System

Abstract

An object of the system according to the embodiment is to generate AI content based on thinking and conversation of a specific person.SOLUTION: A system according to an embodiment includes a learning unit, a generation unit, a sound generation unit, and an automatic video generation unit. The learning unit learns thinking and conversation of a specific individual. The generation unit generates the generated AI based on the content learned by the learning unit. The voice generation unit converts the text data generated by the generation unit into voice. The automatic video generation unit generates a video based on the text data generated by the generation unit and the audio data generated by the audio 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 technology does not adequately generate AI content based on the thoughts and conversations of specific individuals, and there is room for improvement.

[0005] The system of the embodiment aims to generate AI content based on the thoughts and conversations of a specific individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a generation unit, an audio generation unit, and an automatic video generation unit. The learning unit learns the thoughts and conversations of a specific individual. The generation unit creates a generation AI based on the content learned by the learning unit. The audio generation unit converts text data generated by the generation unit into audio. The automatic video generation unit generates video based on the text data generated by the generation unit and the audio data generated by the audio generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate AI content based on the thoughts and conversations of a specific individual. [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 commodity AI system according to an embodiment of the present invention is a system that learns the thoughts and conversations of a specific individual, creates a generative AI that imitates that individual, and makes it available to anyone using voice and automatic video generation software. This allows the commodity AI system to widely share the knowledge and experience of a specific individual, making it possible to use it in a variety of fields.

[0029] A commodity AI system according to an embodiment includes a learning unit, a generation unit, a voice generation unit, and an automatic video generation unit. The learning unit learns the thoughts and conversations of a specific individual. For example, the learning unit collects data such as past statements, interviews, and books of the specific individual and performs learning based on the data. The generation unit creates a generative AI based on the content learned by the learning unit. For example, the generation unit generates answers to questions and instructions from a user based on the thoughts and conversations of the specific individual. The voice generation unit converts text data generated by the generation unit into voice. For example, the voice generation unit generates voice that imitates the voice of the specific individual. The automatic video generation unit generates video based on the text data generated by the generation unit and the voice data generated by the voice generation unit. For example, the automatic video generation unit generates video that imitates the appearance and facial expression of the specific individual. This enables the commodity AI system according to an embodiment to widely share the knowledge and experience of the specific individual and be utilized in a variety of fields.

[0030] The learning unit learns the non-verbal communication of a specific individual and can reproduce more natural conversations. For example, the learning unit collects past video data of a specific individual and analyzes gestures and facial expressions. For example, hand movements and facial expressions are taken in as data and learned. The learning unit also trains the generation AI based on non-verbal communication data so that it can reproduce natural gestures and facial expressions. For example, it generates appropriate gestures according to the content of speech. The learning unit also analyzes changes in gestures and facial expressions in real time and reflects these changes in the learning data. For example, it learns changes in facial expressions according to changes in emotions. This makes it possible to have natural conversations that take into account the non-verbal communication of a specific individual.

[0031] The learning unit can simulate the thought process of a specific individual and learn decision-making patterns under specific circumstances. For example, the learning unit collects past decision-making data of a specific individual and analyzes their thought process. For example, it imports the decision-making process in a specific business scenario as data. The learning unit then uses the thought process data to allow the generative AI to learn decision-making patterns under specific circumstances. For example, it trains the process of risk assessment and opportunity recognition. The learning unit also simulates the decision-making patterns of a specific individual and predicts decision-making in different scenarios. For example, it recreates the decision-making process when launching a new business. This makes it possible to learn decision-making patterns under specific circumstances.

[0032] The learning unit can simultaneously study the thoughts and conversations of other well-known business leaders to create a generative AI that integrates the knowledge of multiple leaders. For example, the learning unit collects speech data from other well-known business leaders and integrates it with data from specific individuals for learning. For example, speeches and interviews by different leaders are taken in as data. The learning unit also analyzes the thinking and conversation patterns of multiple leaders and reflects similarities and differences in the learning data. For example, it learns differences in leadership styles and decision-making processes. The learning unit also creates a generative AI with knowledge of multiple leaders based on the integrated data. For example, it can develop an AI that provides advice from the perspectives of different leaders. This makes it possible to create a generative AI that integrates the knowledge of multiple leaders.

[0033] When learning the thoughts and conversations of a specific individual, the learning unit can also incorporate data from different cultural and linguistic backgrounds to create a generative AI with a global perspective. For example, the learning unit collects data from different cultural and linguistic backgrounds and integrates it with data from a specific individual to learn from. For example, it could incorporate statements and interviews from overseas business leaders as data. The learning unit could also analyze cross-cultural communication patterns to train the generative AI to have a global perspective. For example, it could generate statements that take cultural differences into account. The learning unit could also learn statements in different languages ​​based on multilingual data. For example, it could develop an AI that generates statements in multiple languages, such as English and Chinese. This makes it possible to create a generative AI with a global perspective.

[0034] The generation unit can provide deep insights into specific themes or topics by analyzing the thought and conversation patterns of a specific individual in detail. For example, the generation unit can analyze past speech data of a specific individual in detail to extract speech patterns on specific themes or topics. For example, it can analyze speech related to business strategy or leadership. The generation unit can also create a generative AI that provides deep insights into specific themes based on the speech patterns. For example, it can generate detailed advice on business strategy. The generation unit can also classify speech data by theme and build a system that provides insights into each theme. For example, it can provide insights into marketing strategies or technological innovations. This makes it possible to provide deep insights into specific themes or topics.

[0035] The generation unit can add a function to compare a specific individual's past statements with current trends and make future predictions. The generation unit, for example, collects a specific individual's past statement data and current business trends and performs a comparative analysis. For example, it compares past statements with current market trends. The generation unit also adds to the generation AI a function to make future predictions based on past statements and current trends. For example, it predicts upcoming business opportunities and risks. The generation unit also builds a system that provides specific advice to users based on the results of future predictions. For example, it makes strategic proposals based on future market trends. This makes it possible to compare past statements with current trends and make future predictions.

[0036] The generation unit can analyze the business strategy and philosophy behind the statements of a specific individual and provide advice based on that. For example, the generation unit analyzes the statement data of a specific individual and extracts the business strategy and philosophy behind it. For example, it analyzes the intention and purpose of the statement. The generation unit also creates a generative AI that provides specific advice to the user based on the extracted business strategy and philosophy. For example, it provides advice regarding strategic decision-making. The generation unit also builds a system that generates appropriate advice in response to a user's question based on the business strategy and philosophy data. For example, it provides advice regarding a company's growth strategy. This makes it possible to provide advice based on the business strategy and philosophy behind the statement.

[0037] The generation unit applies generative AI to the field of education, making it possible to provide students with learning support based on the thoughts and conversations of specific individuals. For example, the generation unit applies generative AI to the field of education, building a system that provides students with learning support based on the thoughts and conversations of specific individuals. For example, providing educational content related to business strategy and leadership. The generation unit also generates answers to students' questions based on the thoughts and conversations of specific individuals. For example, providing specific advice for questions about business case studies. The generation unit also anticipates use in the field of education, and develops a system in which generative AI provides customized advice according to students' learning progress. For example, providing feedback according to the learning content. This makes it possible to apply generative AI to the field of education, making it possible to provide students with learning support based on the thoughts and conversations of specific individuals.

[0038] The voice generation unit can analyze the tone and rhythm of a specific individual's voice in detail and generate more natural and realistic voice. The voice generation unit, for example, collects past voice data of a specific individual and analyzes the tone and rhythm of the voice in detail. For example, it incorporates data on the intonation and pauses of speech. The voice generation unit also develops voice generation software that generates more natural and realistic voice based on the data on the tone and rhythm of the voice. For example, it reproduces an appropriate tone according to the content of speech. The voice generation unit also adds a function to the voice generation software that reflects changes in tone and rhythm of the voice in real time. For example, it adjusts the tone according to changes in emotions. This makes it possible to analyze the tone and rhythm of a specific individual's voice in detail and generate more natural and realistic voice.

[0039] The speech generation unit can learn speech patterns of a specific individual under specific circumstances and generate speech appropriate to the situation. For example, the speech generation unit collects past speech data of a specific individual and analyzes speech patterns under specific circumstances. For example, speech content appropriate to a situation such as a meeting or an interview is captured as data. The speech generation unit also develops speech generation software that generates speech appropriate to the situation based on the speech pattern data. For example, speech generated during a meeting may be distinguished from speech generated during an interview. The speech generation unit also adds a function to the speech generation software that reflects speech patterns under specific circumstances in real time. For example, an appropriate speech pattern may be generated depending on the content of a user's question. This makes it possible to learn speech patterns under specific circumstances and generate speech appropriate to the situation.

[0040] The voice generation unit can also learn the voices of other famous people, allowing the development of voice generation software that can be used to switch between multiple voices. The voice generation unit, for example, collects voice data of other famous people and integrates it with data of a specific individual for learning. For example, speeches and interviews of different leaders are taken in as data. The voice generation unit also develops voice generation software that can be used to switch between multiple voices. For example, the voice of a different leader is reproduced in response to a user's selection. The voice generation unit also adds a function to the voice generation software that can switch between multiple voices in real time. For example, different voices are generated in response to a user's instruction. This makes it possible to develop voice generation software that can be used to learn the voices of other famous people, allowing the development of voice generation software that can be used to switch between multiple voices.

[0041] The voice generation unit can make the voice generation software multilingual, enabling voice generation in different languages. The voice generation unit, for example, collects multilingual voice data and trains the voice generation software. For example, voice data in multiple languages ​​such as English, French, and Chinese is imported. The voice generation unit also develops voice generation software that enables voice generation in different languages. For example, voices in different languages ​​are generated in response to user selection. The voice generation unit also adds a multilingual function to the voice generation software, generating voices in different languages ​​in real time. For example, the language is switched in response to user instructions. This makes the voice generation software multilingual, enabling voice generation in different languages.

[0042] The automatic video generation unit can analyze the facial expressions and gestures of a specific individual in detail and generate more realistic images. The automatic video generation unit, for example, collects past video data of a specific individual and analyzes the facial expressions and gestures in detail. For example, it captures facial expressions and hand movements as data. The automatic video generation unit also develops automatic video generation software that generates more realistic images based on the facial expression and gesture data. For example, it reproduces appropriate facial expressions and gestures according to the content of speech. The automatic video generation unit also adds a function to the automatic video generation software that reflects changes in facial expressions and gestures in real time. For example, it adjusts facial expressions according to changes in emotions. This makes it possible to analyze the facial expressions and gestures of a specific individual in detail and generate more realistic images.

[0043] The automatic video generation unit can learn video patterns under specific situations based on past video data of a specific individual. The automatic video generation unit, for example, collects past video data of a specific individual and analyzes video patterns under specific situations. For example, it imports video data according to situations such as meetings and interviews. The automatic video generation unit also develops automatic video generation software that generates video under specific situations based on video pattern data. For example, it generates videos that are taken during meetings and interviews separately. The automatic video generation unit also adds a function to the automatic video generation software that reflects video patterns under specific situations in real time. For example, it generates appropriate video patterns according to the content of a user's question. This makes it possible to learn video patterns under specific situations.

[0044] The automatic video generation unit can also learn from videos of other famous people, thereby developing video generation software that can switch between videos of multiple people. The automatic video generation unit, for example, collects video data of other famous people and integrates it with data of a specific individual for learning. For example, speeches and interviews of different leaders are taken in as data. The automatic video generation unit also develops automatic video generation software that can switch between videos of multiple people. For example, it reproduces videos of different leaders in response to user selection. The automatic video generation unit also adds a function to the automatic video generation software that can switch between videos of multiple people in real time. For example, it generates different videos in response to user instructions. This makes it possible to develop video generation software that can learn from videos of other famous people and switch between videos of multiple people.

[0045] The automatic image generation unit can make the image generation software virtual reality (VR) compatible, allowing a user to experience an image of a specific individual in a VR environment. For example, the automatic image generation unit develops a system that makes the automatic image generation software virtual reality (VR) compatible and allows a user to experience an image of a specific individual in a VR environment. For example, the automatic image generation unit reproduces an image of a specific individual using a VR headset. The automatic image generation unit also adds a function to the VR-compatible image generation software that reflects the facial expressions and gestures of a specific individual in real time. For example, the automatic image generation unit adjusts the image according to the user's viewpoint. The automatic image generation unit also adds interactive elements to the image generation software to enhance the experience in the VR environment. For example, the automatic image generation unit provides a function that allows a user to interact with a specific individual. This makes it possible to make the image generation software virtual reality (VR) compatible and allow a user to experience an image of a specific individual in a VR environment.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Commodity AI systems can also be equipped with a health management unit that monitors the user's health status and provides health advice. For example, it monitors the user's heart rate and sleep patterns and analyzes their health status. The health management unit also provides appropriate exercise and dietary advice based on the user's health data. For example, it detects a user's lack of exercise and sends a notification encouraging them to exercise. The health management unit also analyzes changes in the user's health status in real time and recommends that the user visit a medical institution if necessary. This makes it possible to support the user's health management.

[0048] The commodity AI system can also include a recommendation unit that provides content based on the user's hobbies and interests. For example, it can analyze the user's past browsing history and purchase history to recommend movies and books that match their interests. The recommendation unit can also provide information about events and activities based on the user's hobbies. For example, if the user is interested in music, it can notify the user of concert information. The recommendation unit can also improve its recommendation algorithm based on user feedback to provide more accurate content. This makes it possible to provide personalized content based on the user's hobbies and interests.

[0049] The commodity AI system can further include an education support unit that monitors the user's learning progress and provides learning support. For example, it analyzes the user's learning history and grasps the learning progress. The education support unit also provides a customized learning plan according to the user's learning pace. For example, it recommends intensive study of areas where the user is weak. The education support unit also evaluates the user's learning results and provides feedback. For example, it suggests the next learning step based on the test results. This makes it possible to effectively support the user's learning.

[0050] The commodity AI system can further include a schedule management unit that manages the user's schedule and supports efficient time management. For example, it analyzes the user's plans and proposes an optimal schedule. The schedule management unit also sends reminders and notifications based on the user's schedule. For example, it sends notifications before important meetings or events. The schedule management unit also analyzes changes in the user's schedule in real time and dynamically adjusts the schedule. For example, it proposes a new schedule in response to changes in the schedule. This makes it possible to support efficient time management for the user.

[0051] The commodity AI system can further include a shopping support unit that analyzes a user's purchasing history and provides personalized shopping advice. For example, it can analyze a user's past purchase data and recommend products that match their interests. The shopping support unit can also provide special offers and discount information based on the user's purchasing patterns. For example, it can provide discount coupons for products that the user frequently purchases. The shopping support unit can also improve the recommendation algorithm based on user feedback and provide more accurate shopping advice. This can improve the user's purchasing experience.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The learning unit studies the thoughts and conversations of a specific individual. For example, the learning unit collects data such as past statements, interviews, and books of a specific individual and uses that data as a basis for learning. Step 2: The generator creates a generative AI based on the content learned by the learning unit. For example, the generator generates answers to questions or instructions from the user based on the thoughts and conversations of a specific individual. Step 3: The voice generator converts the text data generated by the generator into voice. For example, the voice generator generates voice that imitates the voice of a specific individual. Step 4: The automatic video generation unit generates a video based on the text data generated by the generation unit and the audio data generated by the audio generation unit. For example, the automatic video generation unit generates a video that imitates the appearance and facial expression of a specific individual.

[0054] (Example 2) The commodity AI system according to an embodiment of the present invention is a system that learns the thoughts and conversations of a specific individual, creates a generative AI that imitates that individual, and makes it available to anyone using voice and automatic video generation software. This allows the commodity AI system to widely share the knowledge and experience of a specific individual, making it possible to use it in a variety of fields.

[0055] A commodity AI system according to an embodiment includes a learning unit, a generation unit, a voice generation unit, and an automatic video generation unit. The learning unit learns the thoughts and conversations of a specific individual. For example, the learning unit collects data such as past statements, interviews, and books of the specific individual and performs learning based on the data. The generation unit creates a generative AI based on the content learned by the learning unit. For example, the generation unit generates answers to questions and instructions from a user based on the thoughts and conversations of the specific individual. The voice generation unit converts text data generated by the generation unit into voice. For example, the voice generation unit generates voice that imitates the voice of the specific individual. The automatic video generation unit generates video based on the text data generated by the generation unit and the voice data generated by the voice generation unit. For example, the automatic video generation unit generates video that imitates the appearance and facial expression of the specific individual. This enables the commodity AI system according to an embodiment to widely share the knowledge and experience of the specific individual and be utilized in a variety of fields.

[0056] The learning unit can estimate the emotional state of a specific individual, classify the content of their statements based on that emotion, and reflect changes in emotion in the learning process. The learning unit, for example, collects past speech data from a specific individual and performs emotion analysis. For example, it estimates emotions from the tone and content of the speech and classifies emotional states such as joy, anger, and sadness. The learning unit also classifies the speech content by emotion based on the emotion estimation results and reflects changes in emotion in the learning data. For example, it trains speech that expresses positive emotions separately from speech that expresses negative emotions. The learning unit also analyzes changes in the emotion of a specific individual in real time and incorporates those changes into the learning data. For example, it tracks changes in emotion during an interview and reflects those changes in the learning data. This enables learning that takes into account the emotional state of a specific individual.

[0057] The learning unit learns the non-verbal communication of a specific individual and can reproduce more natural conversations. For example, the learning unit collects past video data of a specific individual and analyzes gestures and facial expressions. For example, hand movements and facial expressions are taken in as data and learned. The learning unit also trains the generation AI based on non-verbal communication data so that it can reproduce natural gestures and facial expressions. For example, it generates appropriate gestures according to the content of speech. The learning unit also analyzes changes in gestures and facial expressions in real time and reflects these changes in the learning data. For example, it learns changes in facial expressions according to changes in emotions. This makes it possible to have natural conversations that take into account the non-verbal communication of a specific individual.

[0058] The learning unit can simulate the thought process of a specific individual and learn decision-making patterns under specific circumstances. For example, the learning unit collects past decision-making data of a specific individual and analyzes their thought process. For example, it imports the decision-making process in a specific business scenario as data. The learning unit then uses the thought process data to allow the generative AI to learn decision-making patterns under specific circumstances. For example, it trains the process of risk assessment and opportunity recognition. The learning unit also simulates the decision-making patterns of a specific individual and predicts decision-making in different scenarios. For example, it recreates the decision-making process when launching a new business. This makes it possible to learn decision-making patterns under specific circumstances.

[0059] The learning unit can simultaneously study the thoughts and conversations of other well-known business leaders to create a generative AI that integrates the knowledge of multiple leaders. For example, the learning unit collects speech data from other well-known business leaders and integrates it with data from specific individuals for learning. For example, speeches and interviews by different leaders are taken in as data. The learning unit also analyzes the thinking and conversation patterns of multiple leaders and reflects similarities and differences in the learning data. For example, it learns differences in leadership styles and decision-making processes. The learning unit also creates a generative AI with knowledge of multiple leaders based on the integrated data. For example, it can develop an AI that provides advice from the perspectives of different leaders. This makes it possible to create a generative AI that integrates the knowledge of multiple leaders.

[0060] When learning the thoughts and conversations of a specific individual, the learning unit can also incorporate data from different cultural and linguistic backgrounds to create a generative AI with a global perspective. For example, the learning unit collects data from different cultural and linguistic backgrounds and integrates it with data from a specific individual to learn from. For example, it could incorporate statements and interviews from overseas business leaders as data. The learning unit could also analyze cross-cultural communication patterns to train the generative AI to have a global perspective. For example, it could generate statements that take cultural differences into account. The learning unit could also learn statements in different languages ​​based on multilingual data. For example, it could develop an AI that generates statements in multiple languages, such as English and Chinese. This makes it possible to create a generative AI with a global perspective.

[0061] The learning unit uses the emotion estimation function to generate utterances based on the emotions of a specific individual in real time, and can provide a response that matches the user's emotions. The learning unit, for example, uses the emotion estimation function to develop a system that generates utterances based on the emotions of a specific individual in real time. For example, it generates utterances with appropriate emotions in response to a user's question. The learning unit also analyzes the user's emotions in real time and provides a response that matches those emotions. For example, if the user has positive emotions, it generates words of encouragement. The learning unit also builds a system that provides a customized response that matches the user's emotions based on the emotion estimation data. For example, it adjusts the content of the utterance in response to changes in the user's emotions. This makes it possible to generate utterances based on the emotions of a specific individual in real time, and provide a response that matches the user's emotions.

[0062] The generation unit can provide deep insights into specific themes or topics by analyzing the thought and conversation patterns of a specific individual in detail. For example, the generation unit can analyze past speech data of a specific individual in detail to extract speech patterns on specific themes or topics. For example, it can analyze speech related to business strategy or leadership. The generation unit can also create a generative AI that provides deep insights into specific themes based on the speech patterns. For example, it can generate detailed advice on business strategy. The generation unit can also classify speech data by theme and build a system that provides insights into each theme. For example, it can provide insights into marketing strategies or technological innovations. This makes it possible to provide deep insights into specific themes or topics.

[0063] The generation unit can add a function to compare a specific individual's past statements with current trends and make future predictions. The generation unit, for example, collects a specific individual's past statement data and current business trends and performs a comparative analysis. For example, it compares past statements with current market trends. The generation unit also adds to the generation AI a function to make future predictions based on past statements and current trends. For example, it predicts upcoming business opportunities and risks. The generation unit also builds a system that provides specific advice to users based on the results of future predictions. For example, it makes strategic proposals based on future market trends. This makes it possible to compare past statements with current trends and make future predictions.

[0064] The generation unit can analyze the business strategy and philosophy behind the statements of a specific individual and provide advice based on that. For example, the generation unit analyzes the statement data of a specific individual and extracts the business strategy and philosophy behind it. For example, it analyzes the intention and purpose of the statement. The generation unit also creates a generative AI that provides specific advice to the user based on the extracted business strategy and philosophy. For example, it provides advice regarding strategic decision-making. The generation unit also builds a system that generates appropriate advice in response to a user's question based on the business strategy and philosophy data. For example, it provides advice regarding a company's growth strategy. This makes it possible to provide advice based on the business strategy and philosophy behind the statement.

[0065] The generation unit applies generative AI to the field of education, making it possible to provide students with learning support based on the thoughts and conversations of specific individuals. For example, the generation unit applies generative AI to the field of education, building a system that provides students with learning support based on the thoughts and conversations of specific individuals. For example, providing educational content related to business strategy and leadership. The generation unit also generates answers to students' questions based on the thoughts and conversations of specific individuals. For example, providing specific advice for questions about business case studies. The generation unit also anticipates use in the field of education, and develops a system in which generative AI provides customized advice according to students' learning progress. For example, providing feedback according to the learning content. This makes it possible to apply generative AI to the field of education, making it possible to provide students with learning support based on the thoughts and conversations of specific individuals.

[0066] The generation unit can use the emotion estimation function to create a generation AI that provides customized advice according to the user's emotions. For example, the generation unit uses the emotion estimation function to create a generation AI that provides customized advice according to the user's emotions. For example, optimal advice is generated based on the user's emotion score. The generation unit also builds a system that analyzes the user's emotions in real time and provides customized advice based on the results. For example, words of encouragement are provided when positive emotions are strong. The generation unit also develops a system that dynamically adjusts advice according to the user's emotions based on the emotion estimation data. For example, the advice content is adjusted according to changes in the user's emotions. This makes it possible to use the emotion estimation function to provide customized advice according to the user's emotions.

[0067] The voice generation unit can analyze the tone and rhythm of a specific individual's voice in detail and generate more natural and realistic voice. The voice generation unit, for example, collects past voice data of a specific individual and analyzes the tone and rhythm of the voice in detail. For example, it incorporates data on the intonation and pauses of speech. The voice generation unit also develops voice generation software that generates more natural and realistic voice based on the data on the tone and rhythm of the voice. For example, it reproduces an appropriate tone according to the content of speech. The voice generation unit also adds a function to the voice generation software that reflects changes in tone and rhythm of the voice in real time. For example, it adjusts the tone according to changes in emotions. This makes it possible to analyze the tone and rhythm of a specific individual's voice in detail and generate more natural and realistic voice.

[0068] The voice generation unit can reproduce emotional changes in the voice of a specific individual and generate voice corresponding to the user's emotions. The voice generation unit, for example, analyzes past voice data of a specific individual to identify emotional changes. For example, it estimates emotional changes from changes in speech content and tone. The voice generation unit also develops voice generation software that generates voice corresponding to the user's emotions based on the data on emotional changes. For example, if the user has positive emotions, it generates voice with a bright tone. The voice generation unit also builds a system that analyzes the user's emotions in real time and generates voice corresponding to those emotions. For example, it adjusts the tone and rhythm of the voice according to changes in the user's emotions. This makes it possible to reproduce emotional changes in the voice of a specific individual and generate voice corresponding to the user's emotions.

[0069] The speech generation unit can learn speech patterns of a specific individual under specific circumstances and generate speech appropriate to the situation. For example, the speech generation unit collects past speech data of a specific individual and analyzes speech patterns under specific circumstances. For example, speech content appropriate to a situation such as a meeting or an interview is captured as data. The speech generation unit also develops speech generation software that generates speech appropriate to the situation based on the speech pattern data. For example, speech generated during a meeting may be distinguished from speech generated during an interview. The speech generation unit also adds a function to the speech generation software that reflects speech patterns under specific circumstances in real time. For example, an appropriate speech pattern may be generated depending on the content of a user's question. This makes it possible to learn speech patterns under specific circumstances and generate speech appropriate to the situation.

[0070] The voice generation unit can also learn the voices of other famous people, allowing the development of voice generation software that can be used to switch between multiple voices. The voice generation unit, for example, collects voice data of other famous people and integrates it with data of a specific individual for learning. For example, speeches and interviews of different leaders are taken in as data. The voice generation unit also develops voice generation software that can be used to switch between multiple voices. For example, the voice of a different leader is reproduced in response to a user's selection. The voice generation unit also adds a function to the voice generation software that can switch between multiple voices in real time. For example, different voices are generated in response to a user's instruction. This makes it possible to develop voice generation software that can be used to learn the voices of other famous people, allowing the development of voice generation software that can be used to switch between multiple voices.

[0071] The voice generation unit can make the voice generation software multilingual, enabling voice generation in different languages. The voice generation unit, for example, collects multilingual voice data and trains the voice generation software. For example, voice data in multiple languages ​​such as English, French, and Chinese is imported. The voice generation unit also develops voice generation software that enables voice generation in different languages. For example, voices in different languages ​​are generated in response to user selection. The voice generation unit also adds a multilingual function to the voice generation software, generating voices in different languages ​​in real time. For example, the language is switched in response to user instructions. This makes the voice generation software multilingual, enabling voice generation in different languages.

[0072] The voice generation unit can use the emotion estimation function to add a function for automatically adjusting the voice tone according to the user's emotion. For example, the voice generation unit uses the emotion estimation function to add a function for automatically adjusting the voice tone according to the user's emotion to the voice generation software. For example, if the user has a positive emotion, a bright tone of voice is generated. The voice generation unit also builds a system that analyzes the user's emotion in real time and generates a voice tone according to that emotion. For example, the voice tone is adjusted according to changes in the user's emotion. The voice generation unit also develops a function for dynamically adjusting the voice tone according to the user's emotion based on the emotion estimation data. For example, the voice tone is changed according to the user's emotion score. This makes it possible to automatically adjust the voice tone according to the user's emotion using the emotion estimation function.

[0073] The automatic video generation unit can analyze the facial expressions and gestures of a specific individual in detail and generate more realistic images. The automatic video generation unit, for example, collects past video data of a specific individual and analyzes the facial expressions and gestures in detail. For example, it captures facial expressions and hand movements as data. The automatic video generation unit also develops automatic video generation software that generates more realistic images based on the facial expression and gesture data. For example, it reproduces appropriate facial expressions and gestures according to the content of speech. The automatic video generation unit also adds a function to the automatic video generation software that reflects changes in facial expressions and gestures in real time. For example, it adjusts facial expressions according to changes in emotions. This makes it possible to analyze the facial expressions and gestures of a specific individual in detail and generate more realistic images.

[0074] The automatic video generation unit can learn video patterns under specific situations based on past video data of a specific individual. The automatic video generation unit, for example, collects past video data of a specific individual and analyzes video patterns under specific situations. For example, it imports video data according to situations such as meetings and interviews. The automatic video generation unit also develops automatic video generation software that generates video under specific situations based on video pattern data. For example, it generates videos that are taken during meetings and interviews separately. The automatic video generation unit also adds a function to the automatic video generation software that reflects video patterns under specific situations in real time. For example, it generates appropriate video patterns according to the content of a user's question. This makes it possible to learn video patterns under specific situations.

[0075] The automatic video generation unit can be added with a function to generate videos that reflect the emotional changes of a specific individual. The automatic video generation unit, for example, analyzes past video data of a specific individual to identify emotional changes. For example, it estimates emotional changes from changes in speech content and facial expressions. The automatic video generation unit also develops automatic video generation software that generates videos corresponding to emotions based on data on emotional changes. For example, if a user has positive emotions, it generates a video with a bright expression. The automatic video generation unit also builds a system that analyzes user emotions in real time and generates videos corresponding to those emotions. For example, it adjusts the facial expressions and gestures of the video according to changes in the user's emotions. This makes it possible to generate videos that reflect the emotional changes of a specific individual.

[0076] The automatic video generation unit can also learn from videos of other famous people, thereby developing video generation software that can switch between videos of multiple people. The automatic video generation unit, for example, collects video data of other famous people and integrates it with data of a specific individual for learning. For example, speeches and interviews of different leaders are taken in as data. The automatic video generation unit also develops automatic video generation software that can switch between videos of multiple people. For example, it reproduces videos of different leaders in response to user selection. The automatic video generation unit also adds a function to the automatic video generation software that can switch between videos of multiple people in real time. For example, it generates different videos in response to user instructions. This makes it possible to develop video generation software that can learn from videos of other famous people and switch between videos of multiple people.

[0077] The automatic image generation unit can make the image generation software virtual reality (VR) compatible, allowing a user to experience an image of a specific individual in a VR environment. For example, the automatic image generation unit develops a system that makes the automatic image generation software virtual reality (VR) compatible and allows a user to experience an image of a specific individual in a VR environment. For example, the automatic image generation unit reproduces an image of a specific individual using a VR headset. The automatic image generation unit also adds a function to the VR-compatible image generation software that reflects the facial expressions and gestures of a specific individual in real time. For example, the automatic image generation unit adjusts the image according to the user's viewpoint. The automatic image generation unit also adds interactive elements to the image generation software to enhance the experience in the VR environment. For example, the automatic image generation unit provides a function that allows a user to interact with a specific individual. This makes it possible to make the image generation software virtual reality (VR) compatible and allow a user to experience an image of a specific individual in a VR environment.

[0078] The automatic video generation unit can use the emotion estimation function to add a function for automatically adjusting video expression according to the user's emotion. For example, the automatic video generation unit adds a function for automatically adjusting video expression according to the user's emotion using the emotion estimation function to the automatic video generation software. For example, if the user has positive emotions, it generates a video with a bright expression. The automatic video generation unit also builds a system that analyzes the user's emotions in real time and generates video expression according to those emotions. For example, it adjusts the facial expressions and gestures of the video according to changes in the user's emotions. The automatic video generation unit also develops a function for dynamically adjusting video expression according to the user's emotion based on the emotion estimation data. For example, it changes the video expression according to the user's emotion score. This makes it possible to automatically adjust video expression according to the user's emotion using the emotion estimation function.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] Commodity AI systems can also be equipped with a health management unit that monitors the user's health status and provides health advice. For example, it monitors the user's heart rate and sleep patterns and analyzes their health status. The health management unit also provides appropriate exercise and dietary advice based on the user's health data. For example, it detects a user's lack of exercise and sends a notification encouraging them to exercise. The health management unit also analyzes changes in the user's health status in real time and recommends that the user visit a medical institution if necessary. This makes it possible to support the user's health management.

[0081] The commodity AI system can also include a recommendation unit that provides content based on the user's hobbies and interests. For example, it can analyze the user's past browsing history and purchase history to recommend movies and books that match their interests. The recommendation unit can also provide information about events and activities based on the user's hobbies. For example, if the user is interested in music, it can notify the user of concert information. The recommendation unit can also improve its recommendation algorithm based on user feedback to provide more accurate content. This makes it possible to provide personalized content based on the user's hobbies and interests.

[0082] The commodity AI system can further include an education support unit that monitors the user's learning progress and provides learning support. For example, it analyzes the user's learning history and grasps the learning progress. The education support unit also provides a customized learning plan according to the user's learning pace. For example, it recommends intensive study of areas where the user is weak. The education support unit also evaluates the user's learning results and provides feedback. For example, it suggests the next learning step based on the test results. This makes it possible to effectively support the user's learning.

[0083] The commodity AI system can further include a relaxation unit that estimates the user's emotions and provides relaxation content based on those emotions. For example, it can analyze the user's stress level and recommend relaxing music or videos. The relaxation unit can also provide meditation or breathing exercise guidance based on the user's emotional state. For example, if the user is tense, it can suggest breathing techniques to help them relax. The relaxation unit can also analyze the user's emotional changes in real time and automatically adjust relaxation content as needed. This makes it possible to provide relaxation based on the user's emotions.

[0084] The commodity AI system can further include a feedback unit that estimates the user's emotions and provides feedback based on the emotions. For example, it analyzes the user's emotional state and provides positive feedback. The feedback unit also generates encouraging or comforting words according to the user's emotions. For example, if the user is feeling down, it sends an encouraging message. The feedback unit also analyzes the user's emotional changes in real time and dynamically adjusts the feedback content. For example, it changes the tone and content of the feedback according to the user's emotional score. This makes it possible to provide feedback based on the user's emotions.

[0085] The commodity AI system can further include an entertainment unit that estimates the user's emotions and provides entertainment content based on the emotions. For example, it analyzes the user's emotional state and recommends appropriate movies or games. The entertainment unit also provides interactive content based on the user's emotions. For example, if the user wants to relax, it will suggest a relaxing game. The entertainment unit also analyzes the user's emotional changes in real time and dynamically adjusts the entertainment content. For example, it changes the difficulty and tone of the content depending on the user's emotional score. This makes it possible to provide entertainment based on the user's emotions.

[0086] Commodity AI systems can also be equipped with a communication unit that estimates the user's emotions and provides communication support based on those emotions. For example, it analyzes the user's emotional state and suggests appropriate communication methods. The communication unit also adjusts the tone and content of the dialogue according to the user's emotions. For example, if the user is nervous, it will engage in dialogue to relax the user. The communication unit also analyzes the user's emotional changes in real time and dynamically adjusts the content of the communication. For example, it changes the progress of the dialogue depending on the user's emotional score. This makes it possible to provide communication support based on the user's emotions.

[0087] The commodity AI system can further include a news section that estimates a user's emotions and provides personalized news based on the user's emotions. For example, it analyzes the user's emotional state and recommends news that piques their interest. The news section also adjusts the tone and content of the news according to the user's emotions. For example, if the user has positive emotions, it provides upbeat news. The news section also analyzes changes in the user's emotions in real time and dynamically adjusts the news content. For example, it changes the news category or topic according to the user's emotion score. This makes it possible to provide personalized news based on the user's emotions.

[0088] The commodity AI system can further include a schedule management unit that manages the user's schedule and supports efficient time management. For example, it analyzes the user's plans and proposes an optimal schedule. The schedule management unit also sends reminders and notifications based on the user's schedule. For example, it sends notifications before important meetings or events. The schedule management unit also analyzes changes in the user's schedule in real time and dynamically adjusts the schedule. For example, it proposes a new schedule in response to changes in the schedule. This makes it possible to support efficient time management for the user.

[0089] The commodity AI system can further include a shopping support unit that analyzes a user's purchasing history and provides personalized shopping advice. For example, it can analyze a user's past purchase data and recommend products that match their interests. The shopping support unit can also provide special offers and discount information based on the user's purchasing patterns. For example, it can provide discount coupons for products that the user frequently purchases. The shopping support unit can also improve the recommendation algorithm based on user feedback and provide more accurate shopping advice. This can improve the user's purchasing experience.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The learning unit studies the thoughts and conversations of a specific individual. For example, the learning unit collects data such as past statements, interviews, and books of a specific individual and uses that data as a basis for learning. Step 2: The generator creates a generative AI based on the content learned by the learning unit. For example, the generator generates answers to questions or instructions from the user based on the thoughts and conversations of a specific individual. Step 3: The voice generator converts the text data generated by the generator into voice. For example, the voice generator generates voice that imitates the voice of a specific individual. Step 4: The automatic video generation unit generates a video based on the text data generated by the generation unit and the audio data generated by the audio generation unit. For example, the automatic video generation unit generates a video that imitates the appearance and facial expression of a specific individual.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning department that studies the thoughts and conversations of specific individuals, a generation unit that generates a generation AI based on the content learned by the learning unit; a voice generation unit that converts the text data generated by the generation unit into voice; an automatic video generation unit that generates a video based on the text data generated by the generation unit and the audio data generated by the audio generation unit; A system characterized by:

2. The learning unit The emotional state of the specific individual is estimated, the speech content is classified based on the emotion, and the change in emotion is reflected in learning.

2. The system of claim 1.

3. The learning unit Learn the non-verbal communication of the specific individual and recreate more natural conversations 2. The system of claim 1.

4. The learning unit Simulate the thought process of the specific individual and learn their decision-making patterns under specific circumstances.

2. The system of claim 1.

5. The learning unit Simultaneously learn the thoughts and conversations of other prominent business leaders to create a generative AI that integrates the knowledge of multiple leaders.

2. The system of claim 1.

6. The learning unit When learning the thoughts and conversations of the specific individual, data from different cultures and languages ​​will also be incorporated to create a generative AI with a global perspective.

2. The system of claim 1.

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