System

Abstract

An object of a system according to an exemplary embodiment is to efficiently describe the same contents in a plurality of documents.SOLUTION: A system includes an input analysis unit, a format conversion unit, and a transcription unit. The input analysis unit analyzes the content input by the user. The format conversion unit converts the input content analyzed by the input analysis unit into a form suitable for the format of each document. The transcription part automatically transcribes the contents converted by the format conversion part to a plurality of documents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it takes time and effort to write the same content on multiple documents.

[0005] The system according to the embodiment aims to efficiently write the same content on multiple documents. [Means for solving the problem]

[0006] The system according to the embodiment includes an input analysis unit, a format conversion unit, and a transcription unit. The input analysis unit analyzes the content entered by the user. The format conversion unit converts the input content analyzed by the input analysis unit into a format suitable for each document. The transcription unit automatically transcribes the content converted by the format conversion unit into multiple documents. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently write the same content on multiple documents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The document creation system according to the embodiment of the present invention is a system that automatically transfers information entered by a user once to multiple documents. This allows the document creation system to automatically reflect information entered by a user once in multiple documents, thereby significantly reducing the time required for document creation.

[0029] A document creation system according to an embodiment includes an input analysis unit, a format conversion unit, and a transcription unit. The input analysis unit analyzes content input by a user. For example, the input analysis unit uses a generation AI to analyze text input by a user and understand its content. The input analysis unit can also analyze the input content using natural language processing technology. For example, the generation AI analyzes meeting minutes input by a user and extracts each item of the minutes (date and time, attendees, agenda, agenda content, etc.). The format conversion unit converts the input content analyzed by the input analysis unit into a format appropriate for each document. For example, the format conversion unit converts the content analyzed using the generation AI into PDF format or Word format. The format conversion unit can also convert into Excel format or presentation format. For example, the generation AI converts the analyzed content into a different document format, such as a meeting report or a project progress report, based on the analyzed content. The transcription unit automatically transcribes the content converted by the format conversion unit into multiple documents. For example, the transcription unit automatically transcribes the content analyzed using the generation AI into a meeting report or a project progress report. The transcription unit can also automatically transcribe the content analyzed using the generation AI into different document formats. For example, the generation AI automatically transcribes meeting minutes into a meeting report or a project progress report based on the analyzed content. This allows the document creation system according to the embodiment to automatically reflect content entered by a user once in multiple documents, significantly reducing the time required for document creation.

[0030] The input analysis unit can reference the user's past input history and automatically complete frequently occurring terms or phrases. For example, the input analysis unit allows the generation AI to analyze the user's past input history and automatically complete frequently occurring terms and phrases. For example, it automatically suggests specific technical terms and phrases that the user has used in the past. Furthermore, when the user starts typing, the input analysis unit allows the generation AI to suggest appropriate terms and phrases in real time based on the user's past input history. For example, when entering meeting minutes, it automatically displays phrases used in past minutes. Furthermore, the input analysis unit provides a function where the generation AI learns the user's past input history and automatically completes frequently occurring terms and phrases. For example, if the user frequently uses a specific industry term, it automatically completes that term. This improves input efficiency by automatically completing frequently occurring terms and phrases based on the user's past input history.

[0031] The format conversion unit can automatically convert data into terminology or abbreviations specific to the user's industry. For example, the format conversion unit allows the generation AI to automatically recognize terminology and abbreviations specific to the user's industry and convert the analyzed content into those terms and abbreviations. For example, it automatically converts terms in the medical industry. The format conversion unit also provides a function to analyze content entered by the user and automatically convert it into industry-specific terminology and abbreviations. For example, it automatically expands abbreviations in the IT industry. The format conversion unit also allows the generation AI to learn terminology and abbreviations specific to the user's industry and automatically convert the analyzed content into those terms and abbreviations. For example, it automatically converts terminology in the legal industry. This allows the creation of industry-specific documents by automatically converting data into terminology and abbreviations specific to the user's industry.

[0032] The format conversion unit can automatically translate the analyzed content into different languages ​​to support international document creation. For example, the format conversion unit automatically translates content analyzed by the generation AI into different languages ​​to support international document creation. For example, content entered in English is automatically translated into French or Chinese. The format conversion unit also analyzes content entered by users and provides a function to automatically translate it into different languages. For example, it creates meeting minutes in multiple languages. The format conversion unit also automatically translates into different languages ​​based on the content analyzed by the generation AI, building a system to support international document creation. For example, it creates project reports in multiple languages. This makes it easier to create international documents by automatically translating the analyzed content into different languages.

[0033] The input analysis unit can support voice input or handwritten input, thereby diversifying input methods. The input analysis unit, for example, provides a function that allows the generation AI to analyze voice input or handwritten input and convert it into text data. For example, creating meeting minutes using voice input. The input analysis unit also allows users to input content using voice input or handwritten input, and the generation AI analyzes that content and converts it into text data. For example, the input analysis unit automatically converts handwritten notes into text. The input analysis unit also builds a system in which the generation AI analyzes voice input or handwritten input and automatically transcribes it into different documents. For example, content entered via voice can be automatically transcribed into a meeting report or project progress report. This allows the system to support voice input or handwritten input, thereby diversifying input methods.

[0034] The transcription unit can automatically generate the optimal layout for each document format. For example, the generation AI automatically generates the optimal layout for each document format and transcribes the content. For example, it automatically adjusts the layout of a meeting report and a project progress report. The transcription unit also analyzes the content entered by the user and provides a function to automatically generate a layout appropriate for each document format. For example, it adjusts the layout to suit different document formats. The transcription unit also builds a system in which the generation AI generates the optimal layout for each document format and transcribes the content when automatically transcribing. For example, it automatically transcribes meeting minutes into different document formats. This improves the appearance of documents by automatically generating the optimal layout for each document format.

[0035] The transcription unit can use a common template to maintain consistency between documents. For example, when the generation AI automatically transcribes, the transcription unit uses a common template to maintain consistency between documents. For example, the same template is used for meeting reports and project status reports. The transcription unit also analyzes the content entered by the user and provides a function to automatically transcribe it into multiple documents using a common template. For example, a common template is applied to different document formats. The transcription unit also builds a system in which a common template is used to maintain consistency between documents when the generation AI automatically transcribes. For example, meeting minutes are transcribed into different document formats using a common template. This improves the sense of unity of the documents by using a common template to maintain consistency between documents.

[0036] The transcription unit can provide customizable templates for different industries or applications. For example, the generation AI provides customizable templates for different industries or applications and automatically transcribes them. For example, templates specialized for the medical or IT industry are used. The transcription unit also provides a function to analyze content entered by the user and provide customizable templates for different industries or applications. For example, templates specialized for the legal or education industry are used. The transcription unit also builds a system that provides customizable templates for different industries or applications when the generation AI automatically transcribes. For example, templates specialized for the marketing or manufacturing industry are used. This makes it possible to meet a wide range of needs by providing customizable templates for different industries and applications.

[0037] The transcription unit can visualize the contents of a document and automatically generate graphs or charts. For example, when the generation AI automatically transcribes, the transcription unit visualizes the contents of the document and automatically generates graphs and charts. For example, it creates a graph showing progress based on meeting minutes. The transcription unit also analyzes the content entered by the user and provides a function to visualize the contents of the document. For example, it creates a chart showing results based on a project report. The transcription unit also builds a system that visualizes the contents of the document and automatically generates graphs and charts when the generation AI automatically transcribes. For example, it visually displays data based on a meeting report. In this way, visualizing the contents of the document and automatically generating graphs and charts makes it easier to visually understand the information.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] When analyzing the user's input, the input analyzer can refer to the user's past input history and automatically complete frequently occurring terms and phrases. For example, it can automatically suggest specific technical terms and phrases that the user has used in the past. The input analyzer can also suggest appropriate terms and phrases in real time based on the user's past input history when the user starts input. For example, when entering meeting minutes, it can automatically display phrases used in past minutes. The input analyzer can also learn the user's past input history and provide a function to automatically complete frequently occurring terms and phrases. For example, if the user frequently uses a specific industry term, it can automatically complete that term. This improves input efficiency by automatically completing frequently occurring terms and phrases based on the user's past input history.

[0040] The format conversion unit can automatically recognize technical terms and abbreviations specific to the user's industry and convert the analyzed content into those terms and abbreviations. For example, it can automatically convert terms in the medical industry. The format conversion unit can also provide a function that analyzes content entered by the user and automatically converts it into industry-specific technical terms and abbreviations. For example, it can automatically expand abbreviations in the IT industry. The format conversion unit can also learn technical terms and abbreviations specific to the user's industry and automatically convert the analyzed content into those terms and abbreviations. For example, it can automatically convert technical terms in the legal industry. This allows for the creation of industry-specific documents by automatically converting to technical terms and abbreviations specific to the user's industry.

[0041] The format conversion unit can automatically translate the analyzed content into different languages ​​to support international document creation. For example, content entered in English can be automatically translated into French or Chinese. The format conversion unit can also provide a function for analyzing content entered by a user and automatically translating it into different languages. For example, it can create meeting minutes in multiple languages. The format conversion unit can also automatically translate the analyzed content into different languages ​​to build a system that supports international document creation. For example, it can create project reports in multiple languages. This makes it easier to create international documents by automatically translating the analyzed content into different languages.

[0042] The input analysis unit can support voice input or handwritten input, thereby diversifying input methods. For example, the generation AI can provide a function to analyze voice input or handwritten input and convert it into text data. For example, meeting minutes can be created using voice input. The input analysis unit can also allow users to input content using voice input or handwritten input, and the generation AI can analyze that content and convert it into text data. For example, it can automatically convert handwritten notes into text. The input analysis unit can also build a system that analyzes voice input or handwritten input and automatically transcribes it into different documents. For example, content entered via voice can be automatically transcribed into a meeting report or project progress report. This allows for the diversification of input methods by supporting voice input or handwritten input.

[0043] The transcription unit can automatically generate the optimal layout for each document format. For example, the generation AI can automatically generate the optimal layout for each document format and transcribe the content. For example, it can automatically adjust the layout of a meeting report and a project progress report. The transcription unit can also provide a function that analyzes the content entered by the user and automatically generates a layout appropriate for each document format. For example, it can adjust the layout to suit different document formats. The transcription unit can also build a system in which the generation AI automatically transcribes the content by generating the optimal layout for each document format. For example, it can automatically transcribe meeting minutes into different document formats. This improves the appearance of documents by automatically generating the optimal layout for each document format.

[0044] The transcription unit can use a common template to maintain consistency between documents. For example, when the generation AI automatically transcribes, a common template can be used to maintain consistency between documents. For example, the same template can be used for meeting reports and project status reports. The transcription unit can also analyze the content entered by the user and provide a function to automatically transcribe it into multiple documents using a common template. For example, a common template can be applied to different document formats. The transcription unit can also build a system in which a common template is used to maintain consistency between documents when the generation AI automatically transcribes. For example, a common template can be used to transcribe meeting minutes into different document formats. This improves the sense of unity of documents by using a common template to maintain consistency between documents.

[0045] The transcription unit can provide customizable templates for different industries or applications. For example, the generation AI can provide customizable templates for different industries or applications and automatically transcribe them. For example, templates specialized for the medical or IT industry can be used. The transcription unit can also provide a function to analyze content entered by the user and provide customizable templates for different industries or applications. For example, templates specialized for the legal or education industry can be used. The transcription unit can also build a system that provides customizable templates for different industries or applications when the generation AI automatically transcribes. For example, templates specialized for the marketing or manufacturing industry can be used. This makes it possible to meet a wide range of needs by providing customizable templates for different industries and applications.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The input analysis unit analyzes the content entered by the user. For example, the input analysis unit uses a generation AI to analyze the text entered by the user and understand its content. The input analysis unit can also analyze the input content using natural language processing technology. For example, the generation AI analyzes the minutes of a meeting entered by the user and extracts each item in the minutes (date and time, attendees, agenda, content of the meeting, etc.). Step 2: The format conversion unit converts the input content analyzed by the input analysis unit into a format appropriate for each document. For example, the format conversion unit uses the generation AI to convert the analyzed content into PDF or Word format. The format conversion unit can also convert into Excel or presentation format. For example, the generation AI converts the analyzed content into a different document format, such as a meeting report or project progress report. Step 3: The transcription unit automatically transcribes the content converted by the format conversion unit into multiple documents. For example, the transcription unit automatically transcribes the content analyzed using the generation AI into a meeting report or a project progress report. The transcription unit can also automatically transcribe the content analyzed using the generation AI into different document formats. For example, the generation AI automatically transcribes meeting minutes into a meeting report or a project progress report based on the analyzed content.

[0048] (Example 2) The document creation system according to the embodiment of the present invention is a system that automatically transfers information entered by a user once to multiple documents. This allows the document creation system to automatically reflect information entered by a user once in multiple documents, thereby significantly reducing the time required for document creation.

[0049] A document creation system according to an embodiment includes an input analysis unit, a format conversion unit, and a transcription unit. The input analysis unit analyzes content input by a user. For example, the input analysis unit uses a generation AI to analyze text input by a user and understand its content. The input analysis unit can also analyze the input content using natural language processing technology. For example, the generation AI analyzes meeting minutes input by a user and extracts each item of the minutes (date and time, attendees, agenda, agenda content, etc.). The format conversion unit converts the input content analyzed by the input analysis unit into a format appropriate for each document. For example, the format conversion unit converts the content analyzed using the generation AI into PDF format or Word format. The format conversion unit can also convert into Excel format or presentation format. For example, the generation AI converts the analyzed content into a different document format, such as a meeting report or a project progress report, based on the analyzed content. The transcription unit automatically transcribes the content converted by the format conversion unit into multiple documents. For example, the transcription unit automatically transcribes the content analyzed using the generation AI into a meeting report or a project progress report. The transcription unit can also automatically transcribe the content analyzed using the generation AI into different document formats. For example, the generation AI automatically transcribes meeting minutes into a meeting report or a project progress report based on the analyzed content. This allows the document creation system according to the embodiment to automatically reflect content entered by a user once in multiple documents, significantly reducing the time required for document creation.

[0050] The input analysis unit can reference the user's past input history and automatically complete frequently occurring terms or phrases. For example, the input analysis unit allows the generation AI to analyze the user's past input history and automatically complete frequently occurring terms and phrases. For example, it automatically suggests specific technical terms and phrases that the user has used in the past. Furthermore, when the user starts typing, the input analysis unit allows the generation AI to suggest appropriate terms and phrases in real time based on the user's past input history. For example, when entering meeting minutes, it automatically displays phrases used in past minutes. Furthermore, the input analysis unit provides a function where the generation AI learns the user's past input history and automatically completes frequently occurring terms and phrases. For example, if the user frequently uses a specific industry term, it automatically completes that term. This improves input efficiency by automatically completing frequently occurring terms and phrases based on the user's past input history.

[0051] The format conversion unit can automatically convert data into terminology or abbreviations specific to the user's industry. For example, the format conversion unit allows the generation AI to automatically recognize terminology and abbreviations specific to the user's industry and convert the analyzed content into those terms and abbreviations. For example, it automatically converts terms in the medical industry. The format conversion unit also provides a function to analyze content entered by the user and automatically convert it into industry-specific terminology and abbreviations. For example, it automatically expands abbreviations in the IT industry. The format conversion unit also allows the generation AI to learn terminology and abbreviations specific to the user's industry and automatically convert the analyzed content into those terms and abbreviations. For example, it automatically converts terminology in the legal industry. This allows the creation of industry-specific documents by automatically converting data into terminology and abbreviations specific to the user's industry.

[0052] The format conversion unit can use the emotion estimation function to analyze the emotional nuances of the content entered by the user and convert it into positive expressions. For example, the format conversion unit uses a generative AI to analyze the content entered by the user and identify the emotional nuances using the emotion estimation function. For example, it converts negative expressions into positive expressions. The format conversion unit also provides a function to analyze the emotional nuances of the content entered by the user and convert them into positive expressions. For example, it converts negative opinions into positive opinions. The format conversion unit also uses the emotion estimation function to build a system that analyzes the emotional nuances of the content entered by the user and converts them into positive expressions. For example, it converts critical comments into constructive comments. In this way, by converting the emotional nuances of the content entered by the user into positive expressions, the content of the document becomes more positive.

[0053] The format conversion unit can automatically translate the analyzed content into different languages ​​to support international document creation. For example, the format conversion unit automatically translates content analyzed by the generation AI into different languages ​​to support international document creation. For example, content entered in English is automatically translated into French or Chinese. The format conversion unit also analyzes content entered by users and provides a function to automatically translate it into different languages. For example, it creates meeting minutes in multiple languages. The format conversion unit also automatically translates into different languages ​​based on the content analyzed by the generation AI, building a system to support international document creation. For example, it creates project reports in multiple languages. This makes it easier to create international documents by automatically translating the analyzed content into different languages.

[0054] The input analysis unit can support voice input or handwritten input, thereby diversifying input methods. The input analysis unit, for example, provides a function that allows the generation AI to analyze voice input or handwritten input and convert it into text data. For example, creating meeting minutes using voice input. The input analysis unit also allows users to input content using voice input or handwritten input, and the generation AI analyzes that content and converts it into text data. For example, the input analysis unit automatically converts handwritten notes into text. The input analysis unit also builds a system in which the generation AI analyzes voice input or handwritten input and automatically transcribes it into different documents. For example, content entered via voice can be automatically transcribed into a meeting report or project progress report. This allows the system to support voice input or handwritten input, thereby diversifying input methods.

[0055] The input analysis unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and provide emotional feedback according to the input content. The input analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering input in real time and provide appropriate feedback. For example, if the user is feeling stressed, the input analysis unit may suggest relaxing. The input analysis unit also builds a system that analyzes the emotion of the user when entering input in real time and provides emotional feedback. For example, it may display an encouraging message to elicit positive emotions. The input analysis unit also uses the emotion estimation function to analyze the user's emotion and provide emotional feedback according to the input content. For example, it may make positive suggestions to a user who is feeling negative emotions. In this way, the user's input experience is improved by analyzing the emotion of the user when entering input in real time and providing emotional feedback.

[0056] The transcription unit can automatically generate the optimal layout for each document format. For example, the generation AI automatically generates the optimal layout for each document format and transcribes the content. For example, it automatically adjusts the layout of a meeting report and a project progress report. The transcription unit also analyzes the content entered by the user and provides a function to automatically generate a layout appropriate for each document format. For example, it adjusts the layout to suit different document formats. The transcription unit also builds a system in which the generation AI generates the optimal layout for each document format and transcribes the content when automatically transcribing. For example, it automatically transcribes meeting minutes into different document formats. This improves the appearance of documents by automatically generating the optimal layout for each document format.

[0057] The transcription unit can use a common template to maintain consistency between documents. For example, when the generation AI automatically transcribes, the transcription unit uses a common template to maintain consistency between documents. For example, the same template is used for meeting reports and project status reports. The transcription unit also analyzes the content entered by the user and provides a function to automatically transcribe it into multiple documents using a common template. For example, a common template is applied to different document formats. The transcription unit also builds a system in which a common template is used to maintain consistency between documents when the generation AI automatically transcribes. For example, meeting minutes are transcribed into different document formats using a common template. This improves the sense of unity of the documents by using a common template to maintain consistency between documents.

[0058] The transcription unit can use the emotion estimation function to adjust the content of the transcribed document so that it evokes positive emotions in the user. The transcription unit, for example, uses the emotion estimation function to adjust the content of the transcribed document so that it evokes positive emotions in the user. For example, it creates a document using positive expressions. The transcription unit also uses the emotion estimation function to adjust the content of the transcribed document based on the content analyzed by the generation AI. For example, it changes the expression of the document so that the user evokes positive emotions. The transcription unit also uses the emotion estimation function to build a system that adjusts the content of the transcribed document so that it evokes positive emotions in the user. For example, it uses expressions that elicit positive emotions. In this way, the content of the transcribed document is adjusted so that it evokes positive emotions in the user, thereby improving user satisfaction.

[0059] The transcription unit can provide customizable templates for different industries or applications. For example, the generation AI provides customizable templates for different industries or applications and automatically transcribes them. For example, templates specialized for the medical or IT industry are used. The transcription unit also provides a function to analyze content entered by the user and provide customizable templates for different industries or applications. For example, templates specialized for the legal or education industry are used. The transcription unit also builds a system that provides customizable templates for different industries or applications when the generation AI automatically transcribes. For example, templates specialized for the marketing or manufacturing industry are used. This makes it possible to meet a wide range of needs by providing customizable templates for different industries and applications.

[0060] The transcription unit can visualize the contents of a document and automatically generate graphs or charts. For example, when the generation AI automatically transcribes, the transcription unit visualizes the contents of the document and automatically generates graphs and charts. For example, it creates a graph showing progress based on meeting minutes. The transcription unit also analyzes the content entered by the user and provides a function to visualize the contents of the document. For example, it creates a chart showing results based on a project report. The transcription unit also builds a system that visualizes the contents of the document and automatically generates graphs and charts when the generation AI automatically transcribes. For example, it visually displays data based on a meeting report. In this way, visualizing the contents of the document and automatically generating graphs and charts makes it easier to visually understand the information.

[0061] The transcription unit can use the emotion estimation function to dynamically change the content of the transcribed document according to the user's emotions. The transcription unit, for example, uses the emotion estimation function to dynamically change the content of the transcribed document according to the user's emotions. For example, if the user has positive emotions, the transcription unit changes the content of the document to a more positive expression. The transcription unit also provides a function to dynamically change the content of the transcribed document using the emotion estimation function based on the content analyzed by the generation AI. For example, the transcription unit adjusts the tone of the document according to the user's emotions. The transcription unit also uses the emotion estimation function to build a system in which the content of the transcribed document dynamically changes according to the user's emotions. For example, the transcription unit flexibly changes the content of the document according to the user's emotions. This allows the content of the transcribed document to dynamically change according to the user's emotions, making it possible to create documents that match the user's emotions.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] When analyzing the user's input, the input analyzer can refer to the user's past input history and automatically complete frequently occurring terms and phrases. For example, it can automatically suggest specific technical terms and phrases that the user has used in the past. The input analyzer can also suggest appropriate terms and phrases in real time based on the user's past input history when the user starts input. For example, when entering meeting minutes, it can automatically display phrases used in past minutes. The input analyzer can also learn the user's past input history and provide a function to automatically complete frequently occurring terms and phrases. For example, if the user frequently uses a specific industry term, it can automatically complete that term. This improves input efficiency by automatically completing frequently occurring terms and phrases based on the user's past input history.

[0064] The format conversion unit can automatically recognize technical terms and abbreviations specific to the user's industry and convert the analyzed content into those terms and abbreviations. For example, it can automatically convert terms in the medical industry. The format conversion unit can also provide a function that analyzes content entered by the user and automatically converts it into industry-specific technical terms and abbreviations. For example, it can automatically expand abbreviations in the IT industry. The format conversion unit can also learn technical terms and abbreviations specific to the user's industry and automatically convert the analyzed content into those terms and abbreviations. For example, it can automatically convert technical terms in the legal industry. This allows for the creation of industry-specific documents by automatically converting to technical terms and abbreviations specific to the user's industry.

[0065] The format conversion unit can use the emotion estimation function to analyze the emotional nuances of the content entered by the user and convert them into positive expressions. For example, it converts negative expressions into positive expressions. The format conversion unit can also provide a function to analyze the emotional nuances of the content entered by the user and convert them into positive expressions. For example, it converts negative opinions into positive opinions. The format conversion unit can also use the emotion estimation function to build a system that analyzes the emotional nuances of the content entered by the user and converts them into positive expressions. For example, it converts critical comments into constructive comments. In this way, by converting the emotional nuances of the content entered by the user into positive expressions, the content of the document becomes more positive.

[0066] The format conversion unit can automatically translate the analyzed content into different languages ​​to support international document creation. For example, content entered in English can be automatically translated into French or Chinese. The format conversion unit can also provide a function for analyzing content entered by a user and automatically translating it into different languages. For example, it can create meeting minutes in multiple languages. The format conversion unit can also automatically translate the analyzed content into different languages ​​to build a system that supports international document creation. For example, it can create project reports in multiple languages. This makes it easier to create international documents by automatically translating the analyzed content into different languages.

[0067] The input analysis unit can support voice input or handwritten input, thereby diversifying input methods. For example, the generation AI can provide a function to analyze voice input or handwritten input and convert it into text data. For example, meeting minutes can be created using voice input. The input analysis unit can also allow users to input content using voice input or handwritten input, and the generation AI can analyze that content and convert it into text data. For example, it can automatically convert handwritten notes into text. The input analysis unit can also build a system that analyzes voice input or handwritten input and automatically transcribes it into different documents. For example, content entered via voice can be automatically transcribed into a meeting report or project progress report. This allows for the diversification of input methods by supporting voice input or handwritten input.

[0068] The input analysis unit can use the emotion estimation function to analyze the emotions of a user when entering text in real time and provide emotional feedback according to the input content. For example, if the user is feeling stressed, the input analysis unit can make a suggestion to relax. The input analysis unit can also build a system that analyzes the emotions of a user when entering text in real time and provides emotional feedback. For example, it can display an encouraging message to elicit positive emotions. The input analysis unit can also use the emotion estimation function to analyze the emotions of a user and provide emotional feedback according to the input content. For example, it can make a positive suggestion to a user who is feeling negative emotions. In this way, the user's input experience is improved by analyzing the emotions of a user when entering text in real time and providing emotional feedback.

[0069] The transcription unit can automatically generate the optimal layout for each document format. For example, the generation AI can automatically generate the optimal layout for each document format and transcribe the content. For example, it can automatically adjust the layout of a meeting report and a project progress report. The transcription unit can also provide a function that analyzes the content entered by the user and automatically generates a layout appropriate for each document format. For example, it can adjust the layout to suit different document formats. The transcription unit can also build a system in which the generation AI automatically transcribes the content by generating the optimal layout for each document format. For example, it can automatically transcribe meeting minutes into different document formats. This improves the appearance of documents by automatically generating the optimal layout for each document format.

[0070] The transcription unit can use a common template to maintain consistency between documents. For example, when the generation AI automatically transcribes, a common template can be used to maintain consistency between documents. For example, the same template can be used for meeting reports and project status reports. The transcription unit can also analyze the content entered by the user and provide a function to automatically transcribe it into multiple documents using a common template. For example, a common template can be applied to different document formats. The transcription unit can also build a system in which a common template is used to maintain consistency between documents when the generation AI automatically transcribes. For example, a common template can be used to transcribe meeting minutes into different document formats. This improves the sense of unity of documents by using a common template to maintain consistency between documents.

[0071] The transcription unit can use the emotion estimation function to adjust the content of the transcribed document so that it evokes positive emotions in the user. For example, the emotion estimation function can be used to adjust the content of the transcribed document so that it evokes positive emotions in the user. For example, the document can be created using positive expressions. The transcription unit can also use the emotion estimation function to adjust the content of the transcribed document based on the content analyzed by the generation AI. For example, the expression of the document can be changed so that the user evokes positive emotions. The transcription unit can also use the emotion estimation function to build a system that adjusts the content of the transcribed document so that it evokes positive emotions in the user. For example, the expression is used to elicit positive emotions. In this way, the content of the transcribed document can be adjusted so that it evokes positive emotions in the user, thereby improving user satisfaction.

[0072] The transcription unit can provide customizable templates for different industries or applications. For example, the generation AI can provide customizable templates for different industries or applications and automatically transcribe them. For example, templates specialized for the medical or IT industry can be used. The transcription unit can also provide a function to analyze content entered by the user and provide customizable templates for different industries or applications. For example, templates specialized for the legal or education industry can be used. The transcription unit can also build a system that provides customizable templates for different industries or applications when the generation AI automatically transcribes. For example, templates specialized for the marketing or manufacturing industry can be used. This makes it possible to meet a wide range of needs by providing customizable templates for different industries and applications.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The input analysis unit analyzes the content entered by the user. For example, the input analysis unit uses a generation AI to analyze the text entered by the user and understand its content. The input analysis unit can also analyze the input content using natural language processing technology. For example, the generation AI analyzes the minutes of a meeting entered by the user and extracts each item in the minutes (date and time, attendees, agenda, content of the meeting, etc.). Step 2: The format conversion unit converts the input content analyzed by the input analysis unit into a format appropriate for each document. For example, the format conversion unit uses the generation AI to convert the analyzed content into PDF or Word format. The format conversion unit can also convert into Excel or presentation format. For example, the generation AI converts the analyzed content into a different document format, such as a meeting report or project progress report. Step 3: The transcription unit automatically transcribes the content converted by the format conversion unit into multiple documents. For example, the transcription unit automatically transcribes the content analyzed using the generation AI into a meeting report or a project progress report. The transcription unit can also automatically transcribe the content analyzed using the generation AI into different document formats. For example, the generation AI automatically transcribes meeting minutes into a meeting report or a project progress report based on the analyzed content.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 AI 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 AI 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, a 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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 AI 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0141] 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]

[0142] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input analysis unit that analyzes the content input by the user; a format conversion unit that converts the input content analyzed by the input analysis unit into a format suitable for each document format; a transcription unit that automatically transcribes the content converted by the format conversion unit into multiple documents. A system characterized by:

2. The input analysis unit Referencing the user's past input history to automatically complete frequently occurring terms or phrases 2. The system of claim 1.

3. The format conversion unit Automatically converting to jargon or abbreviations specific to the user's industry 2. The system of claim 1.

4. The format conversion unit Analyzes the emotional nuances of user input and converts them into positive expressions 2. The system of claim 1.

5. The format conversion unit Automatically translates analyzed content into different languages ​​to support international document creation 2. The system of claim 1.

Images