System

Abstract

An object of a system according to an embodiment is to support a user through interaction with a character selected by the user.SOLUTION: A system according to an embodiment includes a character selection unit, a dialogue processing unit, a voice recognition unit, and an external service cooperation unit. The character selection component selects the character chosen by the user. The dialogue processor performs natural language processing of the dialogue between the user and the character selected by the character selector. The voice recognition unit converts a user's voice input into a text. The external service cooperation unit cooperates with an external service.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide a system that supports users through interactions with characters selected by the user, and there is room for improvement.

[0005] The system according to the embodiment aims to support the user through a dialogue with a character selected by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a character selection unit, a dialogue processing unit, a voice recognition unit, and an external service linkage unit. The character selection unit selects a character selected by a user. The dialogue processing unit performs natural language processing on a dialogue between the user and the character selected by the character selection unit. The voice recognition unit converts a voice input by the user into text. The external service linkage unit links with an external service. [Effects of the Invention]

[0007] The system according to the embodiment can support the user through a dialogue with a character selected by the user. [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 support system according to an embodiment of the present invention allows users to interact with a character of their choice, and a generation AI performs natural language processing to convert voice input into text and link with external services. This allows the support system to make users' lives more efficient and reduce stress.

[0029] A support system according to an embodiment includes a character selection unit, a dialogue processing unit, a voice recognition unit, and an external service linkage unit. The character selection unit selects a character selected by a user. For example, the user can choose from anime characters, animal characters, and the like. The dialogue processing unit performs natural language processing on the dialogue between the character selected by the character selection unit and the user. For example, if a user inputs, "I'm tired today," the generation AI responds, "Thank you for your hard work. Let's take a short break and refresh yourself." The voice recognition unit converts the user's voice input into text. For example, if a user inputs, "Tell me what your plans are for tomorrow," the generation AI responds, "I have a meeting tomorrow at 10:00 AM." The external service linkage unit links with external services. For example, it may link with a calendar app to manage schedules or with a weather forecast service to provide weather information. This allows the support system according to an embodiment to streamline users' lives and reduce stress.

[0030] The character selection unit can analyze the user's past interaction history and behavioral patterns and automatically recommend the most suitable character. For example, the character selection unit analyzes the user's past interaction history and scores the likeability rating for a specific character. For example, it preferentially recommends characters with which the user has had many interactions in the past. The character selection unit also analyzes the user's behavioral patterns and recommends the most suitable character. For example, if the user has many interactions during a specific time period, it recommends a character that is suitable for that time period. This makes it possible to provide characters that match the user's preferences.

[0031] The character selection unit can provide customization options based on the user's hobbies and interests. For example, the character selection unit collects the user's hobbies and interests in advance through a questionnaire and provides character customization options based on the collected information. For example, a sports character is suggested for a user who likes sports. The character selection unit also analyzes the user's past behavior history and provides customization options based on the user's hobbies and interests. For example, a sports character is suggested for a user who has had many sports-related interactions in the past. This makes it possible to provide characters that match the user's preferences.

[0032] The character selection unit can provide characters that correspond to different cultural spheres and languages. For example, the character selection unit builds a multilingual character database in order to provide characters that correspond to different cultural spheres and languages. For example, the character selection unit supports multiple languages ​​such as Japanese, English, and Chinese. Furthermore, the character selection unit customizes the character based on cultural background in order to provide characters that correspond to different cultural spheres. For example, the character selection unit provides characters based on Japanese culture and characters based on American culture. This allows the system to accommodate a global user base.

[0033] The dialogue processing unit can analyze the content of the user's dialogue and support long-term goal setting and progress management. The dialogue processing unit, for example, analyzes the content of the user's dialogue and builds a system that supports long-term goal setting. For example, if a user says, "I want to go on a diet," a specific goal is set. The dialogue processing unit also manages progress based on the content of the user's dialogue. For example, if a user says, "I exercised today," the progress is recorded. This can support the user in achieving their goals.

[0034] The dialogue processor can implement an advanced semantic analysis algorithm to deeply understand the context of the dialogue and grasp the user's intention more accurately. The dialogue processor implements an advanced semantic analysis algorithm to deeply understand the context of the dialogue. For example, when a user says, "What are your plans for tomorrow?", the dialogue processor generates an appropriate response by referring to the content of the previous dialogue. The dialogue processor also uses deep learning technology to more accurately grasp the user's intention. For example, the dialogue processor analyzes the intention behind the user's utterance and generates an appropriate response. This allows for the generation of a more appropriate response.

[0035] The dialogue processing unit can visualize the dialogue content and provide information in a format that is visually easy for the user to understand. The dialogue processing unit, for example, builds a system that visualizes the dialogue content and provides information in a format that is visually easy for the user to understand. For example, the dialogue content is displayed in graphs or charts. The dialogue processing unit also provides information using infographics. For example, visually representing the dialogue content promotes the user's understanding. This can promote understanding of the information.

[0036] The dialogue processor can add natural language processing functions that support different languages ​​and dialects, enabling multilingual support. The dialogue processor, for example, builds a system that adds natural language processing functions that support different languages ​​and dialects. For example, it supports multiple languages ​​such as Japanese, English, and Chinese. The dialogue processor also uses dialect recognition technology to support different dialects. For example, it supports dialects such as Kansai dialect and Tohoku dialect. This allows it to support global users.

[0037] The voice recognition unit can learn the characteristics of the user's voice and improve the accuracy of voice recognition. The voice recognition unit, for example, builds a system that learns the characteristics of the user's voice and improves the accuracy of voice recognition. For example, it analyzes the tone and accent of the user's voice. The voice recognition unit also optimizes the voice recognition algorithm based on the characteristics of the user's voice. For example, it analyzes the waveform of the user's voice and improves the accuracy of voice recognition. This enables more accurate voice input.

[0038] The speech recognition unit can automatically filter background sounds and noise when speech is input, thereby obtaining clear speech data. For example, the speech recognition unit constructs a system that automatically filters background sounds and noise when speech is input. For example, the speech recognition unit obtains clear speech data by using noise canceling technology. The speech recognition unit also uses a speech filtering algorithm to remove background sounds. For example, the speech recognition unit reduces noise by filtering a specific frequency band. This can improve the accuracy of speech recognition.

[0039] The voice recognition unit can use the voice recognition function to analyze the tone and speed of the user's voice and estimate the stress level and fatigue level. For example, the voice recognition unit uses the voice recognition function to analyze the tone and speed of the user's voice and build a system that estimates the stress level. For example, the voice recognition unit estimates stress based on the pitch of the voice and the speaking speed. The voice recognition unit also estimates the fatigue level based on the characteristics of the user's voice. For example, if the voice tone is low and the speaking speed is slow, it is determined that the fatigue level is high. This makes it possible to understand the user's health condition.

[0040] The speech recognition unit can combine gesture and facial expression recognition when inputting speech, thereby realizing more natural conversations. The speech recognition unit, for example, builds a system that combines gesture and facial expression recognition when inputting speech. For example, it analyzes the user's hand movements and facial expressions to complement the content of the conversation. The speech recognition unit also uses camera recognition technology to recognize gestures and facial expressions. For example, when a user raises their hand, it recognizes that movement and reflects it in the content of the conversation. This allows for more natural conversations.

[0041] The external service linking unit can strengthen linking with external services and integrate the user's life log data to provide comprehensive support. The external service linking unit, for example, strengthens linking with external services and builds a system that integrates the user's life log data. For example, it links with fitness apps and sleep trackers. The external service linking unit also provides comprehensive support based on the user's life log data. For example, it provides appropriate advice based on the user's activity data and health data. This makes it possible to provide comprehensive support.

[0042] The external service integration unit can acquire the user's health and fitness data through API integration and support health management. The external service integration unit, for example, builds a system that acquires the user's health and fitness data through API integration. For example, it integrates with a smartwatch or fitness app. The external service integration unit also supports health management based on the acquired data. For example, it provides appropriate advice based on the user's heart rate data and exercise amount data. This makes it easier to understand the user's health condition.

[0043] The external service integration unit can expand integration with external services and realize integration with smart home devices and IoT devices. For example, the external service integration unit expands integration with external services and builds a system that realizes integration with smart home devices and IoT devices. For example, it integrates with smart lights and smart thermostats. The external service integration unit also supports the user's life through smart home devices and IoT devices. For example, it adjusts the room temperature before the user returns home, making the user's life more convenient.

[0044] The external service integration unit can acquire user purchase history and preference data through the API and make personalized product recommendations. The external service integration unit, for example, builds a system that acquires user purchase history and preference data through the API. For example, it integrates with online shopping sites and point card systems. The external service integration unit also makes personalized product recommendations based on the acquired data. For example, it recommends related products based on the user's past purchase history. This makes it possible to provide products that match the user's preferences.

[0045] The dialogue processing unit can analyze the user's past dialogue history and generate individually optimized cheering messages. The dialogue processing unit, for example, builds a system that analyzes the user's past dialogue history and generates individually optimized cheering messages. For example, the dialogue processing unit generates a message based on words of encouragement that the user has received in the past. The dialogue processing unit also customizes cheering messages based on the user's dialogue history. For example, based on a message that the user received in a specific situation, the dialogue processing unit provides a cheering message again in a similar situation. This helps maintain the user's motivation.

[0046] The dialogue processing unit can dynamically change the content of the cheering message depending on the user's current situation and goals. The dialogue processing unit builds a system that dynamically changes the content of the cheering message depending on the user's current situation and goals, for example. For example, if the user is on a diet, a cheering message related to dieting is provided. The dialogue processing unit also changes the cheering message based on the user's progress toward achieving their goal. For example, if the user is approaching their goal, an encouraging message is provided. This allows for more appropriate support to be provided.

[0047] The dialogue processing unit can provide cheering messages not only as text but also as audio or visual content. The dialogue processing unit, for example, builds a system that provides cheering messages not only as text but also as audio or visual content. For example, the dialogue processing unit provides cheering messages using audio messages or animations. The dialogue processing unit also provides cheering messages in a format that suits the user's preferences. For example, if the user prefers audio messages, the dialogue processing unit provides cheering messages by audio. This allows the user to receive cheering messages in a format that suits their preferences.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The character selection unit can analyze the user's past interaction history and behavioral patterns and automatically recommend the most suitable character. For example, it can analyze the user's past interaction history and score the likeability rating for a specific character. It can preferentially recommend characters with which the user has had many interactions in the past. It can also analyze the user's behavioral patterns and recommend the most suitable character. For example, if the user has many interactions during a specific time period, it can recommend a character that is suitable for that time period. This makes it possible to provide characters that match the user's preferences.

[0050] The character selection unit can provide customization options based on the user's hobbies and interests. For example, the user's hobbies and interests can be collected in advance through a questionnaire, and character customization options can be provided based on the collected information. For a user who likes sports, a sports character can be suggested. In addition, the user's past behavior history can be analyzed to provide customization options based on the user's hobbies and interests. For a user who has had many sports-related interactions in the past, a sports character can be suggested. This makes it possible to provide characters that match the user's preferences.

[0051] The character selection unit can provide characters that correspond to different cultural spheres and languages. For example, in order to provide characters that correspond to different cultural spheres and languages, a multilingual character database is constructed. Multiple languages ​​such as Japanese, English, and Chinese are supported. Furthermore, in order to provide characters that correspond to different cultural spheres, customization is performed based on cultural background. Characters based on Japanese culture and characters based on American culture are provided. This allows for global user support.

[0052] The dialogue processing unit can analyze the content of the user's dialogue and support long-term goal setting and progress management. For example, a system can be constructed that analyzes the content of the user's dialogue and supports long-term goal setting. If the user says, "I want to go on a diet," a specific goal can be set. Progress management can also be performed based on the content of the user's dialogue. If the user says, "I exercised today," the progress can be recorded. This can help the user achieve their goals.

[0053] The dialogue processor can incorporate advanced semantic analysis algorithms to gain a deeper understanding of the context of the dialogue and grasp the user's intention more accurately. For example, an advanced semantic analysis algorithm is incorporated to gain a deeper understanding of the context of the dialogue. When a user asks, "What are your plans for tomorrow?", an appropriate response is generated by referring to the content of past dialogue. Deep learning technology is also used to more accurately grasp the user's intention. The intention behind the user's utterance is analyzed and an appropriate response is generated. This allows for the generation of a more appropriate response.

[0054] The dialogue processing unit can visualize the dialogue content and provide information in a format that is visually easy for the user to understand. For example, a system can be constructed that visualizes the dialogue content and provides information in a format that is visually easy for the user to understand. The dialogue content can be displayed in graphs or charts. Information can also be provided using infographics. Visually expressing the dialogue content can facilitate the user's understanding. This can facilitate understanding of the information.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The character selection unit selects a character of the user's choice. For example, the user can choose from anime characters, animal characters, etc. Step 2: The dialogue processor processes the dialogue between the character selected by the character selector and the user in natural language. For example, if the user says, "I'm tired today," the generator AI responds, "Thank you for your hard work. Let's take a short break and refresh yourself." Step 3: The speech recognition unit converts the user's voice input into text. For example, if the user says, "Tell me what's on my schedule for tomorrow," the AI ​​generator will respond with, "I have a meeting tomorrow at 10 a.m." Step 4: The external service integration unit integrates with external services. For example, it can integrate with a calendar app to manage schedules, or with a weather forecast service to provide weather information.

[0057] (Example 2) The support system according to an embodiment of the present invention allows users to interact with a character of their choice, and a generation AI performs natural language processing to convert voice input into text and link with external services. This allows the support system to make users' lives more efficient and reduce stress.

[0058] A support system according to an embodiment includes a character selection unit, a dialogue processing unit, a voice recognition unit, and an external service linkage unit. The character selection unit selects a character selected by a user. For example, the user can choose from anime characters, animal characters, and the like. The dialogue processing unit performs natural language processing on the dialogue between the character selected by the character selection unit and the user. For example, if a user inputs, "I'm tired today," the generation AI responds, "Thank you for your hard work. Let's take a short break and refresh yourself." The voice recognition unit converts the user's voice input into text. For example, if a user inputs, "Tell me what your plans are for tomorrow," the generation AI responds, "I have a meeting tomorrow at 10:00 AM." The external service linkage unit links with external services. For example, it may link with a calendar app to manage schedules or with a weather forecast service to provide weather information. This allows the support system according to an embodiment to streamline users' lives and reduce stress.

[0059] The character selection unit can analyze the user's past interaction history and behavioral patterns and automatically recommend the most suitable character. For example, the character selection unit analyzes the user's past interaction history and scores the likeability rating for a specific character. For example, it preferentially recommends characters with which the user has had many interactions in the past. The character selection unit also analyzes the user's behavioral patterns and recommends the most suitable character. For example, if the user has many interactions during a specific time period, it recommends a character that is suitable for that time period. This makes it possible to provide characters that match the user's preferences.

[0060] The character selection unit can analyze the user's psychological state in real time and suggest the most relaxing character. The character selection unit, for example, analyzes the user's tone of voice and facial expressions to estimate the psychological state in real time. For example, if it is determined that stress is high, it suggests a character with a relaxing effect. The character selection unit also analyzes the user's biometric data to estimate the psychological state. For example, it estimates the stress level based on heart rate and electrodermal activity. This can reduce the user's stress.

[0061] The character selection unit can use the emotion estimation function to dynamically change the character's facial expression and tone of voice according to the user's emotions. For example, the character selection unit uses the emotion estimation function to build a system that changes the character's facial expression in real time according to the user's emotions. For example, if the user is happy, the character will smile. The character selection unit also uses the emotion estimation function to change the character's tone of voice according to the user's emotions. For example, if the user is depressed, the character will speak in a gentle tone of voice. This allows for a more personalized experience.

[0062] The character selection unit can provide customization options based on the user's hobbies and interests. For example, the character selection unit collects the user's hobbies and interests in advance through a questionnaire and provides character customization options based on the collected information. For example, a sports character is suggested for a user who likes sports. The character selection unit also analyzes the user's past behavior history and provides customization options based on the user's hobbies and interests. For example, a sports character is suggested for a user who has had many sports-related interactions in the past. This makes it possible to provide characters that match the user's preferences.

[0063] The character selection unit can provide characters that correspond to different cultural spheres and languages. For example, the character selection unit builds a multilingual character database in order to provide characters that correspond to different cultural spheres and languages. For example, the character selection unit supports multiple languages ​​such as Japanese, English, and Chinese. Furthermore, the character selection unit customizes the character based on cultural background in order to provide characters that correspond to different cultural spheres. For example, the character selection unit provides characters based on Japanese culture and characters based on American culture. This allows the system to accommodate a global user base.

[0064] The character selection unit can use the emotion estimation function to analyze the emotional reaction of the user to the character selected by the user and continuously suggest the most suitable character. For example, the character selection unit uses the emotion estimation function to build a system that analyzes the emotional reaction of the user to the character selected by the user in real time. For example, if the user is happy, that character is continuously suggested. The character selection unit also suggests characters based on the user's emotional reaction. For example, if the user has a positive reaction to a particular character, that character is preferentially suggested. This makes it possible to provide characters that match the user's preferences.

[0065] The dialogue processing unit can analyze the content of the user's dialogue and support long-term goal setting and progress management. The dialogue processing unit, for example, analyzes the content of the user's dialogue and builds a system that supports long-term goal setting. For example, if a user says, "I want to go on a diet," a specific goal is set. The dialogue processing unit also manages progress based on the content of the user's dialogue. For example, if a user says, "I exercised today," the progress is recorded. This can support the user in achieving their goals.

[0066] The dialogue processor can implement an advanced semantic analysis algorithm to deeply understand the context of the dialogue and grasp the user's intention more accurately. The dialogue processor implements an advanced semantic analysis algorithm to deeply understand the context of the dialogue. For example, when a user says, "What are your plans for tomorrow?", the dialogue processor generates an appropriate response by referring to the content of the previous dialogue. The dialogue processor also uses deep learning technology to more accurately grasp the user's intention. For example, the dialogue processor analyzes the intention behind the user's utterance and generates an appropriate response. This allows for the generation of a more appropriate response.

[0067] The dialogue processor can use the emotion estimation function to dynamically adjust the dialogue content according to the user's emotions and generate more personalized responses. The dialogue processor, for example, uses the emotion estimation function to build a system that dynamically adjusts the dialogue content according to the user's emotions. For example, if the user is feeling down, the dialogue processor can provide words of encouragement. The dialogue processor also uses an emotion estimation algorithm to generate responses according to the user's emotions. For example, the dialogue processor can analyze the user's tone of voice and facial expressions to estimate the user's emotions. This allows the dialogue processor to provide more personalized responses.

[0068] The dialogue processing unit can visualize the dialogue content and provide information in a format that is visually easy for the user to understand. The dialogue processing unit, for example, builds a system that visualizes the dialogue content and provides information in a format that is visually easy for the user to understand. For example, the dialogue content is displayed in graphs or charts. The dialogue processing unit also provides information using infographics. For example, visually representing the dialogue content promotes the user's understanding. This can promote understanding of the information.

[0069] The dialogue processor can add natural language processing functions that support different languages ​​and dialects, enabling multilingual support. The dialogue processor, for example, builds a system that adds natural language processing functions that support different languages ​​and dialects. For example, it supports multiple languages ​​such as Japanese, English, and Chinese. The dialogue processor also uses dialect recognition technology to support different dialects. For example, it supports dialects such as Kansai dialect and Tohoku dialect. This allows it to support global users.

[0070] The dialogue processing unit can use the emotion estimation function to monitor the user's emotional changes during a dialogue in real time and provide encouraging messages at appropriate times. For example, the dialogue processing unit uses the emotion estimation function to build a system that monitors the user's emotional changes during a dialogue in real time. For example, if the user is feeling down, it provides an encouraging message. The dialogue processing unit also provides encouraging messages based on the user's emotional changes. For example, if the user is happy, it provides a congratulatory message. This helps maintain the user's motivation.

[0071] The voice recognition unit can learn the characteristics of the user's voice and improve the accuracy of voice recognition. The voice recognition unit, for example, builds a system that learns the characteristics of the user's voice and improves the accuracy of voice recognition. For example, it analyzes the tone and accent of the user's voice. The voice recognition unit also optimizes the voice recognition algorithm based on the characteristics of the user's voice. For example, it analyzes the waveform of the user's voice and improves the accuracy of voice recognition. This enables more accurate voice input.

[0072] The speech recognition unit can automatically filter background sounds and noise when speech is input, thereby obtaining clear speech data. For example, the speech recognition unit constructs a system that automatically filters background sounds and noise when speech is input. For example, the speech recognition unit obtains clear speech data by using noise canceling technology. The speech recognition unit also uses a speech filtering algorithm to remove background sounds. For example, the speech recognition unit reduces noise by filtering a specific frequency band. This can improve the accuracy of speech recognition.

[0073] The speech recognition unit can use the emotion estimation function to analyze the user's emotion at the time of speech input and generate a response according to the emotion. For example, the speech recognition unit uses the emotion estimation function to build a system that analyzes the user's emotion at the time of speech input. For example, the speech recognition unit analyzes the tone and speed of the user's voice to estimate the emotion. The speech recognition unit also uses an emotion estimation algorithm to generate a response according to the user's emotion. For example, if the user is feeling down, it may provide words of encouragement. This allows for a more personalized experience.

[0074] The voice recognition unit can use the voice recognition function to analyze the tone and speed of the user's voice and estimate the stress level and fatigue level. For example, the voice recognition unit uses the voice recognition function to analyze the tone and speed of the user's voice and build a system that estimates the stress level. For example, the voice recognition unit estimates stress based on the pitch of the voice and the speaking speed. The voice recognition unit also estimates the fatigue level based on the characteristics of the user's voice. For example, if the voice tone is low and the speaking speed is slow, it is determined that the fatigue level is high. This makes it possible to understand the user's health condition.

[0075] The speech recognition unit can combine gesture and facial expression recognition when inputting speech, thereby realizing more natural conversations. The speech recognition unit, for example, builds a system that combines gesture and facial expression recognition when inputting speech. For example, it analyzes the user's hand movements and facial expressions to complement the content of the conversation. The speech recognition unit also uses camera recognition technology to recognize gestures and facial expressions. For example, when a user raises their hand, it recognizes that movement and reflects it in the content of the conversation. This allows for more natural conversations.

[0076] The speech recognition unit can use the emotion estimation function to monitor the user's emotion during speech input in real time and provide appropriate feedback. The speech recognition unit, for example, uses the emotion estimation function to build a system that monitors the user's emotion during speech input in real time. For example, the speech recognition unit analyzes the tone and speed of the user's voice to estimate the emotion. The speech recognition unit also provides feedback according to the user's emotion. For example, if the user is feeling down, it provides an encouraging message. This makes it possible to respond according to the user's emotion.

[0077] The external service linking unit can strengthen linking with external services and integrate the user's life log data to provide comprehensive support. The external service linking unit, for example, strengthens linking with external services and builds a system that integrates the user's life log data. For example, it links with fitness apps and sleep trackers. The external service linking unit also provides comprehensive support based on the user's life log data. For example, it provides appropriate advice based on the user's activity data and health data. This makes it possible to provide comprehensive support.

[0078] The external service integration unit can acquire the user's health and fitness data through API integration and support health management. The external service integration unit, for example, builds a system that acquires the user's health and fitness data through API integration. For example, it integrates with a smartwatch or fitness app. The external service integration unit also supports health management based on the acquired data. For example, it provides appropriate advice based on the user's heart rate data and exercise amount data. This makes it easier to understand the user's health condition.

[0079] The external service cooperation unit can use the emotion estimation function to analyze the user's emotional state based on data acquired from an external service and provide appropriate advice. The external service cooperation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state based on data acquired from an external service. For example, the external service cooperation unit estimates emotions based on the user's health data and activity data. The external service cooperation unit also provides advice according to the user's emotional state. For example, if the user is feeling stressed, the external service cooperation unit suggests ways to relax. This makes it possible to provide support according to the user's emotions.

[0080] The external service integration unit can expand integration with external services and realize integration with smart home devices and IoT devices. For example, the external service integration unit expands integration with external services and builds a system that realizes integration with smart home devices and IoT devices. For example, it integrates with smart lights and smart thermostats. The external service integration unit also supports the user's life through smart home devices and IoT devices. For example, it adjusts the room temperature before the user returns home, making the user's life more convenient.

[0081] The external service integration unit can acquire user purchase history and preference data through the API and make personalized product recommendations. The external service integration unit, for example, builds a system that acquires user purchase history and preference data through the API. For example, it integrates with online shopping sites and point card systems. The external service integration unit also makes personalized product recommendations based on the acquired data. For example, it recommends related products based on the user's past purchase history. This makes it possible to provide products that match the user's preferences.

[0082] The external service cooperation unit can use the emotion estimation function to provide services according to the user's emotions based on data acquired from external services. For example, the external service cooperation unit uses the emotion estimation function to build a system that analyzes the user's emotions based on data acquired from external services. For example, emotions are estimated based on the user's purchase history and preference data. The external service cooperation unit also provides services according to the user's emotions. For example, if the user is feeling stressed, methods for relaxation are suggested. This makes it possible to respond according to the user's emotions.

[0083] The dialogue processing unit can analyze the user's past dialogue history and generate individually optimized cheering messages. The dialogue processing unit, for example, builds a system that analyzes the user's past dialogue history and generates individually optimized cheering messages. For example, the dialogue processing unit generates a message based on words of encouragement that the user has received in the past. The dialogue processing unit also customizes cheering messages based on the user's dialogue history. For example, based on a message that the user received in a specific situation, the dialogue processing unit provides a cheering message again in a similar situation. This helps maintain the user's motivation.

[0084] The dialogue processing unit can dynamically change the content of the cheering message depending on the user's current situation and goals. The dialogue processing unit builds a system that dynamically changes the content of the cheering message depending on the user's current situation and goals, for example. For example, if the user is on a diet, a cheering message related to dieting is provided. The dialogue processing unit also changes the cheering message based on the user's progress toward achieving their goal. For example, if the user is approaching their goal, an encouraging message is provided. This allows for more appropriate support to be provided.

[0085] The dialogue processing unit uses an emotion estimation function to generate cheering messages in real time according to the user's emotions, thereby maintaining motivation. The dialogue processing unit, for example, uses the emotion estimation function to build a system that generates cheering messages in real time according to the user's emotions. For example, if the user is feeling down, an encouraging message is provided. The dialogue processing unit also uses an emotion estimation algorithm to generate cheering messages according to the user's emotions. For example, the dialogue processing unit analyzes the user's tone of voice and facial expression to estimate the user's emotions. This allows the user to maintain motivation.

[0086] The dialogue processing unit can provide cheering messages not only as text but also as audio or visual content. The dialogue processing unit, for example, builds a system that provides cheering messages not only as text but also as audio or visual content. For example, the dialogue processing unit provides cheering messages using audio messages or animations. The dialogue processing unit also provides cheering messages in a format that suits the user's preferences. For example, if the user prefers audio messages, the dialogue processing unit provides cheering messages by audio. This allows the user to receive cheering messages in a format that suits their preferences.

[0087] The dialogue processing unit can use the emotion estimation function to monitor changes in the user's emotions and provide a message of encouragement at the optimal timing. For example, the dialogue processing unit uses the emotion estimation function to build a system that monitors changes in the user's emotions in real time. For example, if the user is feeling down, it provides an encouraging message. The dialogue processing unit also provides a message of encouragement based on changes in the user's emotions. For example, if the user is happy, it provides a congratulatory message. This helps maintain the user's motivation.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The character selection unit can analyze the user's past interaction history and behavioral patterns and automatically recommend the most suitable character. For example, it can analyze the user's past interaction history and score the likeability rating for a specific character. It can preferentially recommend characters with which the user has had many interactions in the past. It can also analyze the user's behavioral patterns and recommend the most suitable character. For example, if the user has many interactions during a specific time period, it can recommend a character that is suitable for that time period. This makes it possible to provide characters that match the user's preferences.

[0090] The character selection unit can analyze the user's psychological state in real time and suggest the most relaxing character. For example, it can analyze the user's tone of voice and facial expressions to estimate the psychological state in real time. If it is determined that stress is high, it can suggest a character with a relaxing effect. It can also analyze the user's biometric data to estimate the psychological state. For example, it can estimate the stress level based on heart rate and electrodermal activity. This can reduce the user's stress.

[0091] The character selection unit can use the emotion estimation function to dynamically change the character's facial expression and tone of voice according to the user's emotions. For example, a system can be built using the emotion estimation function to change the character's facial expression in real time according to the user's emotions. If the user is happy, the character will smile. The emotion estimation function can also be used to change the character's tone of voice according to the user's emotions. If the user is depressed, the character will speak in a gentle tone of voice. This can provide a more personalized experience.

[0092] The character selection unit can provide customization options based on the user's hobbies and interests. For example, the user's hobbies and interests can be collected in advance through a questionnaire, and character customization options can be provided based on the collected information. For a user who likes sports, a sports character can be suggested. In addition, the user's past behavior history can be analyzed to provide customization options based on the user's hobbies and interests. For a user who has had many sports-related interactions in the past, a sports character can be suggested. This makes it possible to provide characters that match the user's preferences.

[0093] The character selection unit can provide characters that correspond to different cultural spheres and languages. For example, in order to provide characters that correspond to different cultural spheres and languages, a multilingual character database is constructed. Multiple languages ​​such as Japanese, English, and Chinese are supported. Furthermore, in order to provide characters that correspond to different cultural spheres, customization is performed based on cultural background. Characters based on Japanese culture and characters based on American culture are provided. This allows for global user support.

[0094] The dialogue processing unit can analyze the content of the user's dialogue and support long-term goal setting and progress management. For example, a system can be constructed that analyzes the content of the user's dialogue and supports long-term goal setting. If the user says, "I want to go on a diet," a specific goal can be set. Progress management can also be performed based on the content of the user's dialogue. If the user says, "I exercised today," the progress can be recorded. This can help the user achieve their goals.

[0095] The dialogue processor can incorporate advanced semantic analysis algorithms to gain a deeper understanding of the context of the dialogue and grasp the user's intention more accurately. For example, an advanced semantic analysis algorithm is incorporated to gain a deeper understanding of the context of the dialogue. When a user asks, "What are your plans for tomorrow?", an appropriate response is generated by referring to the content of past dialogue. Deep learning technology is also used to more accurately grasp the user's intention. The intention behind the user's utterance is analyzed and an appropriate response is generated. This allows for the generation of a more appropriate response.

[0096] The dialogue processor can use the emotion estimation function to dynamically adjust the dialogue content according to the user's emotions and generate more personalized responses. For example, a system can be built that uses the emotion estimation function to dynamically adjust the dialogue content according to the user's emotions. If the user is feeling down, it can provide words of encouragement. In addition, an emotion estimation algorithm is used to generate responses according to the user's emotions. The user's tone of voice and facial expressions are analyzed to estimate their emotions. This allows for more personalized responses to be provided.

[0097] The dialogue processing unit can visualize the dialogue content and provide information in a format that is visually easy for the user to understand. For example, a system can be constructed that visualizes the dialogue content and provides information in a format that is visually easy for the user to understand. The dialogue content can be displayed in graphs or charts. Information can also be provided using infographics. Visually expressing the dialogue content can facilitate the user's understanding. This can facilitate understanding of the information.

[0098] The dialogue processing unit can use the emotion estimation function to monitor the user's emotional changes during a dialogue in real time and provide encouraging messages at appropriate times. For example, a system can be constructed that uses the emotion estimation function to monitor the user's emotional changes during a dialogue in real time. If the user is feeling down, an encouraging message can be provided. Also, a cheering message can be provided based on the user's emotional changes. If the user is happy, a congratulatory message can be provided. This helps maintain the user's motivation.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The character selection unit selects a character of the user's choice. For example, the user can choose from anime characters, animal characters, etc. Step 2: The dialogue processor processes the dialogue between the character selected by the character selector and the user in natural language. For example, if the user says, "I'm tired today," the generator AI responds, "Thank you for your hard work. Let's take a short break and refresh yourself." Step 3: The speech recognition unit converts the user's voice input into text. For example, if the user says, "Tell me what's on my schedule for tomorrow," the AI ​​generator will respond with, "I have a meeting tomorrow at 10 a.m." Step 4: The external service integration unit integrates with external services. For example, it can integrate with a calendar app to manage schedules, or with a weather forecast service to provide weather information.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

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

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] 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.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 character selection unit for selecting a character selected by the user; a dialogue processing unit that performs natural language processing on a dialogue between a user and the character selected by the character selection unit; a speech recognition unit that converts a user's voice input into text; an external service linking unit that links with an external service; A system characterized by:

2. The character selection unit Analyzes the user's psychological state in real time and suggests the most relaxing character 2. The system of claim 1.

3. The dialogue processing unit Analyzes user interactions to support long-term goal setting and progress management 2. The system of claim 1.

4. The voice recognition unit Learns the characteristics of the user's voice and improves the accuracy of voice recognition 2. The system of claim 1.

5. The external service cooperation unit Strengthen collaboration with external services and integrate user life log data to provide comprehensive support 2. The system of claim 1.

6. The character selection unit Dynamically change the character's facial expression and tone of voice according to the user's emotions 2. The system of claim 1.

7. The dialogue processing unit Dynamically adjust dialogue content based on user emotions to generate more personalized responses 2. The system of claim 1.

8. The voice recognition unit Analyzes the user's emotions during voice input and generates responses according to those emotions 2. The system of claim 1.

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