System

Abstract

An object of the system according to the embodiment is to automatically convert an in-house explanatory material into a customer explanatory material.SOLUTION: A system includes a material input part, a customer information input part, an image input part, a material conversion part, and a material output part. The material input unit inputs an in-house explanatory material. The customer information input unit inputs customer information. The image input unit inputs the converted image. The material conversion unit automatically converts the material on the basis of the in-house explanatory material input by the material input unit, the customer information input by the customer information input unit, and the converted image input by the image input unit. The material output unit outputs the material converted by the material conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that it took a lot of time and effort to convert internal explanation materials into materials for customer explanations.

[0005] The system according to the embodiment aims to automatically convert materials for internal explanations into materials for customer explanations. [Means for solving the problem]

[0006] The system according to the embodiment includes a material input unit, a customer information input unit, an image input unit, a material conversion unit, and a material output unit. The material input unit inputs in-house explanatory materials. The customer information input unit inputs customer information. The image input unit inputs the converted image. The material conversion unit automatically converts the materials based on the in-house explanatory materials input by the material input unit, the customer information input by the customer information input unit, and the converted image input by the image input unit. The material output unit outputs the materials converted by the material conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically convert internal explanation materials into materials suitable for customer explanations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The material conversion system according to an embodiment of the present invention is a system that automatically converts internal presentation materials into materials for customer presentations. This system inputs the internal presentation materials created, customer information, and the converted image, and outputs presentation materials that are easy to view for the customer. As a result, the material conversion system automatically converts internal presentation materials into materials for customer presentations, enabling efficient proposal activities.

[0029] The document conversion system according to the embodiment includes a document input unit, a customer information input unit, an image input unit, a document conversion unit, and a document output unit. The document input unit inputs internal presentation materials. For example, presentation materials and reports can be uploaded to the system. The document input unit can also directly import digital documents. The customer information input unit inputs customer information. For example, attributes such as company name, industry, job title, and local government can be input. The customer information input unit can also reference past transaction history and feedback. The image input unit inputs a converted image. For example, information such as the document format, design, and font size can be specified. The image input unit can also input the user's desired document layout and color usage. The document conversion unit automatically converts the document based on the internal presentation materials input by the document input unit, the customer information input by the customer information input unit, and the converted image input by the image input unit. For example, the generation AI replaces technical terms with easier-to-understand language and changes the order of explanations. In addition, when creating materials for local governments, the generation AI creates them using simpler explanations and larger fonts. The material output unit outputs the materials converted by the material conversion unit. For example, the converted materials can be output in PDF format or in a format that can be opened in presentation software. The material output unit can also print the converted materials. This allows the material conversion system to automatically convert internal explanatory materials into materials for customer explanations, enabling efficient proposal activities. For example, by inputting internal explanatory materials containing technical details and outputting simple explanatory materials for local governments, proposal activities can be carried out quickly. In addition, for materials for companies, easy-to-understand explanations that avoid technical jargon can deepen customer understanding.

[0030] The document conversion unit can automatically summarize the contents of internal briefing materials and extract key points. For example, when internal briefing materials are input into the system, the generation AI automatically analyzes the content and generates a summary. For example, it concisely summarizes technical details and market analysis results and extracts key points. The document conversion unit also allows the generation AI to set summary criteria and prioritize the extraction of important information. For example, it summarizes based on frequently occurring keywords and important data points. This makes it possible to automatically extract key points from internal briefing materials.

[0031] The document conversion unit analyzes the logical structure of internal briefing materials, automatically detecting contradictions and inconsistencies and making suggestions for revisions. For example, when an internal briefing material is input into the system, the document conversion unit's generation AI analyzes the logical structure and detects contradictions and inconsistencies. For example, if the premise and conclusion do not match, the generation AI will make suggestions for revisions. The document conversion unit also allows the generation AI to perform analysis based on a logical framework and evaluate logical consistency. For example, it detects contradictions based on cause and effect relationships and logical flow. This makes it possible to automatically detect logical contradictions and inconsistencies in internal briefing materials and make suggestions for revisions.

[0032] The document conversion unit can automatically translate the contents of documents into different languages ​​and generate multilingual documents. For example, when an internal explanation document is input into the system, the generation AI automatically analyzes the content and translates it into a different language. For example, it generates documents in multiple languages, such as English, French, and Chinese. The document conversion unit also allows the generation AI to evaluate the accuracy of the translation and perform appropriate translations. For example, it translates technical terms and industry jargon. This allows the content of documents to be automatically translated into different languages ​​and multilingual documents to be generated.

[0033] The document conversion unit can input the contents of documents as audio data and convert them to text using voice recognition technology. For example, the document conversion unit inputs internal explanation materials into the system as audio data, and the generation AI converts them to text using voice recognition technology. For example, the audio of a presentation is converted to text. The document conversion unit also allows the generation AI to analyze the format of the audio data and convert it to text appropriately. For example, it converts it to text based on the audio file format and recording method. This allows the contents of documents to be input as audio data and converted to text using voice recognition technology.

[0034] The document conversion unit can automatically generate optimal proposal content by referencing past transaction history and feedback based on customer information. For example, when customer information is entered into the system, the document conversion unit's generation AI analyzes past transaction history and feedback and automatically generates optimal proposal content. For example, proposal content is created based on past success stories. The document conversion unit also allows the generation AI to evaluate the transaction history and feedback content and make appropriate proposals. For example, proposal content is created based on customer opinions and evaluation comments. This makes it possible to automatically generate optimal proposal content by referencing past transaction history and feedback based on customer information.

[0035] The document conversion unit can refer to the latest industry trends and competitive information and reflect this in proposal materials. For example, when customer information is entered into the system, the document conversion unit's generation AI analyzes the latest industry trends and competitive information and reflects this in proposal materials. For example, it incorporates the latest technological trends and market needs into proposal materials. The document conversion unit also allows the generation AI to perform analysis based on industry reports and market research. For example, it evaluates competitors' product information and marketing strategies. This allows the latest industry trends and competitive information to be referenced and reflected in proposal materials.

[0036] The material conversion unit can refer to social media and public data to collect the latest information and reflect it in proposal materials. For example, when customer information is entered into the material conversion unit, the generation AI analyzes social media and public data to collect the latest information and reflect it in proposal materials. For example, the latest market trends and customer feedback are incorporated into proposal materials. The material conversion unit also allows the generation AI to perform analysis based on social media platforms and types of public data. For example, data from Twitter and Facebook is collected and reflected in proposal materials. This allows social media and public data to be referenced to collect the latest information and reflect it in proposal materials.

[0037] The document conversion unit can refer to best practices from different industries and apply them to proposal materials. For example, when customer information is entered into the system, the document conversion unit's generation AI analyzes best practices from different industries and applies them to proposal materials. For example, the proposal content can be created based on success stories from other industries. The document conversion unit also allows the generation AI to perform analysis based on industry standards and success stories. For example, best practices from the manufacturing and service industries can be reflected in proposal materials. This makes it possible to refer to best practices from different industries and apply them to proposal materials.

[0038] The document conversion unit can refer to past success stories and propose the optimal format and design. For example, when the converted image is input into the system, the generation AI analyzes past success stories and proposes the optimal format and design. For example, the proposal content is created based on the design of a successful presentation. The document conversion unit also allows the generation AI to evaluate the content of successful stories and propose an appropriate format and design. For example, the proposal content is created based on the success stories of past projects. This makes it possible to refer to past success stories and propose the optimal format and design.

[0039] The material conversion unit can automatically optimize visual elements. For example, when the material conversion unit inputs a converted image into the system, the generation AI analyzes the visual elements and automatically optimizes them. For example, it adjusts color contrast and font readability. The material conversion unit also allows the generation AI to perform analysis based on the type of visual element and the optimization method. For example, it optimizes visual elements such as color, font, and layout. This allows the visual elements to be automatically optimized.

[0040] The data conversion unit can automatically generate designs optimized for different devices. For example, when the data conversion unit inputs the converted image into the system, the generation AI automatically generates designs optimized for different devices. For example, it creates a portrait layout for smartphones and a landscape layout for PCs. The data conversion unit also allows the generation AI to perform analysis based on the type of device and optimization method. For example, it automatically generates designs optimized for devices such as smartphones, tablets, and PCs. This makes it possible to automatically generate designs optimized for different devices.

[0041] The material conversion unit can add interactive elements to enhance visual appeal. For example, when the material conversion unit inputs the converted image into the system, the generation AI adds interactive elements to enhance visual appeal. For example, animations and videos can be incorporated into the material. The material conversion unit can also perform analysis based on the type of interactive element and how it is added by the generation AI. For example, elements such as animations, videos, and interactive graphics can be added. This allows interactive elements to be added to enhance visual appeal.

[0042] The material conversion unit can automatically replace technical terms with simpler language to generate materials that are easier to understand. For example, when materials are input into the system, the material conversion unit's generation AI analyzes the technical terms and replaces them with simpler language. For example, it can explain "technical details" as "technical features." The material conversion unit can also perform analysis based on the type of technical terminology and the replacement method. For example, it can replace technical terms and industry jargon with general language. This allows the system to automatically replace technical terms with simpler language and generate materials that are easier to understand.

[0043] The material conversion unit can reconstruct the content of the material and optimize the logical flow. For example, when material is input into the system, the material conversion unit has the generation AI analyze the content and optimize the logical flow. For example, it can change the order of explanations to make the structure easier to understand. The material conversion unit also allows the generation AI to perform analysis based on the reconstruction method and criteria. For example, it can change the order of information or reorganize the content. This allows the content of the material to be reconstructed and the logical flow to be optimized.

[0044] The document conversion unit can automatically generate customized documents for different industries and uses. For example, when documents are input into the system, the generation AI automatically generates customized documents for different industries and uses. For example, it creates documents that combine the technical field with the consumer market. The document conversion unit also enables the generation AI to perform analysis based on the definition of industry type and use. For example, it creates documents that are appropriate for industries such as IT, manufacturing, and services. This makes it possible to automatically generate customized documents for different industries and uses.

[0045] The data conversion unit can visualize the contents of the data and automatically generate graphs and charts. For example, when data is input into the system, the data conversion unit allows the generation AI to analyze and visualize the contents. For example, the data is converted into graphs and charts and displayed visually. The data conversion unit also allows the generation AI to perform analysis based on visualization methods and standards. For example, it automatically generates graphs and charts such as bar graphs, pie charts, and line graphs. This makes it possible to visualize the contents of the data and automatically generate graphs and charts.

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

[0047] The document conversion system can also include a feedback collection unit that collects user feedback in real time and reflects it in the content of the document. For example, questions and comments from participants can be collected in real time during a presentation, and the content of the document can be immediately revised based on that. The feedback collection unit can also link with online conference tools to automatically analyze participants' reactions and reflect them in the document. This allows the document conversion system to reflect feedback in real time and conduct more effective proposal activities.

[0048] The document conversion system may further include a visual enhancement unit for visually enhancing the content of the document. For example, the system may automatically change the color or font size to emphasize important points. The visual enhancement unit may also automatically generate graphs and charts to display them in a visually easy-to-understand manner. This allows the document conversion system to provide visually enhanced documents, thereby enhancing the understanding of the proposed content.

[0049] The document conversion system may further include an audio description unit that provides audio explanations of the contents of the document. For example, it may automatically generate audio narration for each slide of the document to complement the presentation. The audio description unit may also provide audio explanations in different languages. This allows the document conversion system to provide supplemental audio explanations and convey information both visually and aurally.

[0050] The material conversion system may further include an interactive element adding unit for making the content of the material interactive. For example, when a user clicks on a specific item in the material, detailed information is displayed. The interactive element adding unit may also incorporate quizzes or questionnaires into the material. This allows the material conversion system to encourage user participation and provide more interactive material.

[0051] The document conversion system can further include an automatic update unit for automatically updating the content of the documents. For example, the latest market data and technical information can be periodically collected and reflected in the documents. The automatic update unit can also automatically update the content of the documents based on conditions specified by the user. This allows the document conversion system to always provide documents that reflect the latest information.

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

[0053] Step 1: The document input section inputs materials for internal explanations. For example, presentation materials and reports can be uploaded to the system. Digital documents can also be imported directly. Step 2: The customer information input section allows you to input customer information. For example, you can enter attributes such as company name, industry, job title, and local government. You can also refer to past transaction history and feedback. Step 3: The image input section inputs the converted image. For example, you can specify information such as the format and design of the document, the font size to be used, etc. You can also input the layout and color usage of the document you want. Step 4: The material conversion unit automatically converts the materials based on the internal explanatory materials entered by the material input unit, the customer information entered by the customer information input unit, and the converted images entered by the image input unit. For example, the generation AI replaces technical terms with easier-to-understand language and changes the order of explanations. In addition, for materials intended for local governments, the AI ​​creates materials using simpler explanations and larger font. Step 5: The document output unit outputs the document converted by the document conversion unit. For example, the converted document can be output in PDF format or in a format that can be opened in presentation software. The converted document can also be printed.

[0054] (Example 2) The material conversion system according to an embodiment of the present invention is a system that automatically converts internal presentation materials into materials for customer presentations. This system inputs the internal presentation materials created, customer information, and the converted image, and outputs presentation materials that are easy to view for the customer. As a result, the material conversion system automatically converts internal presentation materials into materials for customer presentations, enabling efficient proposal activities.

[0055] The document conversion system according to the embodiment includes a document input unit, a customer information input unit, an image input unit, a document conversion unit, and a document output unit. The document input unit inputs internal presentation materials. For example, presentation materials and reports can be uploaded to the system. The document input unit can also directly import digital documents. The customer information input unit inputs customer information. For example, attributes such as company name, industry, job title, and local government can be input. The customer information input unit can also reference past transaction history and feedback. The image input unit inputs a converted image. For example, information such as the document format, design, and font size can be specified. The image input unit can also input the user's desired document layout and color usage. The document conversion unit automatically converts the document based on the internal presentation materials input by the document input unit, the customer information input by the customer information input unit, and the converted image input by the image input unit. For example, the generation AI replaces technical terms with easier-to-understand language and changes the order of explanations. In addition, when creating materials for local governments, the generation AI creates them using simpler explanations and larger fonts. The material output unit outputs the materials converted by the material conversion unit. For example, the converted materials can be output in PDF format or in a format that can be opened in presentation software. The material output unit can also print the converted materials. This allows the material conversion system to automatically convert internal explanatory materials into materials for customer explanations, enabling efficient proposal activities. For example, by inputting internal explanatory materials containing technical details and outputting simple explanatory materials for local governments, proposal activities can be carried out quickly. In addition, for materials for companies, easy-to-understand explanations that avoid technical jargon can deepen customer understanding.

[0056] The document conversion unit can automatically summarize the contents of internal briefing materials and extract key points. For example, when internal briefing materials are input into the system, the generation AI automatically analyzes the content and generates a summary. For example, it concisely summarizes technical details and market analysis results and extracts key points. The document conversion unit also allows the generation AI to set summary criteria and prioritize the extraction of important information. For example, it summarizes based on frequently occurring keywords and important data points. This makes it possible to automatically extract key points from internal briefing materials.

[0057] The document conversion unit analyzes the logical structure of internal briefing materials, automatically detecting contradictions and inconsistencies and making suggestions for revisions. For example, when an internal briefing material is input into the system, the document conversion unit's generation AI analyzes the logical structure and detects contradictions and inconsistencies. For example, if the premise and conclusion do not match, the generation AI will make suggestions for revisions. The document conversion unit also allows the generation AI to perform analysis based on a logical framework and evaluate logical consistency. For example, it detects contradictions based on cause and effect relationships and logical flow. This makes it possible to automatically detect logical contradictions and inconsistencies in internal briefing materials and make suggestions for revisions.

[0058] The material conversion unit can use the emotion estimation function to evaluate the emotional impact that the content of the material has on the user and make suggestions for revisions to elicit positive emotions. For example, when an internal company briefing material is input into the material conversion unit, the emotion estimation function analyzes the content of the material and evaluates the emotional impact that it has on the user. For example, suggestions for revisions to elicit positive emotions are made. The material conversion unit can also use an emotion analysis algorithm with the emotion estimation function to evaluate the content of the material. For example, it evaluates positive and negative emotions. This allows the emotional impact that the content of the material has on the user to be evaluated and suggestions for revisions to elicit positive emotions to be made.

[0059] The document conversion unit can automatically translate the contents of documents into different languages ​​and generate multilingual documents. For example, when an internal explanation document is input into the system, the generation AI automatically analyzes the content and translates it into a different language. For example, it generates documents in multiple languages, such as English, French, and Chinese. The document conversion unit also allows the generation AI to evaluate the accuracy of the translation and perform appropriate translations. For example, it translates technical terms and industry jargon. This allows the content of documents to be automatically translated into different languages ​​and multilingual documents to be generated.

[0060] The document conversion unit can input the contents of documents as audio data and convert them to text using voice recognition technology. For example, the document conversion unit inputs internal explanation materials into the system as audio data, and the generation AI converts them to text using voice recognition technology. For example, the audio of a presentation is converted to text. The document conversion unit also allows the generation AI to analyze the format of the audio data and convert it to text appropriately. For example, it converts it to text based on the audio file format and recording method. This allows the contents of documents to be input as audio data and converted to text using voice recognition technology.

[0061] The material conversion unit uses the emotion estimation function to evaluate how the content of the material will be received in different cultural spheres and can perform cultural adaptation. For example, when an internal company briefing material is input into the material conversion unit, the emotion estimation function analyzes the content of the material and evaluates how it will be received in different cultural spheres. For example, it proposes modifications that take cultural differences into consideration. The material conversion unit's emotion estimation function can also perform analysis based on the definitions and evaluation criteria of cultural spheres. For example, it evaluates the cultural background and cultural differences of each region. This allows it to evaluate how the content of the material will be received in different cultural spheres and perform cultural adaptation.

[0062] The document conversion unit can automatically generate optimal proposal content by referencing past transaction history and feedback based on customer information. For example, when customer information is entered into the system, the document conversion unit's generation AI analyzes past transaction history and feedback and automatically generates optimal proposal content. For example, proposal content is created based on past success stories. The document conversion unit also allows the generation AI to evaluate the transaction history and feedback content and make appropriate proposals. For example, proposal content is created based on customer opinions and evaluation comments. This makes it possible to automatically generate optimal proposal content by referencing past transaction history and feedback based on customer information.

[0063] The document conversion unit can refer to the latest industry trends and competitive information and reflect this in proposal materials. For example, when customer information is entered into the system, the document conversion unit's generation AI analyzes the latest industry trends and competitive information and reflects this in proposal materials. For example, it incorporates the latest technological trends and market needs into proposal materials. The document conversion unit also allows the generation AI to perform analysis based on industry reports and market research. For example, it evaluates competitors' product information and marketing strategies. This allows the latest industry trends and competitive information to be referenced and reflected in proposal materials.

[0064] The material conversion unit uses the emotion estimation function to analyze past reactions and feedback from customers and make suggestions to elicit a positive reaction. For example, when customer information is input into the system, the material conversion unit's emotion estimation function analyzes past reactions and feedback and makes suggestions to elicit a positive reaction. For example, the material conversion unit creates proposal content based on past success stories. The material conversion unit's emotion estimation function can also perform analysis based on customer satisfaction and favorable comments. For example, the material conversion unit creates proposal content to elicit positive emotions. This allows the material conversion unit to analyze past reactions and feedback from customers and make suggestions to elicit a positive reaction.

[0065] The material conversion unit can refer to social media and public data to collect the latest information and reflect it in proposal materials. For example, when customer information is entered into the material conversion unit, the generation AI analyzes social media and public data to collect the latest information and reflect it in proposal materials. For example, the latest market trends and customer feedback are incorporated into proposal materials. The material conversion unit also allows the generation AI to perform analysis based on social media platforms and types of public data. For example, data from Twitter and Facebook is collected and reflected in proposal materials. This allows social media and public data to be referenced to collect the latest information and reflect it in proposal materials.

[0066] The document conversion unit can refer to best practices from different industries and apply them to proposal materials. For example, when customer information is entered into the system, the document conversion unit's generation AI analyzes best practices from different industries and applies them to proposal materials. For example, the proposal content can be created based on success stories from other industries. The document conversion unit also allows the generation AI to perform analysis based on industry standards and success stories. For example, best practices from the manufacturing and service industries can be reflected in proposal materials. This makes it possible to refer to best practices from different industries and apply them to proposal materials.

[0067] The material conversion unit can use the emotion estimation function to analyze the emotional needs of customers and make customized proposals based on them. For example, when customer information is input into the system, the material conversion unit's emotion estimation function analyzes the emotional needs and makes customized proposals based on them. For example, it creates proposal content that elicits positive emotions. The material conversion unit's emotion estimation function can also perform analysis based on customer emotion analysis and needs research methods. For example, it evaluates the customer's emotional needs and creates proposal content based on them. This makes it possible to analyze the customer's emotional needs and make customized proposals based on them.

[0068] The document conversion unit can refer to past success stories and propose the optimal format and design. For example, when the converted image is input into the system, the generation AI analyzes past success stories and proposes the optimal format and design. For example, the proposal content is created based on the design of a successful presentation. The document conversion unit also allows the generation AI to evaluate the content of successful stories and propose an appropriate format and design. For example, the proposal content is created based on the success stories of past projects. This makes it possible to refer to past success stories and propose the optimal format and design.

[0069] The material conversion unit can automatically optimize visual elements. For example, when the material conversion unit inputs a converted image into the system, the generation AI analyzes the visual elements and automatically optimizes them. For example, it adjusts color contrast and font readability. The material conversion unit also allows the generation AI to perform analysis based on the type of visual element and the optimization method. For example, it optimizes visual elements such as color, font, and layout. This allows the visual elements to be automatically optimized.

[0070] The material conversion unit uses the emotion estimation function to evaluate the emotional impact that visual elements have on the user and make design suggestions to elicit positive emotions. For example, when the material conversion unit inputs the converted image into the system, the emotion estimation function analyzes the visual elements and evaluates the emotional impact that they have on the user. For example, it proposes color usage and layout that will elicit positive emotions. The material conversion unit can also use the emotion estimation function to perform analysis based on the type of visual element and optimization method. For example, it evaluates visual elements such as color, font, and layout and makes design suggestions to elicit positive emotions. This makes it possible to evaluate the emotional impact that visual elements have on the user and make design suggestions to elicit positive emotions.

[0071] The data conversion unit can automatically generate designs optimized for different devices. For example, when the data conversion unit inputs the converted image into the system, the generation AI automatically generates designs optimized for different devices. For example, it creates a portrait layout for smartphones and a landscape layout for PCs. The data conversion unit also allows the generation AI to perform analysis based on the type of device and optimization method. For example, it automatically generates designs optimized for devices such as smartphones, tablets, and PCs. This makes it possible to automatically generate designs optimized for different devices.

[0072] The material conversion unit can add interactive elements to enhance visual appeal. For example, when the material conversion unit inputs the converted image into the system, the generation AI adds interactive elements to enhance visual appeal. For example, animations and videos can be incorporated into the material. The material conversion unit can also perform analysis based on the type of interactive element and how it is added by the generation AI. For example, elements such as animations, videos, and interactive graphics can be added. This allows interactive elements to be added to enhance visual appeal.

[0073] The material conversion unit can use the emotion estimation function to propose optimal designs for different target groups. For example, when the material conversion unit inputs the converted image into the system, the emotion estimation function analyzes the target group and proposes the optimal design. For example, it proposes font size and color usage according to the age group. In addition, the emotion estimation function of the material conversion unit can perform analysis based on the definition and evaluation criteria of the target group. For example, it proposes designs according to target groups such as age group, gender, and region. This makes it possible to propose optimal designs for different target groups.

[0074] The material conversion unit can automatically replace technical terms with simpler language to generate materials that are easier to understand. For example, when materials are input into the system, the material conversion unit's generation AI analyzes the technical terms and replaces them with simpler language. For example, it can explain "technical details" as "technical features." The material conversion unit can also perform analysis based on the type of technical terminology and the replacement method. For example, it can replace technical terms and industry jargon with general language. This allows the system to automatically replace technical terms with simpler language and generate materials that are easier to understand.

[0075] The material conversion unit can reconstruct the content of the material and optimize the logical flow. For example, when material is input into the system, the material conversion unit has the generation AI analyze the content and optimize the logical flow. For example, it can change the order of explanations to make the structure easier to understand. The material conversion unit also allows the generation AI to perform analysis based on the reconstruction method and criteria. For example, it can change the order of information or reorganize the content. This allows the content of the material to be reconstructed and the logical flow to be optimized.

[0076] The material conversion unit can use the emotion estimation function to evaluate the emotional impact that the content of the material has on the user and make modifications to elicit positive emotions. For example, when the material is input into the system, the material conversion unit uses the emotion estimation function to analyze the content and evaluate the emotional impact that the content has on the user. For example, it makes suggestions for modifications to elicit positive emotions. Furthermore, the material conversion unit can use the emotion estimation function to evaluate the content of the material using an emotion analysis algorithm. For example, it evaluates positive and negative emotions. This allows the emotional impact that the content of the material has on the user to be evaluated and modifications to elicit positive emotions to be made.

[0077] The document conversion unit can automatically generate customized documents for different industries and uses. For example, when documents are input into the system, the generation AI automatically generates customized documents for different industries and uses. For example, it creates documents that combine the technical field with the consumer market. The document conversion unit also enables the generation AI to perform analysis based on the definition of industry type and use. For example, it creates documents that are appropriate for industries such as IT, manufacturing, and services. This makes it possible to automatically generate customized documents for different industries and uses.

[0078] The data conversion unit can visualize the contents of the data and automatically generate graphs and charts. For example, when data is input into the system, the data conversion unit allows the generation AI to analyze and visualize the contents. For example, the data is converted into graphs and charts and displayed visually. The data conversion unit also allows the generation AI to perform analysis based on visualization methods and standards. For example, it automatically generates graphs and charts such as bar graphs, pie charts, and line graphs. This makes it possible to visualize the contents of the data and automatically generate graphs and charts.

[0079] The material conversion unit uses the emotion estimation function to evaluate how the content of the material will be received in different cultural spheres and can perform cultural adaptation. For example, when the material is input into the system, the emotion estimation function analyzes the content and evaluates how it will be received in different cultural spheres. For example, it makes suggestions for revisions that take cultural differences into consideration. The emotion estimation function can also perform analysis based on definitions and evaluation criteria of cultural spheres. For example, it evaluates the cultural background and cultural differences of each region. This makes it possible to evaluate how the content of the material will be received in different cultural spheres and perform cultural adaptation.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The document conversion system can also include a feedback collection unit that collects user feedback in real time and reflects it in the content of the document. For example, questions and comments from participants can be collected in real time during a presentation, and the content of the document can be immediately revised based on that. The feedback collection unit can also link with online conference tools to automatically analyze participants' reactions and reflect them in the document. This allows the document conversion system to reflect feedback in real time and conduct more effective proposal activities.

[0082] The document conversion system may further include a visual enhancement unit for visually enhancing the content of the document. For example, the system may automatically change the color or font size to emphasize important points. The visual enhancement unit may also automatically generate graphs and charts to display them in a visually easy-to-understand manner. This allows the document conversion system to provide visually enhanced documents, thereby enhancing the understanding of the proposed content.

[0083] The document conversion system may further include an audio description unit that provides audio explanations of the contents of the document. For example, it may automatically generate audio narration for each slide of the document to complement the presentation. The audio description unit may also provide audio explanations in different languages. This allows the document conversion system to provide supplemental audio explanations and convey information both visually and aurally.

[0084] The material conversion system may further include an interactive element adding unit for making the content of the material interactive. For example, when a user clicks on a specific item in the material, detailed information is displayed. The interactive element adding unit may also incorporate quizzes or questionnaires into the material. This allows the material conversion system to encourage user participation and provide more interactive material.

[0085] The document conversion system can further include an automatic update unit for automatically updating the content of the documents. For example, the latest market data and technical information can be periodically collected and reflected in the documents. The automatic update unit can also automatically update the content of the documents based on conditions specified by the user. This allows the document conversion system to always provide documents that reflect the latest information.

[0086] The document conversion system can further use an emotion estimation function to make customized suggestions based on the user's emotions. For example, it can analyze the user's facial expressions and tone of voice while viewing documents and make suggestions to elicit positive emotions. The emotion estimation function can also generate optimal suggestions based on the user's past responses. This allows the document conversion system to make customized suggestions based on the user's emotions and perform more effective suggestion activities.

[0087] The material conversion system can further use emotion estimation to evaluate how the content of the material will be received in different cultures and make cultural adaptations. For example, if the content of the material is likely to evoke negative emotions in a particular culture, it can make suggestions for revisions. The emotion estimation function can also generate optimal suggestions based on the emotional responses of different cultures. This allows the material conversion system to provide appropriate materials for different cultures.

[0088] The material conversion system can further use the emotion estimation function to evaluate the emotional impact of the material content on the user and make suggestions for revisions to elicit positive emotions. For example, if the material content is likely to cause stress to the user, the system can make suggestions for revisions. The emotion estimation function can also generate optimal suggestions based on the user's emotional response. This allows the material conversion system to provide materials that take the user's emotions into consideration.

[0089] The material conversion system can further use an emotion estimation function to analyze the user's emotional needs and make customized suggestions based on them. For example, the emotion estimation function can analyze the user's emotions while viewing materials and generate suggestions that elicit positive emotions. The emotion estimation function can also generate optimal suggestions based on the user's past emotional responses. This allows the material conversion system to make customized suggestions that meet the user's emotional needs.

[0090] The material conversion system can further use the emotion estimation function to evaluate the emotional impact of the material content on the user and make modifications to elicit positive emotions. For example, if the material content is likely to evoke negative emotions in the user, the system can make suggestions for modifications. The emotion estimation function can also generate optimal suggestions based on the user's emotional response. This allows the material conversion system to provide materials that take the user's emotions into consideration.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The document input section inputs materials for internal explanations. For example, presentation materials and reports can be uploaded to the system. Digital documents can also be imported directly. Step 2: The customer information input section allows you to input customer information. For example, you can enter attributes such as company name, industry, job title, and local government. You can also refer to past transaction history and feedback. Step 3: The image input section inputs the converted image. For example, you can specify information such as the format and design of the document, the font size to be used, etc. You can also input the layout and color usage of the document you want. Step 4: The material conversion unit automatically converts the materials based on the internal explanatory materials entered by the material input unit, the customer information entered by the customer information input unit, and the converted images entered by the image input unit. For example, the generation AI replaces technical terms with easier-to-understand language and changes the order of explanations. In addition, for materials intended for local governments, the AI ​​creates materials using simpler explanations and larger font. Step 5: The document output unit outputs the document converted by the document conversion unit. For example, the converted document can be output in PDF format or in a format that can be opened in presentation software. The converted document can also be printed.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a material input section for inputting materials for internal explanations; a customer information input section for inputting customer information; an image input unit for inputting the converted image; a material conversion unit that automatically converts the materials based on the in-house explanatory materials input by the material input unit, the customer information input by the customer information input unit, and the converted image input by the image input unit; a material output unit that outputs the material converted by the material conversion unit. A system characterized by:

2. The data conversion section Analyzes the logical structure of internal explanatory materials, automatically detects contradictions and inconsistencies, and suggests corrections 2. The system of claim 1.

3. The data conversion section Automatically translate document content into different languages ​​to generate multilingual documents 2. The system of claim 1.

4. The data conversion section Based on customer information, past transaction history and feedback are referenced to automatically generate optimal proposals.

2. The system of claim 1.

5. The data conversion section Referencing past success stories and proposing optimal formats and designs 2. The system of claim 1.

6. The data conversion section Evaluate the emotional impact of the content of the material on users and suggest modifications to elicit positive emotions 2. The system of claim 1.

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