System

Abstract

An object of a system according to an embodiment is to provide specific advice for a user to act with more confidence.SOLUTION: A system according to an embodiment includes a speaking style analysis unit, a behavior analysis unit, an advice generation unit, and a successful experience recording unit. The speaking style analysis unit analyzes the speaking style of the user. The behavior analysis unit analyzes a behavior of a user. The advice generation unit generates specific advice for the user to act with more confidence on the basis of the data obtained by the speaking style analysis unit and the action analysis unit. The successful experience recording unit records a successful experience of a user.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 obtain specific advice that will enable them to act with confidence.

[0005] The system according to the embodiment aims to provide specific advice to help users act with more confidence. [Means for solving the problem]

[0006] The system according to the embodiment includes a speaking style analysis unit, a behavior analysis unit, an advice generation unit, and a success experience recording unit. The speaking style analysis unit analyzes the user's speaking style. The behavior analysis unit analyzes the user's behavior. The advice generation unit generates specific advice for the user to act with more confidence based on data obtained by the speaking style analysis unit and the behavior analysis unit. The success experience recording unit records the user's success experiences. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific advice to help the user act with more confidence. [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 ​​app according to the embodiment of the present invention is a system that analyzes the user's speech and behavior, and the generative AI provides specific advice and records successful experiences. This allows the AI ​​app to support the user in acting with confidence and accumulate small successful experiences every day.

[0029] The AI ​​app according to the embodiment includes a speech style analysis unit, a behavior analysis unit, an advice generation unit, and a success experience recording unit. The speech style analysis unit analyzes the user's speech style. For example, when a user speaks to the app, the speech data is input to the generation AI, which analyzes the user's speech characteristics, tone, and word choice. The speech style analysis unit can also analyze the user's tone, speed, rhythm, and other aspects of the speech to identify areas for improvement in the user's speech style. The behavior analysis unit analyzes the user's behavior. For example, the generation AI collects data on the user's daily behavior and analyzes the behavioral patterns. The behavior analysis unit can also analyze non-verbal communication such as gestures, facial expressions, and posture. The advice generation unit generates specific advice to help the user act more confidently based on the data obtained by the speech style analysis unit and the behavior analysis unit. For example, advice such as "Speak more slowly to make it easier for others to understand you" or "Practice expressing your opinions clearly" is provided. The advice generation unit can also compare the user's speech with past data to specifically indicate progress and areas for improvement. The success experience recording unit records the user's success experiences. For example, the generation AI tracks the progress of goals set by the user and records the successes achieved. The success recorder also records the user's successes in detail and compares them with past successes to give the user a sense of growth. This allows the AI ​​app according to the embodiment to support the user in taking action with confidence and accumulate small successes every day.

[0030] The behavioral analysis unit analyzes the user's non-verbal communication and can improve overall communication skills. For example, when the user speaks to the app, the generation AI uses a camera to analyze the user's facial expressions and gestures. For example, if the user is smiling while speaking, the generation AI can provide advice on how to maintain that positive expression. The behavioral analysis unit can also analyze the user's non-verbal communication (gestures and facial expressions) and improve overall communication skills. In this way, analyzing the user's non-verbal communication and improving overall communication skills can increase the user's confidence.

[0031] The advice generation unit can compare the data with the user's past data and specifically indicate progress and areas for improvement. For example, when the user speaks to the app, the generation AI compares the data with past voice data and analyzes progress. For example, if the user's speaking is becoming smoother than before, the AI ​​provides specific feedback on that progress. The advice generation unit can also compare the data with the user's past data and specifically indicate progress and areas for improvement. This improves the user's confidence by comparing the data with the user's past data and specifically indicating progress and areas for improvement.

[0032] The advice generation unit can provide step-by-step advice toward achieving long-term goals based on the user's past behavioral data. For example, when the user talks to the app, the generation AI provides advice toward achieving long-term goals based on the user's past behavioral data. For example, the advice generation unit suggests specific steps toward achieving a goal set by the user. The advice generation unit can also provide step-by-step advice toward achieving long-term goals based on the user's past behavioral data. This improves the user's confidence by providing step-by-step advice toward achieving long-term goals based on the user's past behavioral data.

[0033] The advice generation unit can analyze the user's behavioral patterns and provide advice at the optimal timing. For example, when the user talks to the app, the generation AI analyzes the behavioral patterns and provides advice at the optimal timing. For example, advice is provided during times when the user is relaxing. The advice generation unit can also analyze the user's behavioral patterns and provide advice at the optimal timing. In this way, analyzing the user's behavioral patterns and providing advice at the optimal timing improves the user's confidence.

[0034] The advice generation unit can visualize the user's advice and provide it in a visually easy-to-understand format. For example, when the user speaks to the app, the advice generation unit uses the generation AI to visualize the advice and provide it in a visually easy-to-understand format. For example, the advice generation unit displays the advice using graphs or charts. The advice generation unit can also visualize the user's advice and provide it in a visually easy-to-understand format. In this way, by visualizing the user's advice and providing it in a visually easy-to-understand format, the user's confidence is improved.

[0035] The advice generation unit can provide the user's advice in audio or video format, thereby providing a more interactive experience. For example, when the user speaks to the app, the advice generation unit uses the generation AI to provide advice in audio or video format. For example, the advice generation unit can provide advice using audio guides or video tutorials. The advice generation unit can also provide the user's advice in audio or video format, thereby providing a more interactive experience. This improves the user's confidence by providing the user's advice in audio or video format and providing a more interactive experience.

[0036] The success experience recording unit records the user's success experiences in detail, allowing the user to feel a sense of growth by comparing them with past success experiences. For example, when the user speaks to the app, the generation AI records the user's success experiences in detail, allowing the user to feel a sense of growth by comparing them with past success experiences. For example, if the user is speaking more confidently than before, the AI ​​provides specific feedback on that progress. The success experience recording unit can also record the user's success experiences in detail, allowing the user to feel a sense of growth by comparing them with past success experiences. This improves the user's confidence by recording the user's success experiences in detail and allowing the user to feel a sense of growth by comparing them with past success experiences.

[0037] The success experience recording unit can share the user's success experiences with other users and promote feedback within the community. For example, when the user talks to the app, the generation AI shares the user's success experiences with other users and promotes feedback within the community. For example, the success experience recording unit can inform other users of goals the user has achieved. The success experience recording unit can also share the user's success experiences with other users and promote feedback within the community. This allows the user's success experiences to be shared with other users and promotes feedback within the community, thereby improving the user's confidence.

[0038] The success experience recording unit can visualize the user's success experiences and show progress in graphs and charts. For example, when the user talks to the app, the generation AI visualizes the success experiences and shows progress in graphs and charts. For example, progress toward goals set by the user is displayed in a graph. The success experience recording unit can also visualize the user's success experiences and show progress in graphs and charts. In this way, visualizing the user's success experiences and showing progress in graphs and charts improves the user's confidence.

[0039] The success experience recording unit can record the user's success experiences in audio or video format, allowing them to be reviewed later. For example, when the user talks to the app, the generation AI records the user's success experiences in audio or video format. For example, the user can record a goal they have achieved in video format, allowing them to be reviewed later. The success experience recording unit can also record the user's success experiences in audio or video format, allowing them to be reviewed later. This improves the user's confidence by recording the user's success experiences in audio or video format, allowing them to be reviewed later.

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

[0041] In addition to analyzing the user's speech patterns and behavior, the system can also provide advice that takes into account the user's hobbies and interests. For example, if the user is interested in sports, the system can provide specific sports-related advice. If the user likes music, the system can provide music-related advice. Furthermore, the system can record related success stories based on the user's hobbies and interests, helping the user grow while having fun.

[0042] In addition to analyzing a user's non-verbal communication, the system can also provide training programs to improve the user's communication skills. For example, it can suggest practice methods to help the user speak more confidently. It can also provide training to help the user use effective gestures and facial expressions. Furthermore, it can improve the user's communication skills by monitoring the user's progress and providing feedback on the effectiveness of the training.

[0043] In addition to comparing with the user's past data, you can also implement a reward system to keep the user motivated to achieve their goals. For example, you can give badges or points when the user achieves a set goal. You can also offer perks or rewards when the user earns a certain number of points. You can also provide visual feedback to keep the user motivated by showing their progress.

[0044] In addition to providing advice based on the user's past behavioral data, the app can also provide health management advice based on the user's behavioral patterns. For example, it can analyze the user's sleep patterns and provide advice on how to get better sleep. It can also analyze the user's diet and exercise data and provide specific advice on maintaining a healthy lifestyle. It can also monitor the user's health status and provide advice from medical professionals as needed.

[0045] In addition to analyzing the user's behavioral patterns, it can also suggest optimal study methods based on the user's behavioral patterns. For example, it can identify the time periods when the user can study efficiently and suggest studying at those times. It can also suggest appropriate study methods and learning materials based on the user's learning style. Furthermore, it can monitor the user's learning progress and provide feedback on effective study methods, thereby improving the user's learning effectiveness.

[0046] In addition to visualizing the user's advice, it can also be gamified to allow users to learn while having fun. For example, a game can be provided to help users achieve goals they have set. Users can also receive specific advice as they progress through the game. Furthermore, the user's progress can be visually displayed within the game, providing feedback to maintain motivation.

[0047] In addition to providing users with advice in audio or video format, advice can also be provided using virtual reality (VR) or augmented reality (AR). For example, a user can receive advice in a virtual space by wearing a VR headset. AR can also be used to overlay advice onto the real world. Furthermore, the user's progress can be visually displayed through VR or AR, providing a more interactive experience.

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

[0049] Step 1: The speech analysis unit analyzes the user's speaking style. For example, when a user speaks to the app, the voice data is input into the generation AI, which analyzes the characteristics of the speaking style, tone, and word choice. It can also analyze the tone, speed, and rhythm of the voice to identify areas for improvement in the user's speaking style. Step 2: The behavioral analysis unit analyzes the user's behavior. For example, data on the user's daily behavior is collected, and the generation AI analyzes their behavioral patterns. It can also analyze non-verbal communication such as gestures, facial expressions, and posture. Step 3: The advice generator generates specific advice based on the data obtained by the speaking style analysis and behavior analysis to help the user act with more confidence. For example, it may provide advice such as, "If you speak more slowly, it will be easier for the other person to understand you," or "Practice expressing your opinions clearly." It can also compare the user's past data to show specific progress and areas for improvement. Step 4: The success experience recording unit records the user's success experiences. For example, the generation AI tracks the progress of the user's set goals and records the success experiences achieved. It can also record the user's success experiences in detail and compare them with past success experiences to give the user a sense of growth.

[0050] (Example 2) The AI ​​app according to the embodiment of the present invention is a system that analyzes the user's speech and behavior, and the generative AI provides specific advice and records successful experiences. This allows the AI ​​app to support the user in acting with confidence and accumulate small successful experiences every day.

[0051] The AI ​​app according to the embodiment includes a speech style analysis unit, a behavior analysis unit, an advice generation unit, and a success experience recording unit. The speech style analysis unit analyzes the user's speech style. For example, when a user speaks to the app, the speech data is input to the generation AI, which analyzes the user's speech characteristics, tone, and word choice. The speech style analysis unit can also analyze the user's tone, speed, rhythm, and other aspects of the speech to identify areas for improvement in the user's speech style. The behavior analysis unit analyzes the user's behavior. For example, the generation AI collects data on the user's daily behavior and analyzes the behavioral patterns. The behavior analysis unit can also analyze non-verbal communication such as gestures, facial expressions, and posture. The advice generation unit generates specific advice to help the user act more confidently based on the data obtained by the speech style analysis unit and the behavior analysis unit. For example, advice such as "Speak more slowly to make it easier for others to understand you" or "Practice expressing your opinions clearly" is provided. The advice generation unit can also compare the user's speech with past data to specifically indicate progress and areas for improvement. The success experience recording unit records the user's success experiences. For example, the generation AI tracks the progress of goals set by the user and records the successes achieved. The success recorder also records the user's successes in detail and compares them with past successes to give the user a sense of growth. This allows the AI ​​app according to the embodiment to support the user in taking action with confidence and accumulate small successes every day.

[0052] The speech style analysis unit can estimate the user's emotional state in real time and provide feedback according to changes in emotion. For example, when a user speaks to an app, the speech style analysis unit uses a generative AI to analyze the voice data in real time and estimate the user's emotional state. For example, it can read emotions from the tone, speed, and word choice of the voice and provide advice to relax if the user is nervous. The speech style analysis unit can also monitor the user's emotional state in real time and provide feedback according to changes in emotion. This improves the user's confidence by estimating the user's emotional state in real time and providing feedback according to changes in emotion.

[0053] The behavioral analysis unit analyzes the user's non-verbal communication and can improve overall communication skills. For example, when the user speaks to the app, the generation AI uses a camera to analyze the user's facial expressions and gestures. For example, if the user is smiling while speaking, the generation AI can provide advice on how to maintain that positive expression. The behavioral analysis unit can also analyze the user's non-verbal communication (gestures and facial expressions) and improve overall communication skills. In this way, analyzing the user's non-verbal communication and improving overall communication skills can increase the user's confidence.

[0054] The advice generation unit can compare the data with the user's past data and specifically indicate progress and areas for improvement. For example, when the user speaks to the app, the generation AI compares the data with past voice data and analyzes progress. For example, if the user's speaking is becoming smoother than before, the AI ​​provides specific feedback on that progress. The advice generation unit can also compare the data with the user's past data and specifically indicate progress and areas for improvement. This improves the user's confidence by comparing the data with the user's past data and specifically indicating progress and areas for improvement.

[0055] The advice generation unit can estimate the user's emotional state in real time and provide specific advice according to the emotion. For example, when the user speaks to the app, the advice generation unit uses a generation AI to analyze the user's emotional state in real time and provide advice according to the emotion. For example, if the user is nervous, the advice generation unit can provide advice to relax. The advice generation unit can also estimate the user's emotional state in real time and provide specific advice according to the emotion. In this way, by estimating the user's emotional state in real time and providing specific advice according to the emotion, the user's confidence can be improved.

[0056] The advice generation unit can provide step-by-step advice toward achieving long-term goals based on the user's past behavioral data. For example, when the user talks to the app, the generation AI provides advice toward achieving long-term goals based on the user's past behavioral data. For example, the advice generation unit suggests specific steps toward achieving a goal set by the user. The advice generation unit can also provide step-by-step advice toward achieving long-term goals based on the user's past behavioral data. This improves the user's confidence by providing step-by-step advice toward achieving long-term goals based on the user's past behavioral data.

[0057] The advice generation unit can analyze the user's behavioral patterns and provide advice at the optimal timing. For example, when the user talks to the app, the generation AI analyzes the behavioral patterns and provides advice at the optimal timing. For example, advice is provided during times when the user is relaxing. The advice generation unit can also analyze the user's behavioral patterns and provide advice at the optimal timing. In this way, analyzing the user's behavioral patterns and providing advice at the optimal timing improves the user's confidence.

[0058] The advice generation unit can visualize the user's advice and provide it in a visually easy-to-understand format. For example, when the user speaks to the app, the advice generation unit uses the generation AI to visualize the advice and provide it in a visually easy-to-understand format. For example, the advice generation unit displays the advice using graphs or charts. The advice generation unit can also visualize the user's advice and provide it in a visually easy-to-understand format. In this way, by visualizing the user's advice and providing it in a visually easy-to-understand format, the user's confidence is improved.

[0059] The advice generation unit can provide the user's advice in audio or video format, thereby providing a more interactive experience. For example, when the user speaks to the app, the advice generation unit uses the generation AI to provide advice in audio or video format. For example, the advice generation unit can provide advice using audio guides or video tutorials. The advice generation unit can also provide the user's advice in audio or video format, thereby providing a more interactive experience. This improves the user's confidence by providing the user's advice in audio or video format and providing a more interactive experience.

[0060] The advice generation unit can use the emotion estimation function to identify advice that will evoke the most positive emotion in the user and provide that advice preferentially. For example, when the user speaks to the app, the advice generation unit uses the emotion estimation function to identify advice that will evoke the most positive emotion in the user. For example, advice that will make the user feel happy is provided preferentially. The advice generation unit can also use the emotion estimation function to identify advice that will evoke the most positive emotion in the user and provide that advice preferentially. In this way, by using the emotion estimation function to identify advice that will evoke the most positive emotion in the user and providing that advice preferentially, the user's confidence is improved.

[0061] The success experience recording unit can estimate the user's emotional state in real time and provide feedback on success experiences according to the emotion. For example, when the user speaks to the app, the generation AI analyzes the user's emotional state in real time and provides feedback on success experiences according to the emotion. For example, if the user feels a sense of accomplishment, feedback is provided to reinforce that emotion. The success experience recording unit can also estimate the user's emotional state in real time and provide feedback on success experiences according to the emotion. In this way, by estimating the user's emotional state in real time and providing feedback on success experiences according to the emotion, the user's confidence is improved.

[0062] The success experience recording unit records the user's success experiences in detail, allowing the user to feel a sense of growth by comparing them with past success experiences. For example, when the user speaks to the app, the generation AI records the user's success experiences in detail, allowing the user to feel a sense of growth by comparing them with past success experiences. For example, if the user is speaking more confidently than before, the AI ​​provides specific feedback on that progress. The success experience recording unit can also record the user's success experiences in detail, allowing the user to feel a sense of growth by comparing them with past success experiences. This improves the user's confidence by recording the user's success experiences in detail and allowing the user to feel a sense of growth by comparing them with past success experiences.

[0063] The success experience recording unit can share the user's success experiences with other users and promote feedback within the community. For example, when the user talks to the app, the generation AI shares the user's success experiences with other users and promotes feedback within the community. For example, the success experience recording unit can inform other users of goals the user has achieved. The success experience recording unit can also share the user's success experiences with other users and promote feedback within the community. This allows the user's success experiences to be shared with other users and promotes feedback within the community, thereby improving the user's confidence.

[0064] The success experience recording unit can visualize the user's success experiences and show progress in graphs and charts. For example, when the user talks to the app, the generation AI visualizes the success experiences and shows progress in graphs and charts. For example, progress toward goals set by the user is displayed in a graph. The success experience recording unit can also visualize the user's success experiences and show progress in graphs and charts. In this way, visualizing the user's success experiences and showing progress in graphs and charts improves the user's confidence.

[0065] The success experience recording unit can record the user's success experiences in audio or video format, allowing them to be reviewed later. For example, when the user talks to the app, the generation AI records the user's success experiences in audio or video format. For example, the user can record a goal they have achieved in video format, allowing them to be reviewed later. The success experience recording unit can also record the user's success experiences in audio or video format, allowing them to be reviewed later. This improves the user's confidence by recording the user's success experiences in audio or video format, allowing them to be reviewed later.

[0066] The successful experience recording unit can use the emotion estimation function to identify the successful experience that the user feels most positive about and emphasize that successful experience. For example, when the user speaks to the app, the generation AI uses the emotion estimation function to identify the successful experience that the user feels most positive about. For example, it can emphasize a successful experience that made the user feel a sense of accomplishment. The successful experience recording unit can also use the emotion estimation function to identify the successful experience that the user feels most positive about and emphasize that successful experience. In this way, by using the emotion estimation function to identify the successful experience that the user feels most positive about and emphasizing that successful experience, the user's confidence is improved.

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

[0068] In addition to analyzing the user's speech patterns and behavior, the system can also provide advice that takes into account the user's hobbies and interests. For example, if the user is interested in sports, the system can provide specific sports-related advice. If the user likes music, the system can provide music-related advice. Furthermore, the system can record related success stories based on the user's hobbies and interests, helping the user grow while having fun.

[0069] In addition to estimating the user's emotional state, the system can also suggest relaxation methods based on the user's emotions. For example, if the user is feeling stressed, the system can suggest deep breathing or meditation. If the user is tired, the system can suggest short breaks or stretching. Furthermore, the system can customize relaxation methods based on the user's emotional state to help the user relax.

[0070] In addition to analyzing a user's non-verbal communication, the system can also provide training programs to improve the user's communication skills. For example, it can suggest practice methods to help the user speak more confidently. It can also provide training to help the user use effective gestures and facial expressions. Furthermore, it can improve the user's communication skills by monitoring the user's progress and providing feedback on the effectiveness of the training.

[0071] In addition to comparing with the user's past data, you can also implement a reward system to keep the user motivated to achieve their goals. For example, you can give badges or points when the user achieves a set goal. You can also offer perks or rewards when the user earns a certain number of points. You can also provide visual feedback to keep the user motivated by showing their progress.

[0072] Not only can it estimate the user's emotional state in real time, but it can also provide music and environmental sounds that correspond to the user's emotions. For example, if the user wants to relax, it can play relaxing music. Or, if the user wants to concentrate, it can provide environmental sounds that will help them concentrate. Furthermore, it can customize music and environmental sounds according to the user's emotional state to help the user feel comfortable.

[0073] In addition to providing advice based on the user's past behavioral data, the app can also provide health management advice based on the user's behavioral patterns. For example, it can analyze the user's sleep patterns and provide advice on how to get better sleep. It can also analyze the user's diet and exercise data and provide specific advice on maintaining a healthy lifestyle. It can also monitor the user's health status and provide advice from medical professionals as needed.

[0074] In addition to analyzing the user's behavioral patterns, it can also suggest optimal study methods based on the user's behavioral patterns. For example, it can identify the time periods when the user can study efficiently and suggest studying at those times. It can also suggest appropriate study methods and learning materials based on the user's learning style. Furthermore, it can monitor the user's learning progress and provide feedback on effective study methods, thereby improving the user's learning effectiveness.

[0075] In addition to visualizing the user's advice, it can also be gamified to allow users to learn while having fun. For example, a game can be provided to help users achieve goals they have set. Users can also receive specific advice as they progress through the game. Furthermore, the user's progress can be visually displayed within the game, providing feedback to maintain motivation.

[0076] In addition to providing users with advice in audio or video format, advice can also be provided using virtual reality (VR) or augmented reality (AR). For example, a user can receive advice in a virtual space by wearing a VR headset. AR can also be used to overlay advice onto the real world. Furthermore, the user's progress can be visually displayed through VR or AR, providing a more interactive experience.

[0077] In addition to identifying the advice that elicits the most positive emotions from the user, the system can also provide positive messages according to the user's emotions. For example, if the user is feeling down, the system can provide an encouraging message. If the user is happy, the system can provide a message to further enhance that joy. Furthermore, the system can customize positive messages according to the user's emotional state, helping the user to always stay positive.

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

[0079] Step 1: The speech analysis unit analyzes the user's speaking style. For example, when a user speaks to the app, the voice data is input into the generation AI, which analyzes the characteristics of the speaking style, tone, and word choice. It can also analyze the tone, speed, and rhythm of the voice to identify areas for improvement in the user's speaking style. Step 2: The behavioral analysis unit analyzes the user's behavior. For example, data on the user's daily behavior is collected, and the generation AI analyzes their behavioral patterns. It can also analyze non-verbal communication such as gestures, facial expressions, and posture. Step 3: The advice generator generates specific advice to help the user act with more confidence based on the data obtained by the speaking style analysis and behavior analysis. For example, advice such as "If you speak more slowly, it will be easier for the other person to understand you" or "Practice expressing your opinions clearly" can be provided. It can also compare the user's past data and provide specific indications of progress and areas for improvement. Step 4: The success experience recording unit records the user's success experiences. For example, the generation AI tracks the progress of the user's set goals and records the success experiences achieved. It can also record the user's success experiences in detail and compare them with past success experiences to give the user a sense of growth.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following 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.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a speaking style analysis unit that analyzes a user's speaking style; a behavior analysis unit that analyzes user behavior; an advice generation unit that generates specific advice for the user to act with more confidence based on the data obtained by the speaking style analysis unit and the behavior analysis unit; A success experience recording unit that records the user's success experiences. A system characterized by:

2. The speaking style analysis unit Estimate the user's emotional state in real time and provide feedback according to changes in emotion 2. The system of claim 1.

3. The behavior analysis unit Analyzes users' non-verbal communication to improve their overall communication skills 2. The system of claim 1.

4. The advice generation unit Compare with the user's past data to clearly show progress and areas for improvement 2. The system of claim 1.

5. The success experience recording unit The user's emotional state is estimated in real time, and feedback on the successful experience is provided according to the user's emotions.

2. The system of claim 1.

6. The advice generation unit Estimates the user's emotional state in real time and provides specific advice based on the emotion.

2. The system of claim 1.

7. The success experience recording unit Record the user's success in detail and compare it with past successes to realize growth.

2. The system of claim 1.

8. The success experience recording unit Using an emotion estimation function, the successful experience that the user feels the most positive about is identified and the successful experience is highlighted.

2. The system of claim 1.

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