System

Abstract

An object of a system according to an embodiment is to generate a response closely following an emotion of a user and reduce a sense of loneliness.SOLUTION: A system according to an embodiment includes a generative AI and an emotion engine. The generation AI includes an analysis unit and a response generation unit. The analysis unit analyzes the input of the natural language from the user and understands the content thereof. The response generation unit generates a response on the basis of the content analyzed by the analysis unit. The emotion engine analyzes the user's emotions and provides the results to the response generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to generate responses that are in tune with the user's emotions, and are therefore not sufficiently effective in reducing feelings of loneliness.

[0005] The system according to the embodiment aims to generate a response that is in tune with the user's emotions and reduce the sense of loneliness. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI and an emotion engine. The generation AI includes an analysis unit and a response generation unit. The analysis unit analyzes natural language input from a user and understands its content. The response generation unit generates a response based on the content analyzed by the analysis unit. The emotion engine analyzes the user's emotion and provides the result to the response generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a response that is in tune with the user's emotions and reduce the sense of loneliness. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The personal assistant system according to the embodiment of the present invention is a system that provides a personal assistant that is considerate to people's hearts by combining a generative AI and an emotion engine. As a result, the personal assistant system can alleviate the user's sense of loneliness and provide emotional support.

[0029] A personal assistant system according to an embodiment includes a generation AI, an emotion engine, an analysis unit, and a response generation unit. The generation AI includes an analysis unit that analyzes natural language input from a user and understands its content. For example, if a user says, "I'm tired today," the generation AI analyzes the meaning of the words and generates an appropriate response. The generation AI analyzes natural language using, for example, a text generation AI (e.g., LLM). The generation AI can also analyze voice or text input using, for example, a multimodal generation AI. The analysis unit analyzes the user's input and understands its content using, for example, natural language processing technology. The response generation unit generates a response based on the content analyzed by the analysis unit. For example, the response generation unit includes an algorithm that generates an appropriate response to the user's input. The response generation unit provides appropriate feedback to the user based on, for example, the response generated by the generation AI. The emotion engine analyzes the user's emotions and provides the result to the response generation unit. For example, the emotion engine analyzes the user's tone of voice and facial expression to estimate the emotion. The emotion engine analyzes the user's emotions using, for example, an emotion analysis algorithm, allowing the personal assistant system according to the embodiment to analyze the user's natural language input and emotions and generate an appropriate response, thereby reducing the user's sense of loneliness.

[0030] The analysis unit can learn the user's past conversation history and generate responses based on the user's individual patterns and preferences. For example, the analysis unit uses a generation AI to analyze the user's past conversation history and learn phrases and topics frequently used by the user. For example, related topics are provided based on the hobbies and interests that the user often talks about. The analysis unit also uses a generation AI to identify the user's preferences and patterns based on the user's past conversation history and generate individually customized responses. For example, topics related to the user's favorite movies and music are provided. The analysis unit also uses a generation AI to learn the user's past conversation history and analyze changes and patterns in the user's emotions. For example, topics that will help the user relax when they are feeling stressed. This allows the system to learn the user's past conversation history and generate individually customized responses, providing a more personalized experience.

[0031] Data can be synchronized in real time between devices, allowing users to have a consistent experience from any device. Data can be synchronized in real time between devices, allowing users to access the latest information from any device. For example, information entered on a smartphone is instantly updated on the glasses or earphones. Data synchronization between devices can also be automated, eliminating the need for users to manually synchronize. For example, data can be synchronized automatically using a cloud service. Data synchronization between devices can also be performed securely to protect user privacy. For example, data can be synchronized using encryption technology. This allows seamless data synchronization between devices, allowing users to have a consistent experience from any device.

[0032] By providing a different interface for each device, it is possible to achieve optimal operability depending on the user's usage scenario. By providing a different interface for each device, it is possible to enable users to operate intuitively. For example, touch operation is provided for smartphones, gaze operation is provided for glasses, and voice operation is provided for earphones. In addition, interfaces are designed according to the characteristics of each device to achieve optimal operability depending on the user's usage scenario. For example, voice operation via earphones can be prioritized during the commute, and touch operation via a smartphone at home. In addition, by providing a different interface for each device, it is possible to enable users to switch between devices seamlessly. For example, operations on a smartphone can be carried over to the glasses. In this way, by providing a different interface for each device, it is possible to achieve optimal operability depending on the user's usage scenario.

[0033] A multi-device-compatible personal assistant can work with smart home devices to improve comfort within the home. For example, a multi-device-compatible personal assistant can work with smart home devices to automatically adjust the home environment. For example, it can adjust the brightness and temperature of lights according to the user's emotional state. It can also work with smart home devices to achieve automation based on the user's lifestyle patterns. For example, it can turn on the air conditioner when the user returns home. A multi-device-compatible personal assistant can also work with smart home devices to enhance home security. For example, it can automatically manage door locks and surveillance cameras while the user is out. In this way, working with smart home devices can improve comfort within the home.

[0034] A multi-device-compatible personal assistant can work in conjunction with an in-vehicle device to improve safety and comfort while driving. A multi-device-compatible personal assistant, for example, can work in conjunction with an in-vehicle device to improve safety while driving. For example, it can analyze a user's emotional state while driving and issue a warning if their attention is declining. It can also work in conjunction with an in-vehicle device to improve comfort while driving. For example, it can adjust music or navigation settings according to the user's emotional state. A multi-device-compatible personal assistant can also work in conjunction with an in-vehicle device to reduce stress while driving. For example, if a user is feeling stressed, it can recommend relaxing music. In this way, by working in conjunction with an in-vehicle device, it can improve safety and comfort while driving.

[0035] Speech recognition technology can analyze and accommodate a user's dialect or accent. For example, algorithms for analyzing a user's dialect or accent are developed. For example, regional dialect data is learned to realize dialect-compatible speech recognition. In addition, user speech data is collected and a speech recognition model that accommodates dialects and accents is trained. For example, the dialect spoken by the user is automatically detected to improve recognition accuracy. In addition, speech recognition technology is advanced to provide a real-time speech conversion function that accommodates a user's dialect or accent. For example, a function is implemented to convert dialects into standard Japanese for recognition. In this way, by advancing speech recognition technology, it can accommodate a user's dialect or accent.

[0036] A text interface can provide an auto-completion function that is tailored to the user's input speed and style. For example, the text interface analyzes the user's input speed and provides auto-completion at an appropriate time. For example, if the input is slow, completion candidates are displayed earlier. The text interface also learns the user's input style and provides an individually customized auto-completion function. For example, phrases and words frequently used by the user are preferentially displayed as completion candidates. The text interface also provides a dynamic auto-completion function that is tailored to the user's input speed and style. For example, completion candidates are updated in real time according to the flow of input. This makes it possible to improve input efficiency by providing an auto-completion function that is tailored to the user's input speed and style.

[0037] The voice / text interface can be integrated with video calls and chatbots to integrate multiple communication methods. For example, the voice / text interface can be integrated with video calls to enable users to communicate via voice or text even during a video call. For example, it can enable users to send text messages during a video call. The voice / text interface can also be integrated with chatbots to enable users to communicate via voice or text with the chatbot. For example, the chatbot can respond in text to questions asked via voice. The voice / text interface can also be integrated with video calls and chatbots to enable users to seamlessly switch communication methods. For example, it can provide a function to switch from voice to text during a video call. In this way, integration with video calls and chatbots can integrate multiple communication methods and improve user convenience.

[0038] A voice / text interface can be integrated with games and entertainment apps to improve the user experience. For example, a voice / text interface can be integrated with a game to allow users to communicate with in-game characters by voice or text. For example, a voice command can be executed by a character in the game. A voice / text interface can also be integrated with an entertainment app to allow users to control in-app content by voice or text. For example, a voice command can be used to play or stop a video. A voice / text interface can also be integrated with games and entertainment apps to allow users to seamlessly control the app by voice or text. For example, a text chat function can be provided during a game. In this way, integration with games and entertainment apps can improve the user experience.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] Personal assistant systems can also monitor the user's health and provide appropriate advice. For example, they can analyze the user's heart rate and sleep patterns to assess their health. They can also suggest healthy lifestyle habits based on the user's diet and exercise records. Furthermore, if the user feels stressed, they can provide advice on relaxation methods and stress management. This provides comprehensive support for the user's health.

[0041] Personal assistant systems can also assist users in managing their schedules. For example, they can analyze the user's calendar and remind them of important appointments and tasks. They can also suggest optimal time management methods based on the user's schedule. Furthermore, when the user is busy, they can provide advice on efficient task management. This can improve the efficiency of the user's schedule management and reduce stress.

[0042] Personal assistant systems can also recommend content based on a user's hobbies and interests. For example, they can analyze a user's favorite movies and music and suggest new related content. They can also recommend content that may be of interest to the user based on the user's past viewing history. Furthermore, if a user is interested in a particular genre, they can provide events and news related to that genre. This allows for a personalized experience tailored to the user's hobbies and interests.

[0043] Personal assistant systems can also assist users in planning their trips. For example, they can analyze the user's travel destinations and itinerary and suggest optimal travel plans. They can also recommend tourist spots and restaurants based on the user's preferences. Furthermore, they can provide users with real-time information they need while traveling. This can improve the user's travel experience.

[0044] Personal assistant systems can also monitor a user's learning progress and provide appropriate learning resources. For example, they can analyze the topic the user is studying and recommend related learning materials and reference materials. They can also provide a customized learning plan based on the user's learning style. Furthermore, if the user is struggling to understand something, they can provide additional support and advice. This can improve the user's learning efficiency.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The analyzer analyzes the natural language input from the user and understands its content. For example, if the user says, "I'm tired today," the meaning of the words is analyzed and an appropriate response is generated. The analyzer uses natural language processing technology to analyze the user's input and understand its content. Step 2: The response generation unit generates a response based on the content analyzed by the analysis unit. For example, the response generation unit may be equipped with an algorithm that generates an appropriate response to the user's input, and provide appropriate feedback to the user based on the response generated by the generation AI. Step 3: The emotion engine analyzes the user's emotion and provides the result to the response generator. For example, the emotion engine analyzes the user's tone of voice and facial expression to infer emotion. The emotion engine uses an emotion analysis algorithm to analyze the user's emotion.

[0047] (Example 2) The personal assistant system according to the embodiment of the present invention is a system that provides a personal assistant that is considerate to people's hearts by combining a generative AI and an emotion engine. As a result, the personal assistant system can alleviate the user's sense of loneliness and provide emotional support.

[0048] A personal assistant system according to an embodiment includes a generation AI, an emotion engine, an analysis unit, and a response generation unit. The generation AI includes an analysis unit that analyzes natural language input from a user and understands its content. For example, if a user says, "I'm tired today," the generation AI analyzes the meaning of the words and generates an appropriate response. The generation AI analyzes natural language using, for example, a text generation AI (e.g., LLM). The generation AI can also analyze voice or text input using, for example, a multimodal generation AI. The analysis unit analyzes the user's input and understands its content using, for example, natural language processing technology. The response generation unit generates a response based on the content analyzed by the analysis unit. For example, the response generation unit includes an algorithm that generates an appropriate response to the user's input. The response generation unit provides appropriate feedback to the user based on, for example, the response generated by the generation AI. The emotion engine analyzes the user's emotions and provides the result to the response generation unit. For example, the emotion engine analyzes the user's tone of voice and facial expression to estimate the emotion. The emotion engine analyzes the user's emotions using, for example, an emotion analysis algorithm, allowing the personal assistant system according to the embodiment to analyze the user's natural language input and emotions and generate an appropriate response, thereby reducing the user's sense of loneliness.

[0049] The analysis unit can learn the user's past conversation history and generate responses based on the user's individual patterns and preferences. For example, the analysis unit uses a generation AI to analyze the user's past conversation history and learn phrases and topics frequently used by the user. For example, related topics are provided based on the hobbies and interests that the user often talks about. The analysis unit also uses a generation AI to identify the user's preferences and patterns based on the user's past conversation history and generate individually customized responses. For example, topics related to the user's favorite movies and music are provided. The analysis unit also uses a generation AI to learn the user's past conversation history and analyze changes and patterns in the user's emotions. For example, topics that will help the user relax when they are feeling stressed. This allows the system to learn the user's past conversation history and generate individually customized responses, providing a more personalized experience.

[0050] The emotion engine analyzes the user's facial expressions and gestures to perform more accurate emotion estimation. The emotion engine, for example, analyzes the user's facial expressions in real time to detect changes in emotions. For example, a camera is used to analyze the user's smile or wrinkles between the eyebrows to estimate emotions. The emotion engine also analyzes the user's gestures to detect changes in emotions. For example, it analyzes hand movements and changes in posture to estimate the user's emotional state. The emotion engine also analyzes the user's facial expressions and gestures in an integrated manner to perform more accurate emotion estimation. For example, it estimates emotions by combining facial expressions and hand movements. In this way, more accurate emotion estimation is possible by analyzing the user's facial expressions and gestures.

[0051] The emotion estimation function can automatically recommend music and videos according to the user's emotions and provide content that provides emotional support. For example, the emotion estimation function automatically recommends music according to the user's emotional state. For example, if the user is feeling sad, it recommends relaxing music. The emotion estimation function also recommends appropriate video content based on the user's emotions. For example, if the user is feeling stressed, it provides relaxing videos. The emotion estimation function also automatically generates content that combines music and videos according to the user's emotions. For example, if the user wants to relax, it provides content that combines relaxing music and videos. In this way, it is possible to provide emotional support content by recommending music and videos according to the user's emotions.

[0052] A personal assistant that combines generative AI and an emotion engine can analyze a student's learning progress in real time and provide appropriate feedback. A personal assistant that combines generative AI and an emotion engine can, for example, analyze a student's learning progress in real time and provide appropriate feedback. For example, it could provide words of encouragement or additional learning resources for topics that a student is struggling to understand. It can also analyze a student's emotional state and suggest approaches to increase their motivation to learn. For example, if a student is tired, it could send a message encouraging them to take a break. It can also use generative AI and an emotion engine to provide customized study plans based on a student's learning style and preferences. For example, for students who prefer visual learning, it could suggest a study plan that includes a lot of visual content. In this way, it can analyze a student's learning progress in real time and provide appropriate feedback to increase their motivation to learn.

[0053] A personal assistant that combines generative AI and an emotion engine can analyze a patient's emotional state and provide appropriate counseling. A personal assistant that combines generative AI and an emotion engine can, for example, analyze a patient's emotional state and provide appropriate counseling. For example, if a patient is feeling anxious, it can provide advice to help them relax. It can also learn the patient's past counseling history and provide individually customized counseling. For example, it can adjust current counseling based on approaches that have been effective for the patient in the past. It can also use an emotion engine to monitor a patient's emotional state in real time and adjust the content of counseling as needed. For example, if a patient is feeling stressed, it can provide advice on stress management. In this way, it is possible to support mental health by analyzing a patient's emotional state and providing appropriate counseling.

[0054] The emotion estimation function provides feedback according to the user's emotions in real time, thereby promoting the user's self-growth. The emotion estimation function, for example, provides feedback according to the user's emotional state in real time. For example, if the user is feeling down, it provides words of encouragement. It also analyzes the user's emotional state and provides specific advice to promote self-growth. For example, if the user is feeling stressed, it suggests methods for managing stress. It also uses the emotion estimation function to provide feedback based on the user's emotional state to support self-growth. For example, if the user is feeling a sense of accomplishment, it provides feedback to reinforce that emotion. In this way, it is possible to promote self-growth by providing feedback according to the user's emotions in real time.

[0055] Data can be synchronized in real time between devices, allowing users to have a consistent experience from any device. Data can be synchronized in real time between devices, allowing users to access the latest information from any device. For example, information entered on a smartphone is instantly updated on the glasses or earphones. Data synchronization between devices can also be automated, eliminating the need for users to manually synchronize. For example, data can be synchronized automatically using a cloud service. Data synchronization between devices can also be performed securely to protect user privacy. For example, data can be synchronized using encryption technology. This allows seamless data synchronization between devices, allowing users to have a consistent experience from any device.

[0056] By providing a different interface for each device, it is possible to achieve optimal operability depending on the user's usage scenario. By providing a different interface for each device, it is possible to enable users to operate intuitively. For example, touch operation is provided for smartphones, gaze operation is provided for glasses, and voice operation is provided for earphones. In addition, interfaces are designed according to the characteristics of each device to achieve optimal operability depending on the user's usage scenario. For example, voice operation via earphones can be prioritized during the commute, and touch operation via a smartphone at home. In addition, by providing a different interface for each device, it is possible to enable users to switch between devices seamlessly. For example, operations on a smartphone can be carried over to the glasses. In this way, by providing a different interface for each device, it is possible to achieve optimal operability depending on the user's usage scenario.

[0057] The emotion estimation function can select the optimal response method depending on the device the user is using. For example, the emotion estimation function selects the optimal response method depending on the device the user is using. For example, if the user is using a smartphone, it provides a text response, and if the user is using earphones, it provides a voice response. The emotion estimation function also analyzes the user's emotional state and provides the optimal response method depending on the device they are using. For example, if the user is feeling stressed, it provides a relaxing voice response. The emotion estimation function also dynamically adjusts the response method depending on the device the user is using. For example, if the user switches from a smartphone to earphones, it changes to a voice response. This allows for more natural communication by selecting the optimal response method depending on the device the user is using.

[0058] A multi-device-compatible personal assistant can work with smart home devices to improve comfort within the home. For example, a multi-device-compatible personal assistant can work with smart home devices to automatically adjust the home environment. For example, it can adjust the brightness and temperature of lights according to the user's emotional state. It can also work with smart home devices to achieve automation based on the user's lifestyle patterns. For example, it can turn on the air conditioner when the user returns home. A multi-device-compatible personal assistant can also work with smart home devices to enhance home security. For example, it can automatically manage door locks and surveillance cameras while the user is out. In this way, working with smart home devices can improve comfort within the home.

[0059] A multi-device-compatible personal assistant can work in conjunction with an in-vehicle device to improve safety and comfort while driving. A multi-device-compatible personal assistant, for example, can work in conjunction with an in-vehicle device to improve safety while driving. For example, it can analyze a user's emotional state while driving and issue a warning if their attention is declining. It can also work in conjunction with an in-vehicle device to improve comfort while driving. For example, it can adjust music or navigation settings according to the user's emotional state. A multi-device-compatible personal assistant can also work in conjunction with an in-vehicle device to reduce stress while driving. For example, if a user is feeling stressed, it can recommend relaxing music. In this way, by working in conjunction with an in-vehicle device, it can improve safety and comfort while driving.

[0060] The emotion estimation function can provide emotional support tailored to the device the user is using, thereby improving user satisfaction. The emotion estimation function, for example, provides emotional support tailored to the device the user is using. For example, if the user is using a smartphone, an encouraging text message can be provided. The emotion estimation function can also analyze the user's emotional state and provide emotional support tailored to the device the user is using. For example, if the user is using earphones, audio advice to help them relax can be provided. The emotion estimation function can also be used to dynamically adjust emotional support tailored to the device the user is using. For example, if the user is using glasses, visual relaxation content can be provided. This can improve user satisfaction by providing emotional support tailored to the device the user is using.

[0061] Speech recognition technology can analyze and accommodate a user's dialect or accent. For example, algorithms for analyzing a user's dialect or accent are developed. For example, regional dialect data is learned to realize dialect-compatible speech recognition. In addition, user speech data is collected and a speech recognition model that accommodates dialects and accents is trained. For example, the dialect spoken by the user is automatically detected to improve recognition accuracy. In addition, speech recognition technology is advanced to provide a real-time speech conversion function that accommodates a user's dialect or accent. For example, a function is implemented to convert dialects into standard Japanese for recognition. In this way, by advancing speech recognition technology, it can accommodate a user's dialect or accent.

[0062] A text interface can provide an auto-completion function that is tailored to the user's input speed and style. For example, the text interface analyzes the user's input speed and provides auto-completion at an appropriate time. For example, if the input is slow, completion candidates are displayed earlier. The text interface also learns the user's input style and provides an individually customized auto-completion function. For example, phrases and words frequently used by the user are preferentially displayed as completion candidates. The text interface also provides a dynamic auto-completion function that is tailored to the user's input speed and style. For example, completion candidates are updated in real time according to the flow of input. This makes it possible to improve input efficiency by providing an auto-completion function that is tailored to the user's input speed and style.

[0063] The emotion estimation function can generate a response according to the tone of the user's voice or text. The emotion estimation function generates a response according to the user's voice tone. For example, if the user speaks in a calm tone, the response is similarly calm. The emotion estimation function also analyzes the tone of the user's text and generates an appropriate response using the emotion estimation function. For example, if the user is excited, a calm response is provided. The emotion estimation function also dynamically adjusts the response according to the tone of the user's voice or text. For example, if the user is angry, a calming response is provided. This allows for more natural communication by generating a response according to the tone of the user's voice or text.

[0064] The voice / text interface can be integrated with video calls and chatbots to integrate multiple communication methods. For example, the voice / text interface can be integrated with video calls to enable users to communicate via voice or text even during a video call. For example, it can enable users to send text messages during a video call. The voice / text interface can also be integrated with chatbots to enable users to communicate via voice or text with the chatbot. For example, the chatbot can respond in text to questions asked via voice. The voice / text interface can also be integrated with video calls and chatbots to enable users to seamlessly switch communication methods. For example, it can provide a function to switch from voice to text during a video call. In this way, integration with video calls and chatbots can integrate multiple communication methods and improve user convenience.

[0065] A voice / text interface can be integrated with games and entertainment apps to improve the user experience. For example, a voice / text interface can be integrated with a game to allow users to communicate with in-game characters by voice or text. For example, a voice command can be executed by a character in the game. A voice / text interface can also be integrated with an entertainment app to allow users to control in-app content by voice or text. For example, a voice command can be used to play or stop a video. A voice / text interface can also be integrated with games and entertainment apps to allow users to seamlessly control the app by voice or text. For example, a text chat function can be provided during a game. In this way, integration with games and entertainment apps can improve the user experience.

[0066] The emotion estimation function provides emotional feedback according to the emotion in the user's voice or text, thereby increasing user engagement. The emotion estimation function, for example, provides emotional feedback according to the emotion in the user's voice. For example, if the user is happy, it returns words of empathy. It also analyzes the emotion in the user's text and uses the emotion estimation function to provide appropriate emotional feedback. For example, if the user is sad, it sends an encouraging message. It also uses the emotion estimation function to dynamically adjust feedback according to the emotion in the user's voice or text. For example, if the user is angry, it provides calm, corresponding feedback. In this way, it is possible to increase user engagement by providing emotional feedback according to the emotion in the user's voice or text.

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

[0068] Personal assistant systems can also monitor the user's health and provide appropriate advice. For example, they can analyze the user's heart rate and sleep patterns to assess their health. They can also suggest healthy lifestyle habits based on the user's diet and exercise records. Furthermore, if the user feels stressed, they can provide advice on relaxation methods and stress management. This provides comprehensive support for the user's health.

[0069] Personal assistant systems can also assist users in managing their schedules. For example, they can analyze the user's calendar and remind them of important appointments and tasks. They can also suggest optimal time management methods based on the user's schedule. Furthermore, when the user is busy, they can provide advice on efficient task management. This can improve the efficiency of the user's schedule management and reduce stress.

[0070] Personal assistant systems can also recommend content based on a user's hobbies and interests. For example, they can analyze a user's favorite movies and music and suggest new related content. They can also recommend content that may be of interest to the user based on the user's past viewing history. Furthermore, if a user is interested in a particular genre, they can provide events and news related to that genre. This allows for a personalized experience tailored to the user's hobbies and interests.

[0071] Personal assistant systems can also assist users in planning their trips. For example, they can analyze the user's travel destinations and itinerary and suggest optimal travel plans. They can also recommend tourist spots and restaurants based on the user's preferences. Furthermore, they can provide users with real-time information they need while traveling. This can improve the user's travel experience.

[0072] Personal assistant systems can also monitor a user's learning progress and provide appropriate learning resources. For example, they can analyze the topic the user is studying and recommend related learning materials and reference materials. They can also provide a customized learning plan based on the user's learning style. Furthermore, if the user is struggling to understand something, they can provide additional support and advice. This can improve the user's learning efficiency.

[0073] The personal assistant system can analyze the user's emotional state and suggest appropriate relaxation methods. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. It can also recommend relaxing music or videos based on the user's emotional state. It can also provide advice on creating a relaxing environment for the user. This can improve the user's emotional state and support their mental health.

[0074] A personal assistant system can analyze a user's emotional state and suggest appropriate communication methods. For example, if the user is angry, it can provide advice on how to stay calm. If the user is sad, it can provide words of encouragement and support. Furthermore, it can suggest appropriate conversation methods depending on the user's emotional state. This allows it to support communication that is appropriate for the user's emotions and improve relationships.

[0075] The personal assistant system can analyze the user's emotional state and provide appropriate feedback. For example, if the user feels a sense of accomplishment, it can provide feedback to reinforce that feeling. If the user is feeling down, it can provide words of encouragement and support. Furthermore, it can provide specific advice to promote self-improvement according to the user's emotional state. In this way, it is possible to provide feedback according to the user's emotions and support self-improvement.

[0076] A personal assistant system can analyze a user's emotional state and provide appropriate entertainment content. For example, if a user wants to relax, it can recommend relaxing movies or music. If a user wants to cheer up, it can provide energetic content. Furthermore, it can provide an appropriate entertainment experience depending on the user's emotional state. This allows it to provide entertainment content that matches the user's emotions and provide emotional support.

[0077] The personal assistant system can analyze the user's emotional state and provide appropriate mental health support. For example, if the user feels anxious, it can provide advice and support to help them relax. If the user feels stressed, it can suggest ways to manage stress. Furthermore, it can provide appropriate counseling and support depending on the user's emotional state. This can improve the user's emotional state and support their mental health.

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

[0079] Step 1: The analyzer analyzes the natural language input from the user and understands its content. For example, if the user says, "I'm tired today," the meaning of the words is analyzed and an appropriate response is generated. The analyzer uses natural language processing technology to analyze the user's input and understand its content. Step 2: The response generation unit generates a response based on the content analyzed by the analysis unit. For example, the response generation unit may be equipped with an algorithm that generates an appropriate response to the user's input, and provide appropriate feedback to the user based on the response generated by the generation AI. Step 3: The emotion engine analyzes the user's emotion and provides the result to the response generator. For example, the emotion engine analyzes the user's tone of voice and facial expression to infer emotion. The emotion engine uses an emotion analysis algorithm to analyze the user's emotion.

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

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

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

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

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

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

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

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

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

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

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

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

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

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

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

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

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

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

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

Claims

1. Equipped with generative AI and emotion engine, The generated AI is an analysis unit that analyzes natural language input from a user and understands the content thereof; a response generation unit that generates a response based on the content analyzed by the analysis unit; The emotion engine Analyzes user emotions and provides the results to the response generator A system characterized by:

2. The analysis unit Learns from a user's past conversation history and generates responses based on the user's individual patterns and preferences 2. The system of claim 1.

3. The emotion engine Analyzing the user's facial expressions and gestures to estimate emotions more accurately 2. The system of claim 1.

4. The emotion estimation function is Automatically recommends music and videos based on the user's emotions, providing emotional support 2. The system of claim 1.

5. A personal assistant that combines generative AI and an emotion engine Analyze students' learning progress in real time and provide appropriate feedback 2. The system of claim 1.

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