System

Abstract

An object of a system according to an embodiment is to organize a friendship without being influenced by a user's feeling and recommend an appropriate friend.SOLUTION: A system includes a friend information input unit, a friend information analysis unit, and a recommendation unit. The friend information input unit inputs friend information of a user. The friend information analysis unit analyzes the friend information input by the friend information input unit. The recommendation unit makes a recommendation based on a result of the analysis by the friend information 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 technologies have had the problem that it is difficult for users to organize their friendships and recommend appropriate friends without being swayed by their emotions.

[0005] The system according to the embodiment aims to allow a user to organize friendships without being swayed by emotions and to recommend suitable friends. [Means for solving the problem]

[0006] The system according to the embodiment includes a friend information input unit, a friend information analysis unit, and a recommendation unit. The friend information input unit inputs friend information of a user. The friend information analysis unit analyzes the friend information input by the friend information input unit. The recommendation unit makes recommendations based on the results of the analysis by the friend information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to organize friendships without being swayed by emotions and recommend suitable friends. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 friend management AI system according to an embodiment of the present invention is a system in which a user inputs friend information, the generation AI analyzes it, and makes appropriate recommendations. This allows the user to calmly organize their friendships and meet with appropriate friends when they are in trouble.

[0029] The friend management AI system according to the embodiment includes a friend information input unit, a friend information analysis unit, and a recommendation unit. The friend information input unit inputs a user's friend information. For example, the user can input the friend's name, contact information, shared memories, the depth and frequency of the relationship, etc. In addition, when the user inputs the friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, if the friend's name or details of the relationship are insufficient, the generation AI provides feedback such as, "Please enter the friend's name more specifically." Furthermore, the friend information input unit uses voice recognition technology to allow the user to input friend information by voice. For example, if the user says, "Friend A was my best friend from high school, and we meet once a month," the generation AI converts that content into text and inputs it. The friend information analysis unit analyzes the friend information input by the friend information input unit. For example, the generation AI analyzes the friend information input by the user and evaluates the relationship and importance of each friend. The generation AI also refers to the friend's social media activity and public information to perform a more detailed relationship evaluation. For example, the generation AI analyzes Friend A's social media posts and reflects common hobbies and interests in the evaluation. The generation AI also takes into account past message history and shared event participation history. For example, it analyzes past message history with Friend A to evaluate the depth of the relationship. The recommendation unit makes recommendations based on the results of the analysis by the friend information analysis unit. For example, if the user is feeling stressed, the generation AI may recommend, "Meeting with Friend A may help you relax." The generation AI also analyzes the user's current psychological state and stress level in real time and recommends the most suitable friends. For example, if the user is feeling highly stressed, the generation AI may recommend, "Meeting with Friend A may help you relax." This allows the friend management AI system according to the embodiment to analyze the user's friend information and make appropriate recommendations.

[0030] In the friend information input unit, when a user enters friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, when a user enters friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, if the friend's name or details of the relationship are insufficient, the generation AI will provide feedback such as "Please enter the friend's name more specifically." This makes it possible to evaluate the consistency and level of detail of the user's input and provide appropriate feedback.

[0031] The friend information input unit can use voice recognition technology to enable the user to input friend information by voice. The friend information input unit, for example, introduces voice recognition technology to enable the user to input friend information by voice. For example, when the user says, "Friend A is my best friend from high school, and we meet once a month," the generation AI converts that content into text and inputs it. This allows the user to input friend information by voice.

[0032] The friend information input unit adds a function that allows users to upload photos and videos of friends, allowing them to manage visual information as well. For example, when a user enters friend information, the friend information input unit adds a function that allows users to upload photos and videos of friends. For example, a user uploads photos of a trip with friend A and links the photos to the friend information. This allows the user to upload photos and videos of friends and manage them as visual information as well.

[0033] The friend information input unit can enhance the synchronization function between different devices, allowing users to easily input friend information from smartphones and tablets. The friend information input unit, for example, enhances the synchronization function between different devices, allowing users to easily input friend information from smartphones and tablets. For example, friend information entered by a user on a smartphone is immediately reflected on the tablet. This enhances the synchronization function between different devices, allowing users to easily input friend information from smartphones and tablets.

[0034] The friend information analysis unit can also refer to the friend's social media activity or public information to perform a more detailed relationship evaluation. For example, when the generation AI analyzes friend information, the friend information analysis unit also refers to the friend's social media activity and public information to perform a more detailed relationship evaluation. For example, it analyzes friend A's social media posts and reflects common hobbies and interests in the evaluation. This allows a more detailed relationship evaluation to be performed by referring to the friend's social media activity and public information.

[0035] The friend information analysis unit can also take into account past message history and common event participation history. For example, when the generation AI analyzes friend information, the friend information analysis unit also takes into account past message history and common event participation history. For example, it analyzes past message history with friend A and evaluates the depth of the relationship. This takes into account past message history and common event participation history.

[0036] The friend information analysis unit can dynamically evaluate the importance of friendships by taking into account the user's life events. For example, when the generation AI analyzes friend information, the friend information analysis unit dynamically evaluates the importance of friendships by taking into account the user's life events (marriage, job change, etc.). For example, if the user gets married, the importance of friends who attended the wedding is highly evaluated. In this way, the importance of friendships is dynamically evaluated by taking into account the user's life events.

[0037] The friend information analysis unit can automatically extract friends' hobbies and interests and suggest common topics. For example, when the generation AI analyzes friend information, the friend information analysis unit automatically extracts friends' hobbies and interests and suggests common topics. For example, if friend A is interested in music, the generation AI will make a suggestion such as "Try talking about music with friend A." This automatically extracts friends' hobbies and interests and suggests common topics.

[0038] The recommendation unit can improve recommendation accuracy by learning from past recommendation results and subsequent user behavior. For example, when the generation AI makes a recommendation, the recommendation unit learns from past recommendation results and subsequent user behavior to improve recommendation accuracy. For example, it analyzes the user's emotional score after meeting a friend who was previously recommended and reflects this in the next recommendation. In this way, past recommendation results and subsequent user behavior are learned, improving recommendation accuracy.

[0039] The recommendation unit can take into account the user's schedule and free time and make suggestions for meeting up with friends at the optimal time. For example, when the generation AI makes recommendations, the recommendation unit takes into account the user's schedule and free time and makes suggestions for meeting up with friends at the optimal time. For example, it analyzes the user's calendar and makes suggestions for meeting up with friend A at a time when the user is free. This takes into account the user's schedule and free time and makes suggestions for meeting up with friends at the optimal time.

[0040] The recommendation unit can recommend appropriate friends by taking into account the friend's current situation. For example, when the generation AI makes a recommendation, the recommendation unit takes into account the friend's current situation (e.g., traveling or busy) and recommends appropriate friends. For example, if friend A is traveling, another friend will be recommended. In this way, appropriate friends are recommended by taking into account the friend's current situation.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The friend management AI system can also be equipped with a health management unit that monitors the user's health condition. For example, it can analyze the user's heart rate and sleep patterns and recommend friends to interact with based on their health condition. If the user is tired, it can suggest conversations with friends that will help them relax. Also, if the user is not getting enough exercise, it can suggest exercise with active friends. This makes it possible to make recommendations that take the user's health condition into consideration.

[0043] The friend management AI system can also be equipped with a hobby analysis unit that analyzes the user's hobbies and interests. For example, it can analyze the topics and activities that the user has recently taken an interest in and recommend interactions with friends based on that. If the user starts a new hobby, it can suggest interactions with friends who are knowledgeable about that hobby. It can also suggest participating in events with friends who share the same hobby. This makes it possible to make recommendations based on the user's hobbies and interests.

[0044] The friend management AI system can also be equipped with a behavior analysis unit that analyzes the user's past behavioral history. For example, it can analyze which friends the user has previously engaged in and what activities they have engaged in, and recommend interactions with friends based on that analysis. It can suggest reunions with friends with whom the user had a good time in the past. It can also suggest interactions with new friends based on past behavioral patterns. This makes it possible to make recommendations based on the user's past behavioral history.

[0045] The friend management AI system can also be equipped with a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm. For example, it can analyze the user's sleep patterns and meal timings and recommend friends to interact with based on that. If the user is a night owl, it can suggest interactions with friends who are also night owls. Also, if the user is a morning person, it can suggest interactions with friends who are active in the morning hours. This makes it possible to make recommendations based on the user's lifestyle rhythm.

[0046] The friend management AI system can also be equipped with an occupation analysis unit that analyzes the user's occupation and work situation. For example, the system can analyze the user's occupation and work situation and recommend friends to interact with based on that information. If the user is feeling stressed at work, the system can suggest conversations with friends that will help them relax. Also, if the user achieves success at work, the system can suggest interactions with friends who can share that success. This makes it possible to make recommendations based on the user's occupation and work situation.

[0047] The friend management AI system can also be equipped with a location information analysis unit that analyzes the user's geographical location information. For example, it can analyze the user's current location and frequently visited places and recommend friends to interact with based on that information. If the user is in a specific area, it can suggest interactions with friends who live in that area. Also, if the user is traveling, it can suggest interactions with friends who live in the travel destination. This makes it possible to make recommendations based on the user's geographical location information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The friend information input unit inputs the user's friend information. For example, the user can input the friend's name, contact information, shared memories, the depth of the relationship with the friend, and frequency of contact. Furthermore, when the user inputs friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, if the friend's name or details of the relationship are insufficient, the generation AI will provide feedback such as, "Please enter the friend's name more specifically." Furthermore, the friend information input unit uses voice recognition technology to allow the user to input friend information by voice. For example, if the user says, "Friend A is my best friend from high school, and we meet once a month," the generation AI converts that content into text and inputs it. Step 2: The friend information analysis unit analyzes the friend information entered by the friend information input unit. For example, the generation AI analyzes the friend information entered by the user and evaluates the relationship and importance with each friend. The generation AI also refers to the friends' social media activities and public information to perform a more detailed relationship evaluation. For example, it analyzes Friend A's social media posts and reflects common hobbies and interests in the evaluation. The generation AI also takes into account past message history and shared event participation history. For example, it analyzes past message history with Friend A and evaluates the depth of the relationship. Step 3: The recommendation unit makes recommendations based on the results of the analysis by the friend information analysis unit. For example, if the user is feeling stressed, the generation AI may recommend, "Meeting with Friend A may help you relax." The generation AI also analyzes the user's current psychological state and stress level in real time and recommends the most suitable friends. For example, if the user is feeling highly stressed, the generation AI may recommend, "Meeting with Friend A may help you relax."

[0050] (Example 2) The friend management AI system according to an embodiment of the present invention is a system in which a user inputs friend information, the generation AI analyzes it, and makes appropriate recommendations. This allows the user to calmly organize their friendships and meet with appropriate friends when they are in trouble.

[0051] The friend management AI system according to the embodiment includes a friend information input unit, a friend information analysis unit, and a recommendation unit. The friend information input unit inputs a user's friend information. For example, the user can input the friend's name, contact information, shared memories, the depth and frequency of the relationship, etc. In addition, when the user inputs the friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, if the friend's name or details of the relationship are insufficient, the generation AI provides feedback such as, "Please enter the friend's name more specifically." Furthermore, the friend information input unit uses voice recognition technology to allow the user to input friend information by voice. For example, if the user says, "Friend A was my best friend from high school, and we meet once a month," the generation AI converts that content into text and inputs it. The friend information analysis unit analyzes the friend information input by the friend information input unit. For example, the generation AI analyzes the friend information input by the user and evaluates the relationship and importance of each friend. The generation AI also refers to the friend's social media activity and public information to perform a more detailed relationship evaluation. For example, the generation AI analyzes Friend A's social media posts and reflects common hobbies and interests in the evaluation. The generation AI also takes into account past message history and shared event participation history. For example, it analyzes past message history with Friend A to evaluate the depth of the relationship. The recommendation unit makes recommendations based on the results of the analysis by the friend information analysis unit. For example, if the user is feeling stressed, the generation AI may recommend, "Meeting with Friend A may help you relax." The generation AI also analyzes the user's current psychological state and stress level in real time and recommends the most suitable friends. For example, if the user is feeling highly stressed, the generation AI may recommend, "Meeting with Friend A may help you relax." This allows the friend management AI system according to the embodiment to analyze the user's friend information and make appropriate recommendations.

[0052] In the friend information input unit, when a user enters friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, when a user enters friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, if the friend's name or details of the relationship are insufficient, the generation AI will provide feedback such as "Please enter the friend's name more specifically." This makes it possible to evaluate the consistency and level of detail of the user's input and provide appropriate feedback.

[0053] The friend information input unit can use voice recognition technology to enable the user to input friend information by voice. The friend information input unit, for example, introduces voice recognition technology to enable the user to input friend information by voice. For example, when the user says, "Friend A is my best friend from high school, and we meet once a month," the generation AI converts that content into text and inputs it. This allows the user to input friend information by voice.

[0054] The friend information input unit can use the emotion estimation function to analyze the emotion a user has when entering information and provide input support that draws out positive emotions. For example, the friend information input unit can use the emotion estimation function to analyze the emotion a user has when entering friend information and provide input support that draws out positive emotions. For example, if the user has negative emotions, the generation AI displays a message such as "Try to remember some fun memories you had with your friends." This allows the unit to analyze the user's emotions and provide input support that draws out positive emotions.

[0055] The friend information input unit adds a function that allows users to upload photos and videos of friends, allowing them to manage visual information as well. For example, when a user enters friend information, the friend information input unit adds a function that allows users to upload photos and videos of friends. For example, a user uploads photos of a trip with friend A and links the photos to the friend information. This allows the user to upload photos and videos of friends and manage them as visual information as well.

[0056] The friend information input unit can enhance the synchronization function between different devices, allowing users to easily input friend information from smartphones and tablets. The friend information input unit, for example, enhances the synchronization function between different devices, allowing users to easily input friend information from smartphones and tablets. For example, friend information entered by a user on a smartphone is immediately reflected on the tablet. This enhances the synchronization function between different devices, allowing users to easily input friend information from smartphones and tablets.

[0057] The friend information input unit can use the emotion estimation function to analyze the emotions of the user when entering information in real time and display an emotional support message according to the input content. The friend information input unit can, for example, use the emotion estimation function to analyze the emotions of the user when entering friend information in real time and display an emotional support message according to the input content. For example, if the user is feeling sad, the generation AI displays a message such as "Try to remember happy memories with your friends." In this way, the user's emotions are analyzed in real time and an emotional support message is displayed.

[0058] The friend information analysis unit can also refer to the friend's social media activity or public information to perform a more detailed relationship evaluation. For example, when the generation AI analyzes friend information, the friend information analysis unit also refers to the friend's social media activity and public information to perform a more detailed relationship evaluation. For example, it analyzes friend A's social media posts and reflects common hobbies and interests in the evaluation. This allows a more detailed relationship evaluation to be performed by referring to the friend's social media activity and public information.

[0059] The friend information analysis unit can also take into account past message history and common event participation history. For example, when the generation AI analyzes friend information, the friend information analysis unit also takes into account past message history and common event participation history. For example, it analyzes past message history with friend A and evaluates the depth of the relationship. This takes into account past message history and common event participation history.

[0060] The friend information analysis unit can use the emotion estimation function to analyze emotional transitions from past interactions between the user and friends and evaluate the depth of the relationship. The friend information analysis unit, for example, uses the emotion estimation function to analyze emotional transitions from past interactions between the user and friends and evaluate the depth of the relationship. For example, the friend information analysis unit analyzes the emotion scores of past messages with friend A and evaluates the depth of the relationship. In this way, the friend information analysis unit analyzes emotional transitions from past interactions between the user and friends and evaluates the depth of the relationship.

[0061] The friend information analysis unit can dynamically evaluate the importance of friendships by taking into account the user's life events. For example, when the generation AI analyzes friend information, the friend information analysis unit dynamically evaluates the importance of friendships by taking into account the user's life events (marriage, job change, etc.). For example, if the user gets married, the importance of friends who attended the wedding is highly evaluated. In this way, the importance of friendships is dynamically evaluated by taking into account the user's life events.

[0062] The friend information analysis unit can automatically extract friends' hobbies and interests and suggest common topics. For example, when the generation AI analyzes friend information, the friend information analysis unit automatically extracts friends' hobbies and interests and suggests common topics. For example, if friend A is interested in music, the generation AI will make a suggestion such as "Try talking about music with friend A." This automatically extracts friends' hobbies and interests and suggests common topics.

[0063] The friend information analysis unit can use the emotion estimation function to evaluate the emotional value of the time the user spent with a friend and reevaluate the depth of the relationship. The friend information analysis unit, for example, uses the emotion estimation function to evaluate the emotional value of the time the user spent with a friend and reevaluate the depth of the relationship. For example, the friend information analysis unit analyzes the emotion score of past interactions with friend A and reevaluates the depth of the relationship. In this way, the emotional value of the time the user spent with a friend is evaluated and the depth of the relationship is reevaluated.

[0064] The recommendation unit can analyze the user's psychological state and stress level in real time and recommend the most suitable friends. For example, when the generation AI makes a recommendation, the recommendation unit analyzes the user's current psychological state and stress level in real time and recommends the most suitable friends. For example, if the user is feeling high stress, the generation AI may recommend, "Meeting with Friend A might help you relax." In this way, the user's psychological state and stress level are analyzed in real time and the most suitable friends are recommended.

[0065] The recommendation unit can improve recommendation accuracy by learning from past recommendation results and subsequent user behavior. For example, when the generation AI makes a recommendation, the recommendation unit learns from past recommendation results and subsequent user behavior to improve recommendation accuracy. For example, it analyzes the user's emotional score after meeting a friend who was previously recommended and reflects this in the next recommendation. In this way, past recommendation results and subsequent user behavior are learned, improving recommendation accuracy.

[0066] The recommendation unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving a recommendation and reflect it in the next recommendation. The recommendation unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when receiving a recommendation and reflect it in the next recommendation. For example, the recommendation unit analyzes the emotion score after the user meets a recommended friend and reflects it in the next recommendation. In this way, the emotional reaction of the user when receiving a recommendation is analyzed and reflected in the next recommendation.

[0067] The recommendation unit can take into account the user's schedule and free time and make suggestions for meeting up with friends at the optimal time. For example, when the generation AI makes recommendations, the recommendation unit takes into account the user's schedule and free time and makes suggestions for meeting up with friends at the optimal time. For example, it analyzes the user's calendar and makes suggestions for meeting up with friend A at a time when the user is free. This takes into account the user's schedule and free time and makes suggestions for meeting up with friends at the optimal time.

[0068] The recommendation unit can recommend appropriate friends by taking into account the friend's current situation. For example, when the generation AI makes a recommendation, the recommendation unit takes into account the friend's current situation (e.g., traveling or busy) and recommends appropriate friends. For example, if friend A is traveling, another friend will be recommended. In this way, appropriate friends are recommended by taking into account the friend's current situation.

[0069] The recommendation unit can use the emotion estimation function to analyze the emotion of the user when receiving a recommendation in real time and dynamically adjust the recommendation content. The recommendation unit, for example, uses the emotion estimation function to analyze the emotion of the user when receiving a recommendation in real time and dynamically adjust the recommendation content. For example, the recommendation unit analyzes the emotion score after the user meets a recommended friend and reflects it in the next recommendation. In this way, the emotion of the user when receiving a recommendation is analyzed in real time and the recommendation content is dynamically adjusted.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The friend management AI system can also be equipped with a health management unit that monitors the user's health condition. For example, it can analyze the user's heart rate and sleep patterns and recommend friends to interact with based on their health condition. If the user is tired, it can suggest conversations with friends that will help them relax. Also, if the user is not getting enough exercise, it can suggest exercise with active friends. This makes it possible to make recommendations that take the user's health condition into consideration.

[0072] The friend management AI system can also be equipped with a hobby analysis unit that analyzes the user's hobbies and interests. For example, it can analyze the topics and activities that the user has recently taken an interest in and recommend interactions with friends based on that. If the user starts a new hobby, it can suggest interactions with friends who are knowledgeable about that hobby. It can also suggest participating in events with friends who share the same hobby. This makes it possible to make recommendations based on the user's hobbies and interests.

[0073] The friend management AI system can also be equipped with an emotion estimation unit that estimates the user's emotions and recommends interactions with friends based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest conversations with friends that will help them relax. Also, if the user is feeling happy, the system can suggest interactions with friends who share that happiness. This makes it possible to make recommendations based on the user's emotions.

[0074] The friend management AI system can also be equipped with a behavior analysis unit that analyzes the user's past behavioral history. For example, it can analyze which friends the user has previously engaged in and what activities they have engaged in, and recommend interactions with friends based on that analysis. It can suggest reunions with friends with whom the user had a good time in the past. It can also suggest interactions with new friends based on past behavioral patterns. This makes it possible to make recommendations based on the user's past behavioral history.

[0075] The friend management AI system can also be equipped with an emotion estimation unit that estimates the user's emotions and recommends interactions with friends based on the estimated emotions. For example, if the user is feeling sad, the system can suggest conversations with friends who will comfort them. Also, if the user is excited, the system can suggest interactions with friends who share that excitement. This makes it possible to make recommendations based on the user's emotions.

[0076] The friend management AI system can also be equipped with a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm. For example, it can analyze the user's sleep patterns and meal timings and recommend friends to interact with based on that. If the user is a night owl, it can suggest interactions with friends who are also night owls. Also, if the user is a morning person, it can suggest interactions with friends who are active in the morning hours. This makes it possible to make recommendations based on the user's lifestyle rhythm.

[0077] The friend management AI system can also be equipped with an emotion estimation unit that estimates the user's emotions and recommends interactions with friends based on the estimated emotions. For example, if the user is feeling anxious, the system can suggest conversations with friends who will give them a sense of security. Also, if the user is feeling happy, the system can suggest interactions with friends who share that happiness. This makes it possible to make recommendations based on the user's emotions.

[0078] The friend management AI system can also be equipped with an occupation analysis unit that analyzes the user's occupation and work situation. For example, the system can analyze the user's occupation and work situation and recommend friends to interact with based on that information. If the user is feeling stressed at work, the system can suggest conversations with friends that will help them relax. Also, if the user achieves success at work, the system can suggest interactions with friends who can share that success. This makes it possible to make recommendations based on the user's occupation and work situation.

[0079] The friend management AI system can also be equipped with an emotion estimation unit that estimates the user's emotions and recommends interactions with friends based on the estimated emotions. For example, if the user is feeling angry, the system can suggest conversations with friends who will listen calmly. Also, if the user is feeling happy, the system can suggest interactions with friends who share that joy. This makes it possible to make recommendations based on the user's emotions.

[0080] The friend management AI system can also be equipped with a location information analysis unit that analyzes the user's geographical location information. For example, it can analyze the user's current location and frequently visited places and recommend friends to interact with based on that information. If the user is in a specific area, it can suggest interactions with friends who live in that area. Also, if the user is traveling, it can suggest interactions with friends who live in the travel destination. This makes it possible to make recommendations based on the user's geographical location information.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The friend information input unit inputs the user's friend information. For example, the user can input the friend's name, contact information, shared memories, the depth of the relationship with the friend, and frequency of contact. Furthermore, when the user inputs friend information, the generation AI analyzes the input content in real time, evaluates the consistency and level of detail of the input, and provides feedback. For example, if the friend's name or details of the relationship are insufficient, the generation AI will provide feedback such as, "Please enter the friend's name more specifically." Furthermore, the friend information input unit uses voice recognition technology to allow the user to input friend information by voice. For example, if the user says, "Friend A is my best friend from high school, and we meet once a month," the generation AI converts that content into text and inputs it. Step 2: The friend information analysis unit analyzes the friend information entered by the friend information input unit. For example, the generation AI analyzes the friend information entered by the user and evaluates the relationship and importance with each friend. The generation AI also refers to the friends' social media activities and public information to perform a more detailed relationship evaluation. For example, it analyzes Friend A's social media posts and reflects common hobbies and interests in the evaluation. The generation AI also takes into account past message history and shared event participation history. For example, it analyzes past message history with Friend A and evaluates the depth of the relationship. Step 3: The recommendation unit makes recommendations based on the results of the analysis by the friend information analysis unit. For example, if the user is feeling stressed, the generation AI may recommend, "Meeting with Friend A may help you relax." The generation AI also analyzes the user's current psychological state and stress level in real time and recommends the most suitable friends. For example, if the user is feeling highly stressed, the generation AI may recommend, "Meeting with Friend A may help you relax."

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 friend information input unit for inputting friend information of the user; a friend information analysis unit that analyzes the friend information input by the friend information input unit; a recommendation unit that makes recommendations based on the results of the analysis by the friend information analysis unit. A system characterized by:

2. The friend information input unit Using voice recognition technology, users can input the friend information by voice.

2. The system of claim 1.

3. The friend information analysis unit Also look at friends' social media activity or public information to make a more detailed relationship assessment 2. The system of claim 1.

4. The recommendation unit Analyze the user's psychological state and stress level in real time and recommend the most suitable friends.

2. The system of claim 1.

5. The friend information input unit Using the emotion estimation function, the emotions expressed by the user when inputting data are analyzed, and input support is provided that elicits positive emotions.

2. The system of claim 1.

6. The friend information analysis unit Using the emotion estimation function, the transition of emotions between the user and their friends is analyzed based on past interactions, and the depth of the relationship is evaluated.

2. The system of claim 1.

7. The recommendation unit Using an emotion estimation function, the emotional reaction of the user when receiving the recommendation is analyzed and reflected in the next recommendation.

2. The system of claim 1.

8. The recommendation unit Using an emotion estimation function, the emotion of the user when receiving the recommendation is analyzed in real time, and the recommendation content is dynamically adjusted.

2. The system of claim 1.

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