 2. The system of claim 1.

5. The free time setting unit Using emotion estimation function, we estimate the time period when users feel like relaxing.

2. The system of claim 1.

6. The preference analysis unit Emotion estimation capabilities analyze the user's emotional responses to previously enjoyed activities to identify preferences 2. The system of claim 1.

7. The activity suggestion unit Using emotion estimation, we suggest activities that will evoke the most positive emotions in users.

2. The system of claim 1.

8. The activity suggestion unit Emotion estimation function evaluates emotional satisfaction based on user feedback and reflects it in future suggestions 2. The system of claim 1.

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