System

Abstract

An object of the system according to the embodiment is to express the content of a story or training of a user in various formats and store the content.SOLUTION: A system includes an interactive AI, a recording unit, a generation unit, and a storage unit. The interactive AI interacts with the user. The recording unit stores the content of the story or training of the user recorded by the interactive AI. The generation unit expresses the contents stored by the recording unit in various forms. The storage unit stores the content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not providing sufficient means for expressing and storing the content of a user's stories and training in a variety of formats.

[0005] The system according to the embodiment aims to express and store the user's story and training content in a variety of formats. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversational AI, a recording unit, a generating unit, and a storage unit. The conversational AI engages in a conversation with a user. The recording unit stores the content of the user's story or training recorded by the conversational AI. The generating unit expresses the content stored by the recording unit in various formats. The storage unit stores the content generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can express and store the user's story and training content in various formats. [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) A story recording system according to an embodiment of the present invention is a system that uses interactive AI to record a user's story and training, and expresses and saves the recorded story and training in a variety of formats. This allows the story recording system to express and save the user's story and training in a variety of formats.

[0029] A story recording system according to an embodiment includes an interactive AI, a recording unit, a generation unit, and a storage unit. The interactive AI records the user's story and training content. For example, when the user speaks to the interactive AI, it records the content as text. The recording unit stores the user's story and training content recorded by the interactive AI. For example, the recording unit stores the text data in a database. The generation unit expresses the content stored by the recording unit in various formats. For example, the generation unit converts the text data into a format such as a novel, a biography, a picture book, or a drama or movie script. The storage unit stores the content generated by the generation unit. For example, the storage unit stores the generated novel or picture book as a digital file. As a result, the story recording system according to an embodiment can express and store the user's story and training records in various formats.

[0030] Conversational AI can refer to a user's past conversation history and automatically extract related episodes to add to the record. For example, conversational AI stores a user's past conversation history in a database and automatically extracts related episodes to add to the record. For example, it generates new episodes based on what the user has previously said. Conversational AI can also analyze a user's past conversation history, extract related keywords and phrases, and add them to the record. For example, it can revisit events that the user has previously talked about. Conversational AI can also build a system that refers to a user's past conversation history and automatically extracts related episodes to add to the record. For example, it can generate new episodes based on what the user has previously said. This makes it possible to add related episodes to the record based on the user's past conversation history.

[0031] Conversational AI can automatically translate conversations in different languages ​​and enable story recording in multiple languages. Conversational AI can, for example, automatically translate conversations in different languages ​​in real time and enable story recording in multiple languages. For example, what is spoken in English can be translated into Japanese and recorded. Conversational AI can also use an automatic translation function to convert conversations in different languages ​​into text and enable story recording in multiple languages. For example, what is spoken in French can be translated into English and recorded. Conversational AI can also build a system that can automatically translate conversations in different languages ​​and enable story recording in multiple languages. For example, what is spoken in Spanish can be translated into Japanese and recorded. This makes it possible to automatically translate conversations in different languages ​​and enable story recording in multiple languages.

[0032] The generation unit allows the interactive AI to automatically generate illustrations or graphics based on the content recorded by the recording unit and express them in the form of a picture book or manga. For example, the generation unit allows the interactive AI to automatically generate illustrations and graphics based on the recorded content and express them in the form of a picture book. For example, the generation unit converts a user's story into an illustrated picture book. The generation unit also allows the interactive AI to analyze the recorded content and automatically generate illustrations and graphics to express in the form of a manga. For example, the generation unit converts the user's experiences into manga panels. The generation unit also builds a system in which the interactive AI automatically generates illustrations and graphics based on the recorded content and expresses them in the form of a picture book or manga. For example, the generation unit converts a user's story into a picture book or manga. This allows the interactive AI to automatically generate illustrations and graphics based on the recorded content and express them in the form of a picture book or manga.

[0033] The generation unit can generate an audiobook using speech synthesis technology based on the content recorded by the recording unit. The generation unit, for example, generates an audiobook using speech synthesis technology based on the recorded content. For example, it converts a user's story into a format that can be played aloud. The generation unit also uses an interactive AI to analyze the recorded content and automatically generate an audiobook using speech synthesis technology. For example, it plays back the user's experiences aloud. The generation unit also builds a system that generates an audiobook using speech synthesis technology based on the recorded content. For example, it converts a user's story into a format that can be played aloud. This makes it possible to generate an audiobook using speech synthesis technology based on the recorded content.

[0034] The generation unit can animate the content recorded by the recording unit and express it as a short animated film. The generation unit, for example, uses an interactive AI to automatically generate animation based on the recorded content and express it as a short animated film. For example, it converts a user's story into an animation. The generation unit also analyzes the recorded content using the interactive AI and automatically generates a scenario and character design for animation. For example, it converts a user's experience into a short animated film. The generation unit also builds a system in which an interactive AI automatically generates animation based on the recorded content and expresses it as a short animated film. For example, it converts a user's story into an animation. In this way, it is possible to animate the recorded content and express it as a short animated film.

[0035] The generation unit can use a conversational AI to analyze a user's training data and generate a model that predicts performance improvement. For example, the generation unit uses a conversational AI to analyze the user's training data and generate a model that predicts performance improvement. For example, the generation unit predicts performance improvement based on the user's training history. The generation unit also analyzes the training data and develops an algorithm for predicting performance improvement. For example, the generation unit predicts performance improvement based on the user's training data. The generation unit also builds a system in which the conversational AI analyzes the user's training data and generates a model that predicts performance improvement. For example, the generation unit predicts performance improvement based on the user's training history. This makes it possible to analyze the user's training data and generate a model that predicts performance improvement.

[0036] The generation unit can analyze training videos and automatically provide feedback on improvements to form or technique. The generation unit, for example, builds a system that analyzes training videos and automatically provides feedback on improvements to form or technique. For example, it analyzes a user's training video and points out improvements to form. The generation unit also uses video analysis technology to automatically extract improvements to form or technique from the training video. For example, it analyzes a user's training video and points out improvements to technique. The generation unit also develops an algorithm that analyzes training videos and automatically provides feedback on improvements to form or technique. For example, it analyzes a user's training video and points out improvements to form. This makes it possible to analyze training videos and automatically provide feedback on improvements to form or technique.

[0037] The generation unit enables the conversational AI to automatically generate an individual training plan based on the training data. The generation unit, for example, builds a system in which the conversational AI automatically generates an individual training plan based on the training data. For example, it proposes an optimal training plan based on the user's training history. The generation unit also develops an algorithm in which the conversational AI analyzes the training data and automatically generates an individual training plan. For example, it proposes an optimal training plan based on the user's training data. The generation unit also develops a system in which the conversational AI automatically generates an individual training plan based on the training data. For example, it proposes an optimal training plan based on the user's training history. This allows the conversational AI to automatically generate an individual training plan based on the training data.

[0038] The generation unit can provide a platform for sharing training records with other players or coaches and receiving feedback. The generation unit, for example, builds a platform for sharing training records with other players or coaches and receiving feedback. For example, training data is shared and feedback is received from other players or coaches. The generation unit also develops an online platform for sharing training records and provides a system for receiving feedback from other players or coaches. For example, training data is shared and feedback is received. The generation unit also builds a system for sharing training records with other players or coaches and receiving feedback. For example, training data is shared and feedback is received from other players or coaches. This makes it possible to provide a platform for sharing training records with other players or coaches and receiving feedback.

[0039] The storage unit can use conversational AI to automatically tag the user's memories, making them easier to search. For example, the storage unit builds a system in which conversational AI automatically tags the user's memories, making them easier to search. For example, it generates tags based on what the user says and makes them searchable. The storage unit also develops an algorithm in which conversational AI analyzes the user's memories and automatically tags them. For example, it generates tags based on what the user says and makes them searchable. The storage unit also develops a system in which conversational AI automatically tags the user's memories, making them easier to search. For example, it generates tags based on what the user says and makes them searchable. This allows the user's memories to be automatically tagged, making them easier to search.

[0040] The storage unit can provide a function for sharing memory records on the cloud and collaboratively editing and saving with family or friends. The storage unit, for example, builds a system for sharing memory records on the cloud and collaboratively editing and saving with family and friends. For example, memories are shared using cloud storage. The storage unit also develops a platform for sharing memory records on the cloud and collaboratively editing and saving with family and friends. For example, a collaborative editing function is provided. The storage unit also develops a system for sharing memory records on the cloud and collaboratively editing and saving with family and friends. For example, memories are shared using cloud storage. This makes it possible to share memory records on the cloud and collaboratively edit and save with family and friends.

[0041] The storage unit is capable of automatically generating an event timeline using an interactive AI based on the memory records and displaying past events in chronological order. The storage unit, for example, builds a system in which an interactive AI automatically generates an event timeline based on the memory records. For example, it displays the user's memories in chronological order. The storage unit also develops an algorithm in which the interactive AI analyzes the memory records and automatically generates an event timeline. For example, it displays the user's memories in chronological order. The storage unit also develops a system in which an interactive AI automatically generates an event timeline based on the memory records and displays past events in chronological order. For example, it displays the user's memories in chronological order. This allows the interactive AI to automatically generate an event timeline based on the memory records and display past events in chronological order.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Conversational AI can also refer to a user's past conversation history and automatically extract related episodes to add to the record. For example, it can generate new episodes based on what the user has previously said. Conversational AI can also analyze a user's past conversation history and extract related keywords and phrases to add to the record. For example, it can revisit events that the user has previously talked about. Conversational AI can also build a system that refers to a user's past conversation history and automatically extracts related episodes to add to the record. For example, it can generate new episodes based on what the user has previously said. This makes it possible to add related episodes to the record based on the user's past conversation history.

[0044] Conversational AI can also automatically translate conversations in different languages, enabling narrative recording in multiple languages. For example, conversations in different languages ​​can be automatically translated in real time, enabling narrative recording in multiple languages. For example, what is spoken in English can be translated into Japanese and recorded. Conversational AI can also use an automatic translation function to convert conversations in different languages ​​into text, enabling narrative recording in multiple languages. For example, what is spoken in French can be translated into English and recorded. Conversational AI can also build a system that automatically translates conversations in different languages, enabling narrative recording in multiple languages. For example, what is spoken in Spanish can be translated into Japanese and recorded. This makes it possible to automatically translate conversations in different languages, enabling narrative recording in multiple languages.

[0045] The generation unit can also cause the conversational AI to automatically generate illustrations or graphics based on the content recorded by the recording unit and present them in the form of a picture book or manga. For example, the conversational AI can automatically generate illustrations and graphics based on the recorded content and present them in the form of a picture book. For example, it can convert a user's story into an illustrated picture book. The generation unit can also cause the conversational AI to analyze the recorded content and automatically generate illustrations and graphics to be presented in the form of a manga. For example, it can convert a user's experience into manga panels. The generation unit can also build a system in which the conversational AI automatically generates illustrations and graphics based on the recorded content and presents them in the form of a picture book or manga. For example, it can convert a user's story into a picture book or manga. This allows the conversational AI to automatically generate illustrations and graphics based on the recorded content and present them in the form of a picture book or manga.

[0046] The generation unit can also generate an audiobook using speech synthesis technology based on the content recorded by the recording unit. For example, the generation unit generates an audiobook using speech synthesis technology based on the recorded content. For example, the generation unit converts a user's story into a format that can be played aloud. The generation unit can also analyze the recorded content using an interactive AI and automatically generate an audiobook using speech synthesis technology. For example, the generation unit plays back the user's experiences aloud. The generation unit can also build a system that generates an audiobook using speech synthesis technology based on the recorded content. For example, the generation unit converts a user's story into a format that can be played aloud. This allows the generation of an audiobook using speech synthesis technology based on the recorded content.

[0047] The generation unit can also animate the content recorded by the recording unit and express it as a short animated film. For example, an interactive AI can automatically generate animation based on the recorded content and express it as a short animated film. For example, a user's story can be converted into an animation. The generation unit can also analyze the recorded content by the interactive AI and automatically generate a scenario and character design for animation. For example, it can convert a user's experience into a short animated film. The generation unit can also build a system in which an interactive AI automatically generates animation based on the recorded content and expresses it as a short animated film. For example, it can convert a user's story into an animation. This makes it possible to animate the recorded content and express it as a short animated film.

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

[0049] Step 1: The conversational AI records the user's story and training. For example, when the user speaks to the conversational AI, the content is recorded as text. Step 2: The recording unit stores the user's story and training content recorded by the conversational AI. For example, the recording unit stores the text data in a database. Step 3: The generator expresses the content stored by the recorder in various formats. For example, the generator converts the text data into a novel, biography, picture book, drama or movie script, etc. Step 4: The storage unit stores the content generated by the generation unit. For example, the storage unit stores the generated novel or picture book as a digital file.

[0050] (Example 2) A story recording system according to an embodiment of the present invention is a system that uses interactive AI to record a user's story and training, and expresses and saves the recorded story and training in a variety of formats. This allows the story recording system to express and save the user's story and training in a variety of formats.

[0051] A story recording system according to an embodiment includes an interactive AI, a recording unit, a generation unit, and a storage unit. The interactive AI records the user's story and training content. For example, when the user speaks to the interactive AI, it records the content as text. The recording unit stores the user's story and training content recorded by the interactive AI. For example, the recording unit stores the text data in a database. The generation unit expresses the content stored by the recording unit in various formats. For example, the generation unit converts the text data into a format such as a novel, a biography, a picture book, or a drama or movie script. The storage unit stores the content generated by the generation unit. For example, the storage unit stores the generated novel or picture book as a digital file. As a result, the story recording system according to an embodiment can express and store the user's story and training records in various formats.

[0052] Conversational AI can analyze a user's tone of voice or speaking style and reflect changes in emotion in the recording. For example, conversational AI can analyze a user's tone of voice and speaking style in real time and reflect changes in emotion in the recording. For example, if a user speaks excitedly, that emotion is reflected in the text. Conversational AI can also analyze the user's tone of voice and quantify the intensity of the emotion and add it to the recording. For example, if a user speaks sadly, that emotion is expressed numerically. Conversational AI can also analyze a user's speaking patterns and automatically detect changes in emotion and reflect them in the recording. For example, if a user suddenly raises their voice, that change is reflected in the recording. This allows changes in the user's emotion to be reflected in the recording.

[0053] Conversational AI can refer to a user's past conversation history and automatically extract related episodes to add to the record. For example, conversational AI stores a user's past conversation history in a database and automatically extracts related episodes to add to the record. For example, it generates new episodes based on what the user has previously said. Conversational AI can also analyze a user's past conversation history, extract related keywords and phrases, and add them to the record. For example, it can revisit events that the user has previously talked about. Conversational AI can also build a system that refers to a user's past conversation history and automatically extracts related episodes to add to the record. For example, it can generate new episodes based on what the user has previously said. This makes it possible to add related episodes to the record based on the user's past conversation history.

[0054] Conversational AI uses an emotion estimation function to suggest story developments based on the user's emotions, making it possible to create records that users can easily empathize with emotionally. For example, conversational AI uses the emotion estimation function to suggest story developments based on the user's emotions. For example, if the user is telling a sad story, the system will suggest developments that empathize with those emotions. Conversational AI also analyzes the user's emotions in real time and automatically generates story developments based on those emotions. For example, if the user is happy, the system will suggest developments that match those emotions. Conversational AI also uses the emotion estimation function to build a system that suggests story developments that users can easily empathize with emotionally. For example, the system will adjust the story development based on the user's emotions. This makes it possible to suggest story developments based on the user's emotions and create records that users can easily empathize with emotionally.

[0055] Conversational AI can analyze a user's gestures or facial expressions using a camera and reflect that information in a recording. Conversational AI can, for example, use a camera to analyze a user's gestures and facial expressions in real time and reflect that information in a recording. For example, if a user is smiling while speaking, that facial expression is added to the recording. Conversational AI can also analyze a user's gestures and facial expressions and reflect changes in emotion in a recording. For example, if a user is waving, that action is added to the recording. Conversational AI can also analyze a user's gestures and facial expressions using a camera, convert that information into text, and reflect that information in a recording. For example, if a user has a surprised expression, that expression is added to the recording. In this way, the user's gestures and facial expressions can be reflected in a recording.

[0056] Conversational AI can automatically translate conversations in different languages ​​and enable story recording in multiple languages. Conversational AI can, for example, automatically translate conversations in different languages ​​in real time and enable story recording in multiple languages. For example, what is spoken in English can be translated into Japanese and recorded. Conversational AI can also use an automatic translation function to convert conversations in different languages ​​into text and enable story recording in multiple languages. For example, what is spoken in French can be translated into English and recorded. Conversational AI can also build a system that can automatically translate conversations in different languages ​​and enable story recording in multiple languages. For example, what is spoken in Spanish can be translated into Japanese and recorded. This makes it possible to automatically translate conversations in different languages ​​and enable story recording in multiple languages.

[0057] Conversational AI can use emotion estimation functions to analyze the emotions a user feels when speaking in real time and generate questions to elicit positive emotions. For example, conversational AI can use emotion estimation functions to analyze the emotions a user feels when speaking in real time and generate questions to elicit positive emotions. For example, if a user is talking about something sad, it generates encouraging questions. Conversational AI can also analyze a user's emotions in real time and automatically generate questions to elicit positive emotions based on those emotions. For example, if a user is feeling depressed, it generates encouraging questions. Conversational AI can also use emotion estimation functions to build a system that analyzes the emotions a user feels when speaking and generates questions to elicit positive emotions. For example, if a user is feeling anxious, it generates reassuring questions. This makes it possible to analyze a user's emotions in real time and generate questions to elicit positive emotions.

[0058] The generation unit allows the interactive AI to automatically generate illustrations or graphics based on the content recorded by the recording unit and express them in the form of a picture book or manga. For example, the generation unit allows the interactive AI to automatically generate illustrations and graphics based on the recorded content and express them in the form of a picture book. For example, the generation unit converts a user's story into an illustrated picture book. The generation unit also allows the interactive AI to analyze the recorded content and automatically generate illustrations and graphics to express in the form of a manga. For example, the generation unit converts the user's experiences into manga panels. The generation unit also builds a system in which the interactive AI automatically generates illustrations and graphics based on the recorded content and expresses them in the form of a picture book or manga. For example, the generation unit converts a user's story into a picture book or manga. This allows the interactive AI to automatically generate illustrations and graphics based on the recorded content and express them in the form of a picture book or manga.

[0059] The generation unit can generate an audiobook using speech synthesis technology based on the content recorded by the recording unit. The generation unit, for example, generates an audiobook using speech synthesis technology based on the recorded content. For example, it converts a user's story into a format that can be played aloud. The generation unit also uses an interactive AI to analyze the recorded content and automatically generate an audiobook using speech synthesis technology. For example, it plays back the user's experiences aloud. The generation unit also builds a system that generates an audiobook using speech synthesis technology based on the recorded content. For example, it converts a user's story into a format that can be played aloud. This makes it possible to generate an audiobook using speech synthesis technology based on the recorded content.

[0060] The generation unit can use the emotion estimation function to add music or sound effects based on the user's emotions, thereby achieving more emotional expression. The generation unit, for example, uses the emotion estimation function to add music or sound effects based on the user's emotions to the recorded content. For example, if the user is happy, cheerful music is added. The generation unit also analyzes the user's emotions in real time and automatically generates music or sound effects based on the emotions. For example, if the user is talking about something sad, moving music is added. The generation unit also uses the emotion estimation function to build a system that adds music or sound effects based on the user's emotions, thereby achieving more emotional expression. For example, the music or sound effects are adjusted to match the user's emotions. This allows music or sound effects based on the user's emotions to be added, thereby achieving more emotional expression.

[0061] The generation unit can animate the content recorded by the recording unit and express it as a short animated film. The generation unit, for example, uses an interactive AI to automatically generate animation based on the recorded content and express it as a short animated film. For example, it converts a user's story into an animation. The generation unit also analyzes the recorded content using the interactive AI and automatically generates a scenario and character design for animation. For example, it converts a user's experience into a short animated film. The generation unit also builds a system in which an interactive AI automatically generates animation based on the recorded content and expresses it as a short animated film. For example, it converts a user's story into an animation. In this way, it is possible to animate the recorded content and express it as a short animated film.

[0062] The generation unit can use the emotion estimation function to automatically select a color or design based on the user's emotion and generate visual content. For example, the generation unit uses the emotion estimation function to add a color or design based on the user's emotion to the recorded content. For example, if the user is happy, a bright color is used. The generation unit also analyzes the user's emotion in real time and automatically generates a color or design based on the emotion. For example, if the user is talking about something sad, a calm color is used. The generation unit also uses the emotion estimation function to automatically select a color or design based on the user's emotion and build a system for generating visual content. For example, the color or design is adjusted according to the user's emotion. In this way, it is possible to automatically select a color or design based on the user's emotion and generate visual content.

[0063] The generation unit can use a conversational AI to analyze a user's training data and generate a model that predicts performance improvement. For example, the generation unit uses a conversational AI to analyze the user's training data and generate a model that predicts performance improvement. For example, the generation unit predicts performance improvement based on the user's training history. The generation unit also analyzes the training data and develops an algorithm for predicting performance improvement. For example, the generation unit predicts performance improvement based on the user's training data. The generation unit also builds a system in which the conversational AI analyzes the user's training data and generates a model that predicts performance improvement. For example, the generation unit predicts performance improvement based on the user's training history. This makes it possible to analyze the user's training data and generate a model that predicts performance improvement.

[0064] The generation unit can analyze training videos and automatically provide feedback on improvements to form or technique. The generation unit, for example, builds a system that analyzes training videos and automatically provides feedback on improvements to form or technique. For example, it analyzes a user's training video and points out improvements to form. The generation unit also uses video analysis technology to automatically extract improvements to form or technique from the training video. For example, it analyzes a user's training video and points out improvements to technique. The generation unit also develops an algorithm that analyzes training videos and automatically provides feedback on improvements to form or technique. For example, it analyzes a user's training video and points out improvements to form. This makes it possible to analyze training videos and automatically provide feedback on improvements to form or technique.

[0065] The generation unit can use the emotion estimation function to record the mental state during training and evaluate mental growth. The generation unit, for example, uses the emotion estimation function to record the mental state during training in real time and evaluate the mental growth based on the data. For example, it records the user's emotion score and evaluates the mental growth. The generation unit also analyzes the mental state during training using the emotion estimation function and builds a system to evaluate the mental growth based on the data. For example, it records the user's emotion changes and evaluates the mental growth. The generation unit also uses the emotion estimation function to record the mental state during training and develops an algorithm to evaluate the mental growth based on the data. For example, it records the user's emotion score and evaluates the mental growth. In this way, the mental state during training can be recorded and the mental growth can be evaluated.

[0066] The generation unit enables the conversational AI to automatically generate an individual training plan based on the training data. The generation unit, for example, builds a system in which the conversational AI automatically generates an individual training plan based on the training data. For example, it proposes an optimal training plan based on the user's training history. The generation unit also develops an algorithm in which the conversational AI analyzes the training data and automatically generates an individual training plan. For example, it proposes an optimal training plan based on the user's training data. The generation unit also develops a system in which the conversational AI automatically generates an individual training plan based on the training data. For example, it proposes an optimal training plan based on the user's training history. This allows the conversational AI to automatically generate an individual training plan based on the training data.

[0067] The generation unit can provide a platform for sharing training records with other players or coaches and receiving feedback. The generation unit, for example, builds a platform for sharing training records with other players or coaches and receiving feedback. For example, training data is shared and feedback is received from other players or coaches. The generation unit also develops an online platform for sharing training records and provides a system for receiving feedback from other players or coaches. For example, training data is shared and feedback is received. The generation unit also builds a system for sharing training records with other players or coaches and receiving feedback. For example, training data is shared and feedback is received from other players or coaches. This makes it possible to provide a platform for sharing training records with other players or coaches and receiving feedback.

[0068] The generation unit can use the emotion estimation function to monitor changes in emotions during training in real time and provide advice to maintain motivation. The generation unit, for example, uses the emotion estimation function to build a system that monitors changes in emotions during training in real time and provides advice to maintain motivation. For example, the generation unit provides advice based on the user's emotion score. The generation unit also analyzes changes in emotions during training using the emotion estimation function and provides advice to maintain motivation in real time. For example, the generation unit provides advice based on the user's emotion change. The generation unit also uses the emotion estimation function to develop an algorithm that monitors changes in emotions during training in real time and provides advice to maintain motivation. For example, the generation unit provides advice based on the user's emotion score. This makes it possible to monitor changes in emotions during training in real time and provide advice to maintain motivation.

[0069] The storage unit can use conversational AI to automatically tag the user's memories, making them easier to search. For example, the storage unit builds a system in which conversational AI automatically tags the user's memories, making them easier to search. For example, it generates tags based on what the user says and makes them searchable. The storage unit also develops an algorithm in which conversational AI analyzes the user's memories and automatically tags them. For example, it generates tags based on what the user says and makes them searchable. The storage unit also develops a system in which conversational AI automatically tags the user's memories, making them easier to search. For example, it generates tags based on what the user says and makes them searchable. This allows the user's memories to be automatically tagged, making them easier to search.

[0070] The storage unit can use the emotion estimation function to add emotional comments or messages to memory records, thereby providing a more moving storage method. The storage unit, for example, uses the emotion estimation function to build a system for adding emotional comments or messages to memory records. For example, the storage unit adds emotional comments based on a user's emotion score. The storage unit also analyzes memory records with the emotion estimation function and develops an algorithm that automatically generates emotional comments or messages. For example, the storage unit adds comments based on changes in the user's emotions. The storage unit also uses the emotion estimation function to develop a system that adds emotional comments or messages to memory records, thereby providing a more moving storage method. For example, the storage unit adds comments based on a user's emotion score. This allows emotional comments or messages to be added to memory records, thereby providing a more moving storage method.

[0071] The storage unit can provide a function for sharing memory records on the cloud and collaboratively editing and saving with family or friends. The storage unit, for example, builds a system for sharing memory records on the cloud and collaboratively editing and saving with family and friends. For example, memories are shared using cloud storage. The storage unit also develops a platform for sharing memory records on the cloud and collaboratively editing and saving with family and friends. For example, a collaborative editing function is provided. The storage unit also develops a system for sharing memory records on the cloud and collaboratively editing and saving with family and friends. For example, memories are shared using cloud storage. This makes it possible to share memory records on the cloud and collaboratively edit and save with family and friends.

[0072] The storage unit is capable of automatically generating an event timeline using an interactive AI based on the memory records and displaying past events in chronological order. The storage unit, for example, builds a system in which an interactive AI automatically generates an event timeline based on the memory records. For example, it displays the user's memories in chronological order. The storage unit also develops an algorithm in which the interactive AI analyzes the memory records and automatically generates an event timeline. For example, it displays the user's memories in chronological order. The storage unit also develops a system in which an interactive AI automatically generates an event timeline based on the memory records and displays past events in chronological order. For example, it displays the user's memories in chronological order. This allows the interactive AI to automatically generate an event timeline based on the memory records and display past events in chronological order.

[0073] The storage unit can use the emotion estimation function to analyze the user's emotional response to the memory record and highlight the most moving moments. The storage unit, for example, uses the emotion estimation function to analyze the user's emotional response to the memory record and build a system that highlights the most moving moments. For example, the storage unit highlights moving moments based on the user's emotion score. The storage unit also analyzes the memory record with the emotion estimation function and develops an algorithm that automatically highlights moments that are likely to be emotionally relatable. For example, the storage unit highlights moving moments based on changes in the user's emotions. The storage unit also uses the emotion estimation function to analyze the user's emotional response to the memory record and develops a system that highlights the most moving moments. For example, the storage unit highlights moving moments based on the user's emotion score. In this way, the user's emotional response to the memory record can be analyzed and the most moving moments can be highlighted.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] Conversational AI not only records the user's story and training content, but can also analyze the user's tone of voice and speaking style, and reflect changes in emotion in the recording. For example, if the user speaks excitedly, that emotion can be reflected in the text. Conversational AI can also analyze the user's tone of voice, quantify the intensity of the emotion, and add it to the recording. For example, if the user speaks sadly, that emotion can be expressed numerically. Conversational AI can also analyze the user's speaking patterns, automatically detecting changes in emotion and reflecting them in the recording. For example, if the user suddenly raises their voice, that change can be reflected in the recording. This allows changes in the user's emotion to be reflected in the recording.

[0076] Conversational AI can also refer to a user's past conversation history and automatically extract related episodes to add to the record. For example, it can generate new episodes based on what the user has previously said. Conversational AI can also analyze a user's past conversation history and extract related keywords and phrases to add to the record. For example, it can revisit events that the user has previously talked about. Conversational AI can also build a system that refers to a user's past conversation history and automatically extracts related episodes to add to the record. For example, it can generate new episodes based on what the user has previously said. This makes it possible to add related episodes to the record based on the user's past conversation history.

[0077] Conversational AI can use its emotion estimation function to suggest story developments based on the user's emotions, creating records that users can easily empathize with. For example, if the user is telling a sad story, it can suggest developments that empathize with those emotions. Conversational AI can also analyze the user's emotions in real time and automatically generate story developments based on those emotions. For example, if the user is happy, it can suggest developments that match those emotions. Conversational AI can also use its emotion estimation function to build a system that suggests story developments that users can easily empathize with. For example, it can adjust the story development based on the user's emotions. This makes it possible to suggest story developments based on the user's emotions and create records that users can easily empathize with.

[0078] Conversational AI can also analyze a user's gestures or facial expressions using a camera and reflect that information in a recording. For example, a camera can be used to analyze a user's gestures and facial expressions in real time and reflect that information in a recording. For example, if a user is smiling while speaking, that facial expression can be added to the recording. Conversational AI can also analyze a user's gestures and facial expressions and reflect changes in emotion in a recording. For example, if a user is waving, that action can be added to the recording. Conversational AI can also analyze a user's gestures and facial expressions using a camera and convert that information into text and reflect that information in a recording. For example, if a user has a surprised expression, that expression can be added to the recording. This allows the user's gestures and facial expressions to be reflected in a recording.

[0079] Conversational AI can also automatically translate conversations in different languages, enabling narrative recording in multiple languages. For example, conversations in different languages ​​can be automatically translated in real time, enabling narrative recording in multiple languages. For example, what is spoken in English can be translated into Japanese and recorded. Conversational AI can also use an automatic translation function to convert conversations in different languages ​​into text, enabling narrative recording in multiple languages. For example, what is spoken in French can be translated into English and recorded. Conversational AI can also build a system that automatically translates conversations in different languages, enabling narrative recording in multiple languages. For example, what is spoken in Spanish can be translated into Japanese and recorded. This makes it possible to automatically translate conversations in different languages, enabling narrative recording in multiple languages.

[0080] The generation unit can also cause the conversational AI to automatically generate illustrations or graphics based on the content recorded by the recording unit and present them in the form of a picture book or manga. For example, the conversational AI can automatically generate illustrations and graphics based on the recorded content and present them in the form of a picture book. For example, it can convert a user's story into an illustrated picture book. The generation unit can also cause the conversational AI to analyze the recorded content and automatically generate illustrations and graphics to be presented in the form of a manga. For example, it can convert a user's experience into manga panels. The generation unit can also build a system in which the conversational AI automatically generates illustrations and graphics based on the recorded content and presents them in the form of a picture book or manga. For example, it can convert a user's story into a picture book or manga. This allows the conversational AI to automatically generate illustrations and graphics based on the recorded content and present them in the form of a picture book or manga.

[0081] The generation unit can also generate an audiobook using speech synthesis technology based on the content recorded by the recording unit. For example, the generation unit generates an audiobook using speech synthesis technology based on the recorded content. For example, the generation unit converts a user's story into a format that can be played aloud. The generation unit can also analyze the recorded content using an interactive AI and automatically generate an audiobook using speech synthesis technology. For example, the generation unit plays back the user's experiences aloud. The generation unit can also build a system that generates an audiobook using speech synthesis technology based on the recorded content. For example, the generation unit converts a user's story into a format that can be played aloud. This allows the generation of an audiobook using speech synthesis technology based on the recorded content.

[0082] The generation unit can also use the emotion estimation function to add music or sound effects based on the user's emotions to achieve more emotional expression. For example, using the emotion estimation function, music and sound effects based on the user's emotions are added to the recorded content. For example, if the user is happy, cheerful music is added. The generation unit can also analyze the user's emotions in real time and automatically generate music and sound effects based on the emotions. For example, if the user is talking about something sad, moving music is added. The generation unit can also use the emotion estimation function to build a system that adds music and sound effects based on the user's emotions to achieve more emotional expression. For example, the music and sound effects can be adjusted to match the user's emotions. This makes it possible to add music and sound effects based on the user's emotions and achieve more emotional expression.

[0083] The generation unit can also animate the content recorded by the recording unit and express it as a short animated film. For example, an interactive AI can automatically generate animation based on the recorded content and express it as a short animated film. For example, a user's story can be converted into an animation. The generation unit can also analyze the recorded content by the interactive AI and automatically generate a scenario and character design for animation. For example, it can convert a user's experience into a short animated film. The generation unit can also build a system in which an interactive AI automatically generates animation based on the recorded content and expresses it as a short animated film. For example, it can convert a user's story into an animation. This makes it possible to animate the recorded content and express it as a short animated film.

[0084] The generation unit can also use the emotion estimation function to automatically select colors or designs based on the user's emotions and generate visual content. For example, the emotion estimation function can be used to add colors or designs based on the user's emotions to the recorded content. For example, if the user is happy, bright colors can be used. The generation unit can also analyze the user's emotions in real time and automatically generate colors or designs based on the emotions. For example, if the user is talking about something sad, calm colors can be used. The generation unit can also use the emotion estimation function to build a system that automatically selects colors or designs based on the user's emotions and generates visual content. For example, the color or design can be adjusted to match the user's emotions. In this way, colors and designs based on the user's emotions can be automatically selected and visual content can be generated.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The conversational AI records the user's story and training. For example, when the user speaks to the conversational AI, the content is recorded as text. Step 2: The recording unit stores the user's story and training content recorded by the conversational AI. For example, the recording unit stores the text data in a database. Step 3: The generator expresses the content stored by the recorder in various formats. For example, the generator converts the text data into a novel, biography, picture book, drama or movie script, etc. Step 4: The storage unit stores the content generated by the generation unit. For example, the storage unit stores the generated novel or picture book as a digital file.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 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. Conversational AI and A recording unit that stores the content of the user's story or training recorded by the conversational AI; a generating unit that expresses the content stored by the recording unit in various formats; a storage unit that stores the content generated by the generation unit; A system characterized by:

2. The conversational AI is Analyzing the user's tone of voice or speaking style and reflecting emotional changes in the recording 2. The system of claim 1.

3. The conversational AI is The system according to claim 1, wherein the system references the user's past conversation history and automatically extracts and adds related episodes to the record.

4. The conversational AI is The system according to claim 1, wherein the system suggests a story development based on the user's emotions and creates a record that the user can easily empathize with emotionally.

5. The conversational AI is The camera analyzes the user's gestures or facial expressions and reflects them in the recording.

2. The system of claim 1.

6. The conversational AI is 10. The system of claim 1, wherein the system automatically translates conversations in different languages ​​and enables narrative recording in multiple languages.

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