System

Abstract

An object of the system according to the embodiment is to provide a real-time interactive conversation experience with a specific person and to distribute profits to a sound data provider.SOLUTION: A system includes an interface unit, a generation unit, and a profit sharing unit. The interface unit receives the user's selection. The generation unit generates a dialogue on the basis of the user's selection received by the interface unit. The profit distribution unit distributes a profit to the voice data provider based on the conversation generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the drawbacks of making it difficult to provide a real-time interactive conversation experience with a specific person, and of not providing sufficient revenue distribution to voice data providers.

[0005] The system according to the embodiment aims to provide a real-time interactive conversation experience with a specific person and distribute revenue to the voice data provider. [Means for solving the problem]

[0006] The system according to the embodiment includes an interface unit, a generation unit, and a revenue distribution unit. The interface unit accepts a user's selection. The generation unit generates a dialogue based on the user's selection accepted by the interface unit. The revenue distribution unit distributes revenue to the voice data provider based on the dialogue generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment provides a real-time interactive conversation experience with a specific person and can distribute revenue to the voice data provider. [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) A conversation platform according to an embodiment of the present invention is a system that uses AI technology to allow users to enjoy conversations with specific people, such as celebrities or deceased people, while recreating the unique style and personality of those people. This allows the conversation platform to enable users to enjoy interactive conversations in real time, while also generating profits for voice data providers.

[0029] A conversation platform according to an embodiment includes an interface unit, a generation unit, and a revenue distribution unit. The interface unit accepts a user's selection. For example, a user simply opens the app, selects a person of their choice from a list, and presses a button to start a conversation, which initiates a dialogue with the generation AI. The generation unit generates a dialogue based on the user's selection accepted by the interface unit. For example, the generation AI generates a dialogue based on a specific person's voice data and prompts for reproducing that person's characteristics. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to reproduce the specific person's style and personality. The revenue distribution unit distributes revenue to a voice data provider based on the dialogue generated by the generation unit. For example, the more data provided by the voice data provider is used, the more revenue the provider receives. This allows users to select their favorite person and enjoy interactive conversations in real time, while also providing a return on profits to the voice data provider.

[0030] The generation unit can reproduce the non-verbal communication of a specific person and provide dialogue not only in audio but also in visuals. For example, the generation unit analyzes past video data and learns gesture and facial expression patterns so that the generation AI can reproduce the non-verbal communication of a specific person. For example, it reproduces gestures and facial expressions according to specific emotions and situations. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to reproduce the non-verbal communication of a specific person and provide dialogue not only in audio but also in visuals. This reproduction of non-verbal communication provides a more realistic dialogue experience.

[0031] The generation unit not only reproduces the style and personality of a specific person, but also predicts that person's future actions and statements and generates virtual scenarios. For example, the generation unit uses a generation AI to learn the past actions and statements of a specific person and predict future actions and statements based on that data. For example, the generation unit generates predicted reactions in specific situations as scenarios. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to not only reproduce the style and personality of a specific person, but also predict that person's future actions and statements and generate virtual scenarios. This provides a more diverse dialogue experience by predicting a specific person's future actions and statements and generating virtual scenarios.

[0032] The generation unit can translate the style of a specific person into other languages, making it possible to accommodate users from different cultural backgrounds. For example, the generation unit trains speech data with a multilingual dataset so that the generation AI can translate the style of a specific person into other languages. For example, English speech data is translated into Japanese or French. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to translate the style of a specific person into other languages, making it possible to accommodate users from different cultural backgrounds. This makes it possible to accommodate users from different cultural backgrounds by translating the style of a specific person into other languages.

[0033] The generation unit can learn the user's past conversation history and provide dialogue based on the user's preferences and interests. For example, the generation unit uses a generation AI to learn the user's past conversation history and identify the user's preferences and interests based on that data. For example, it analyzes the content of past conversations and extracts topics that interest the user. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to learn the user's past conversation history and provide dialogue based on the user's preferences and interests. This provides a more personalized experience by learning the user's past conversation history and providing dialogue based on the user's preferences and interests.

[0034] The generation unit can realize a more natural conversation by analyzing the user's voice tone and speed and generating a response accordingly. For example, the generation unit uses a generation AI to analyze the user's voice tone and speed in real time and generate a response based on that data. For example, if the user's voice tone is high, an excited response is generated. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's voice tone and speed and generate a response accordingly, thereby realizing a more natural conversation. In this way, a more natural conversation can be realized by generating a response according to the user's voice tone and speed.

[0035] The generation unit can use the user's real-time location information to provide topics related to that location. For example, the generation unit uses a generation AI to obtain the user's real-time location information and provide topics related to that location. For example, if the user is in a tourist spot, information about that tourist spot is provided. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to use the user's real-time location information and provide topics related to that location. This provides a more personalized experience by using the user's real-time location information and providing topics related to that location.

[0036] The generation unit enables a conversation to be continued seamlessly between the user's devices, thereby providing an interactive experience across different devices. For example, the generation unit builds a cloud-based data synchronization system to enable the generation AI to continue a conversation seamlessly between the user's devices. For example, the conversation is not interrupted even when switching from a smartphone to a tablet. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to enable a conversation to be continued seamlessly between the user's devices, thereby providing an interactive experience across different devices. This enables a conversation to be continued seamlessly between the user's devices, thereby providing an interactive experience across different devices.

[0037] The revenue distribution unit can distribute revenue based on user satisfaction as well as the frequency of use of data provided by the audio data provider. The revenue distribution unit, for example, analyzes the frequency of use of data provided by the audio data provider and distributes revenue based on that data. For example, it distributes higher revenue to data providers who use data more frequently. The revenue distribution unit also distributes revenue based on user satisfaction. For example, it distributes revenue based on user feedback and ratings. This allows for more equitable revenue distribution by distributing revenue based on user satisfaction as well as the frequency of use of data provided by the audio data provider.

[0038] The revenue distribution unit can evaluate the quality of data provided by the voice data provider and provide additional rewards for high-quality data. For example, to evaluate the quality of data provided by the voice data provider, the revenue distribution unit uses voice recognition technology to analyze the clarity of the data and the presence or absence of noise. For example, additional rewards are provided for clear voice data. The revenue distribution unit also evaluates the quality of the data based on user feedback and ratings. For example, additional rewards are provided for data that users rate highly. In this way, the quality of data provided by the voice data provider is evaluated and additional rewards are provided for high-quality data, thereby improving the quality of the data.

[0039] The revenue distribution unit can use the data provided by the voice data provider in other AI projects, thereby securing multiple revenue streams. The revenue distribution unit, for example, builds a data sharing platform to use the data provided by the voice data provider in other AI projects. For example, it provides data for speech recognition and translation projects. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to use the data provided by the voice data provider in other AI projects, thereby securing multiple revenue streams. In this way, the data provided by the voice data provider can be used in other AI projects, thereby securing multiple revenue streams.

[0040] The revenue sharing unit can anonymize the data provided by the voice data provider and expand the scope of data use while protecting privacy. For example, the revenue sharing unit develops an algorithm to remove personally identifiable information in order to anonymize the data provided by the voice data provider. For example, it removes personal information such as names and addresses. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to anonymize the data provided by the voice data provider and expand the scope of data use while protecting privacy. In this way, by anonymizing the data provided by the voice data provider, the scope of data use is expanded while protecting privacy.

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

[0042] The generator not only reproduces the style and personality of a specific person, but can also generate virtual interview-style dialogues based on that person's past actions and statements. For example, a user can ask how a specific person felt about a specific historical event, and the generator can generate the answer. It can also virtually interview a specific person about their thoughts on future technology and society. Furthermore, it can generate scenarios in which a specific person interacts with other celebrities, allowing users to observe the interaction. This allows users to virtually experience the specific person's deep insights and opinions.

[0043] The generator can reproduce a specific person's non-verbal communication and provide dialogue not only through audio but also through visuals. For example, it can reproduce gestures and facial expressions that correspond to specific emotions and situations. If a specific person is surprised, it can reproduce a gesture of widening the eyes. If a specific person is deep in thought, it can reproduce a gesture of putting a hand to the chin. Furthermore, if a specific person is smiling, it can reproduce an expression of raising the corners of the mouth. By reproducing non-verbal communication in this way, it can provide a more realistic dialogue experience.

[0044] The generation unit not only reproduces the style and personality of a specific person, but also predicts that person's future actions and statements and generates virtual scenarios. For example, it can predict what a specific person will think about future technology. It can also predict how a specific person will speak about future social issues. It can also generate scenarios for when a specific person participates in a future event. This allows the system to predict a specific person's future actions and statements and generate virtual scenarios, providing a more diverse dialogue experience.

[0045] The generation unit can translate a specific person's style into other languages ​​to accommodate users from different cultural backgrounds. For example, English speech data can be translated into Japanese or French. The humor and expressions of a specific person can be adapted to other languages. Furthermore, by performing translations that take into account specific cultural backgrounds, natural dialogue can be provided to users from different cultural backgrounds. Furthermore, by translating a specific person's style into other languages, it is possible to accommodate users from different cultural backgrounds.

[0046] The generation unit can learn the user's past conversation history and provide dialogue based on the user's preferences and interests. For example, it can analyze past conversation content and extract topics that interest the user. It can generate new dialogue based on several topics that the user has talked about in the past. It can also take into account the user's interest in a specific person and provide new information related to that person. Furthermore, by learning the user's past conversation history and providing dialogue based on the user's preferences and interests, it can provide a more personalized experience.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The interface accepts the user's selection. For example, the user simply opens the app, selects a person from a list, and presses the "Start conversation" button to begin a dialogue with the generating AI. Step 2: The generation unit generates a dialogue based on the user's selections accepted by the interface unit. For example, the generation AI generates a dialogue based on a specific person's voice data and prompts to reproduce that person's characteristics. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to reproduce the specific person's style and personality. Step 3: The revenue distribution unit distributes revenue to the voice data provider based on the dialogue generated by the generation unit. For example, the more the data provided by the voice data provider is used, the more revenue is distributed to the provider.

[0049] (Example 2) A conversation platform according to an embodiment of the present invention is a system that uses AI technology to allow users to enjoy conversations with specific people, such as celebrities or deceased people, while recreating the unique style and personality of those people. This allows the conversation platform to enable users to enjoy interactive conversations in real time, while also generating profits for voice data providers.

[0050] A conversation platform according to an embodiment includes an interface unit, a generation unit, and a revenue distribution unit. The interface unit accepts a user's selection. For example, a user simply opens the app, selects a person of their choice from a list, and presses a button to start a conversation, which initiates a dialogue with the generation AI. The generation unit generates a dialogue based on the user's selection accepted by the interface unit. For example, the generation AI generates a dialogue based on a specific person's voice data and prompts for reproducing that person's characteristics. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to reproduce the specific person's style and personality. The revenue distribution unit distributes revenue to a voice data provider based on the dialogue generated by the generation unit. For example, the more data provided by the voice data provider is used, the more revenue the provider receives. This allows users to select their favorite person and enjoy interactive conversations in real time, while also providing a return on profits to the voice data provider.

[0051] The generation unit can learn the emotional changes of a specific person in real time and reflect those emotions during a conversation. For example, the generation unit analyzes past voice data and extracts patterns of emotional changes so that the generation AI can learn the emotional changes of a specific person in real time. For example, it learns changes in voice tone and speed in specific situations and reflects those emotions during a conversation. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to learn the emotional changes of a specific person in real time and reflect those emotions during a conversation. This allows the emotional changes of a specific person to be reflected in real time, resulting in more natural dialogue.

[0052] The generation unit can reproduce the non-verbal communication of a specific person and provide dialogue not only in audio but also in visuals. For example, the generation unit analyzes past video data and learns gesture and facial expression patterns so that the generation AI can reproduce the non-verbal communication of a specific person. For example, it reproduces gestures and facial expressions according to specific emotions and situations. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to reproduce the non-verbal communication of a specific person and provide dialogue not only in audio but also in visuals. This reproduction of non-verbal communication provides a more realistic dialogue experience.

[0053] The generation unit can adjust the response of a specific person according to the user's emotions, thereby realizing a more personalized dialogue. The generation unit, for example, uses an emotion estimation function to analyze the user's emotions in real time and adjust the response of a specific person according to those emotions. For example, if the user is sad, it generates a comforting response. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to adjust the response of a specific person according to the user's emotions, thereby realizing a more personalized dialogue. This allows for a more personalized dialogue by providing responses according to the user's emotions.

[0054] The generation unit not only reproduces the style and personality of a specific person, but also predicts that person's future actions and statements and generates virtual scenarios. For example, the generation unit uses a generation AI to learn the past actions and statements of a specific person and predict future actions and statements based on that data. For example, the generation unit generates predicted reactions in specific situations as scenarios. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to not only reproduce the style and personality of a specific person, but also predict that person's future actions and statements and generate virtual scenarios. This provides a more diverse dialogue experience by predicting a specific person's future actions and statements and generating virtual scenarios.

[0055] The generation unit can translate the style of a specific person into other languages, making it possible to accommodate users from different cultural backgrounds. For example, the generation unit trains speech data with a multilingual dataset so that the generation AI can translate the style of a specific person into other languages. For example, English speech data is translated into Japanese or French. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to translate the style of a specific person into other languages, making it possible to accommodate users from different cultural backgrounds. This makes it possible to accommodate users from different cultural backgrounds by translating the style of a specific person into other languages.

[0056] The generation unit can analyze the emotions of a specific person in real time and generate a new dialogue scenario based on that emotion. The generation unit, for example, uses an emotion estimation function to analyze the emotions of a specific person in real time and generate a dialogue scenario based on that emotion. For example, it provides an appropriate scenario according to a specific emotional state. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the emotions of a specific person in real time and generate a new dialogue scenario based on that emotion. In this way, by analyzing the emotions of a specific person in real time and generating a new dialogue scenario based on that emotion, a more diverse dialogue experience is provided.

[0057] The generation unit can learn the user's past conversation history and provide dialogue based on the user's preferences and interests. For example, the generation unit uses a generation AI to learn the user's past conversation history and identify the user's preferences and interests based on that data. For example, it analyzes the content of past conversations and extracts topics that interest the user. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to learn the user's past conversation history and provide dialogue based on the user's preferences and interests. This provides a more personalized experience by learning the user's past conversation history and providing dialogue based on the user's preferences and interests.

[0058] The generation unit can realize a more natural conversation by analyzing the user's voice tone and speed and generating a response accordingly. For example, the generation unit uses a generation AI to analyze the user's voice tone and speed in real time and generate a response based on that data. For example, if the user's voice tone is high, an excited response is generated. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the user's voice tone and speed and generate a response accordingly, thereby realizing a more natural conversation. In this way, a more natural conversation can be realized by generating a response according to the user's voice tone and speed.

[0059] The generation unit can use the emotion estimation function to adjust the dialogue content in real time according to the user's emotional state, thereby improving user satisfaction. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and adjust the dialogue content based on that data. For example, if the user is sad, the generation unit provides comforting dialogue content. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to adjust the dialogue content in real time according to the user's emotional state, thereby improving user satisfaction. In this way, adjusting the dialogue content in real time according to the user's emotional state improves user satisfaction.

[0060] The generation unit can use the user's real-time location information to provide topics related to that location. For example, the generation unit uses a generation AI to obtain the user's real-time location information and provide topics related to that location. For example, if the user is in a tourist spot, information about that tourist spot is provided. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to use the user's real-time location information and provide topics related to that location. This provides a more personalized experience by using the user's real-time location information and providing topics related to that location.

[0061] The generation unit enables a conversation to be continued seamlessly between the user's devices, thereby providing an interactive experience across different devices. For example, the generation unit builds a cloud-based data synchronization system to enable the generation AI to continue a conversation seamlessly between the user's devices. For example, the conversation is not interrupted even when switching from a smartphone to a tablet. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to enable a conversation to be continued seamlessly between the user's devices, thereby providing an interactive experience across different devices. This enables a conversation to be continued seamlessly between the user's devices, thereby providing an interactive experience across different devices.

[0062] The generation unit can use the emotion estimation function to suggest music and videos based on the user's emotions to complement the conversation experience. For example, the generation unit uses the emotion estimation function to analyze the user's emotions in real time and suggest music based on those emotions. For example, if the user is relaxed, it suggests relaxing music. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to suggest music and videos based on the user's emotions to complement the conversation experience. In this way, the conversation experience is complemented by suggesting music and videos based on the user's emotions.

[0063] The revenue distribution unit can distribute revenue based on user satisfaction as well as the frequency of use of data provided by the audio data provider. The revenue distribution unit, for example, analyzes the frequency of use of data provided by the audio data provider and distributes revenue based on that data. For example, it distributes higher revenue to data providers who use data more frequently. The revenue distribution unit also distributes revenue based on user satisfaction. For example, it distributes revenue based on user feedback and ratings. This allows for more equitable revenue distribution by distributing revenue based on user satisfaction as well as the frequency of use of data provided by the audio data provider.

[0064] The revenue distribution unit can evaluate the quality of data provided by the voice data provider and provide additional rewards for high-quality data. For example, to evaluate the quality of data provided by the voice data provider, the revenue distribution unit uses voice recognition technology to analyze the clarity of the data and the presence or absence of noise. For example, additional rewards are provided for clear voice data. The revenue distribution unit also evaluates the quality of the data based on user feedback and ratings. For example, additional rewards are provided for data that users rate highly. In this way, the quality of data provided by the voice data provider is evaluated and additional rewards are provided for high-quality data, thereby improving the quality of the data.

[0065] The revenue distribution unit can use the emotion estimation function to provide feedback to the voice data provider based on the user's emotional response, thereby promoting data improvement. The revenue distribution unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time and provide feedback to the voice data provider based on that data. For example, if the user expresses positive emotion, the revenue distribution unit provides feedback that the data is highly rated. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to provide feedback to the voice data provider based on the user's emotional response, promoting data improvement. This provides feedback to the voice data provider based on the user's emotional response and promotes data improvement, thereby providing higher quality data.

[0066] The revenue distribution unit can use the data provided by the voice data provider in other AI projects, thereby securing multiple revenue streams. The revenue distribution unit, for example, builds a data sharing platform to use the data provided by the voice data provider in other AI projects. For example, it provides data for speech recognition and translation projects. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to use the data provided by the voice data provider in other AI projects, thereby securing multiple revenue streams. In this way, the data provided by the voice data provider can be used in other AI projects, thereby securing multiple revenue streams.

[0067] The revenue sharing unit can anonymize the data provided by the voice data provider and expand the scope of data use while protecting privacy. For example, the revenue sharing unit develops an algorithm to remove personally identifiable information in order to anonymize the data provided by the voice data provider. For example, it removes personal information such as names and addresses. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to anonymize the data provided by the voice data provider and expand the scope of data use while protecting privacy. In this way, by anonymizing the data provided by the voice data provider, the scope of data use is expanded while protecting privacy.

[0068] The revenue distribution unit can use the emotion estimation function to evaluate the emotional value of the data provided by the voice data provider and distribute revenue based on that value. The revenue distribution unit, for example, uses the emotion estimation function to analyze the user's emotional response to evaluate the emotional value of the data provided by the voice data provider. For example, it assigns a high rating to data that evokes positive emotions. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to evaluate the emotional value of the data provided by the voice data provider and distribute revenue based on that value. In this way, by evaluating the emotional value of the data provided by the voice data provider and distributing revenue based on that value, data with higher emotional value can be provided.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The generator not only reproduces the style and personality of a specific person, but can also generate virtual interview-style dialogues based on that person's past actions and statements. For example, a user can ask how a specific person felt about a specific historical event, and the generator can generate the answer. It can also virtually interview a specific person about their thoughts on future technology and society. Furthermore, it can generate scenarios in which a specific person interacts with other celebrities, allowing users to observe the interaction. This allows users to virtually experience the specific person's deep insights and opinions.

[0071] The generation unit can learn the emotional changes of a specific person in real time and reflect those emotions during conversation. For example, if a specific person is happy, the tone of the voice becomes brighter and the speech rate increases. If a specific person is angry, the tone of the voice becomes lower and the speech rate decreases. Furthermore, if a specific person is sad, the tone of the voice becomes calmer and the speech rate decreases. This allows the emotional changes of a specific person to be reflected in real time, resulting in more natural dialogue.

[0072] The generator can reproduce a specific person's non-verbal communication and provide dialogue not only through audio but also through visuals. For example, it can reproduce gestures and facial expressions that correspond to specific emotions and situations. If a specific person is surprised, it can reproduce a gesture of widening the eyes. If a specific person is deep in thought, it can reproduce a gesture of putting a hand to the chin. Furthermore, if a specific person is smiling, it can reproduce an expression of raising the corners of the mouth. By reproducing non-verbal communication in this way, it can provide a more realistic dialogue experience.

[0073] The generation unit can adjust the response of a specific person according to the user's emotions, thereby realizing a more personalized dialogue. For example, if the user is sad, a comforting response is generated. If the user is excited, a sympathetic response is generated. If the user is angry, a calming response is generated. In this way, a more personalized dialogue is realized by providing a response according to the user's emotions.

[0074] The generation unit not only reproduces the style and personality of a specific person, but also predicts that person's future actions and statements and generates virtual scenarios. For example, it can predict what a specific person will think about future technology. It can also predict how a specific person will speak about future social issues. It can also generate scenarios for when a specific person participates in a future event. This allows the system to predict a specific person's future actions and statements and generate virtual scenarios, providing a more diverse dialogue experience.

[0075] The generation unit can translate a specific person's style into other languages ​​to accommodate users from different cultural backgrounds. For example, English speech data can be translated into Japanese or French. The humor and expressions of a specific person can be adapted to other languages. Furthermore, by performing translations that take into account specific cultural backgrounds, natural dialogue can be provided to users from different cultural backgrounds. Furthermore, by translating a specific person's style into other languages, it is possible to accommodate users from different cultural backgrounds.

[0076] The generation unit can analyze the emotions of a specific person in real time and generate new dialogue scenarios based on those emotions. For example, it can provide an appropriate scenario according to a specific emotional state. If a specific person is happy, it can provide a fun topic. If a specific person is sad, it can provide a comforting scenario. If a specific person is angry, it can provide a calming scenario. In this way, by analyzing the emotions of a specific person in real time and generating new dialogue scenarios based on those emotions, it can provide a more diverse dialogue experience.

[0077] The generation unit can learn the user's past conversation history and provide dialogue based on the user's preferences and interests. For example, it can analyze past conversation content and extract topics that interest the user. It can generate new dialogue based on several topics that the user has talked about in the past. It can also take into account the user's interest in a specific person and provide new information related to that person. Furthermore, by learning the user's past conversation history and providing dialogue based on the user's preferences and interests, it can provide a more personalized experience.

[0078] The generation unit analyzes the user's voice tone and speed and generates a response accordingly, thereby realizing a more natural conversation. For example, if the user's voice tone is high, an excited response is generated. If the user's voice tone is low, a calm response is generated. If the user's speaking speed is fast, a response is generated at the same fast speed. Furthermore, if the user's speaking speed is slow, a slower response is generated. In this way, by generating a response according to the user's voice tone and speed, a more natural conversation is realized.

[0079] The generation unit can use the emotion estimation function to adjust the dialogue content in real time according to the user's emotional state, thereby improving user satisfaction. For example, if the user is sad, comforting dialogue content is provided. If the user is excited, empathetic dialogue content is provided. If the user is angry, calming dialogue content is provided. In this way, adjusting the dialogue content in real time according to the user's emotional state improves user satisfaction.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The interface accepts the user's selection. For example, the user simply opens the app, selects a person from a list, and presses the "Start conversation" button to begin a dialogue with the generating AI. Step 2: The generation unit generates a dialogue based on the user's selections accepted by the interface unit. For example, the generation AI generates a dialogue based on a specific person's voice data and prompts to reproduce that person's characteristics. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to reproduce the specific person's style and personality. Step 3: The revenue distribution unit distributes revenue to the voice data provider based on the dialogue generated by the generation unit. For example, the more the data provided by the voice data provider is used, the more revenue is distributed to the provider.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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, in order to avoid confusion and to 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.

[0148] 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]

[0149] 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. an interface unit that accepts a user selection; a generation unit that generates a dialogue based on the user's selection accepted by the interface unit; a revenue distribution unit that distributes revenue to a voice data provider based on the dialogue generated by the generation unit. system.

2. The generation unit Reproduce the non-verbal communication of a specific person and provide the dialogue not only in audio but also visually. The system of claim 1 .

3. The generation unit Not only does it recreate the style and personality of a specific person, but it also predicts the future actions and statements of that person and generates hypothetical scenarios. The system of claim 1 .

4. The generation unit Learns the user's past conversation history and provides the dialogue based on the user's preferences and interests The system of claim 1 .

5. The revenue distribution unit: The revenue is distributed based on the user's satisfaction as well as the frequency of use of the data provided by the voice data provider. The system of claim 1 .

6. The generation unit Learns the emotional changes of specific people in real time and reflects those emotions during conversations The system of claim 1 .

7. The generation unit To improve satisfaction of the user by adjusting the dialogue content in real time according to the emotional state of the user. The system of claim 1 .

8. The revenue distribution unit: Providing feedback to the voice data provider based on the user's emotional response to facilitate data improvement The system of claim 1 .

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