System

Abstract

An object of the system according to the embodiment is to automate and efficiently perform creation, review, and improvement of materials.SOLUTION: A system includes a material generation part, a wall part, an brush-up part, and a chat providing part. The material generation unit automatically generates a material required by the user by using the generation AI. The walling unit automatically performs walling on the material generated by the material generation unit. A brush-up part automatically performs the brush-up of the data based on the improvement point pointed out by the wall hitting part. The chat providing unit provides a service to a user in a chat format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem that creating, reviewing, and improving documents required a lot of time and effort.

[0005] The system according to the embodiment aims to automate and efficiently create, review, and improve materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a material generation unit, a bouncing-back unit, a brush-up unit, and a chat providing unit. The material generation unit automatically generates materials required by the user using a generation AI. The bouncing-back unit automatically bouncing-back on the materials generated by the material generation unit. The brush-up unit automatically brushes up the materials based on the points for improvement pointed out by the bouncing-back unit. The chat providing unit provides services to users in the form of chat. [Effects of the Invention]

[0007] The system according to the embodiment can automate and efficiently create, review, and improve materials. [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 document creation system according to an embodiment of the present invention utilizes the knowledge of people around the world and similar projects and business documents accumulated in the past to automatically create new documents and provide automatic feedback and refinement in a chat format before the approver reviews. This allows the document creation system to efficiently create high-quality documents.

[0029] A document generation system according to an embodiment includes a document generation unit, a feedback unit, a brush-up unit, and a chat provision unit. The document generation unit automatically generates documents required by a user using a generation AI. For example, the generation AI analyzes the knowledge of predecessors around the world and similar project and business documents accumulated in the past to automatically generate new documents required by the user. The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates the documents based on the prompt. The feedback unit automatically provides feedback on the documents generated by the document generation unit. For example, the generation AI automatically provides feedback on the generated documents, reviews the contents of the documents, and points out areas for improvement or deficiencies. The brush-up unit automatically brushes up the documents based on the improvements pointed out by the feedback unit. For example, the generation AI automatically brushes up the documents based on the results of the feedback, improving the content of the documents and making them more complete. The chat provision unit provides services to users via chat. For example, a user can issue instructions to the generation AI via chat and check the generated documents, the results of the feedback, and the brush-up content in real time. This allows the material generation system to enable the user to efficiently create high-quality materials.

[0030] The document generation unit can automatically incorporate risk avoidance measures by referring to past success stories and failure stories. For example, the generation AI in the document generation unit analyzes past success stories and failure stories and automatically incorporates risk avoidance measures into the documents it generates. For example, in a business proposal, it adds risk management items learned from past failure stories. The generation AI also refers to failure stories and describes specific risk avoidance measures in the documents it generates. For example, in an RFP, it proposes risk avoidance measures based on problems that occurred in past projects. The generation AI also compares success stories and failure stories and incorporates the most appropriate risk avoidance measures into the documents it generates. For example, in a project plan, it reflects lessons learned from past failure stories. In this way, the reliability of the documents is improved by automatically incorporating risk avoidance measures.

[0031] The document generation unit can refer to the user's past project data and customize the documents to suit the user's specific needs and style. For example, the generation AI analyzes the user's past project data and customizes the documents it generates to suit the user's specific needs and style. For example, it reflects the specific format used in the user's past projects in a business flow diagram. The generation AI also refers to the user's past project data and reflects the user's preferences and style in the documents it generates. For example, it uses specific terms and expressions that the user used in the past in a requirements specification document. The generation AI also customizes the documents it generates based on the user's past project data. For example, it reflects the specific procedures and methods used in the user's past projects in a manual. This makes it possible to generate documents that suit the user's specific needs and style.

[0032] The bouncing back and forth unit can analyze the logical structure of a document and point out logical contradictions and inconsistencies. For example, the generation AI analyzes the logical structure of a document and points out logical contradictions and inconsistencies. For example, in a business proposal, if there is a contradiction between the goal setting and the execution plan, it will point this out. The generation AI also analyzes the content of the document and identifies logical inconsistencies. For example, in an RFP, if there is a contradiction between the requirements and the proposal content, it will point this out. The generation AI also analyzes the logical structure of a document and points out logical inconsistencies. For example, in a project plan, if there is an inconsistency between risk management and resource allocation, it will point this out. In this way, pointing out logical contradictions and inconsistencies in the document improves the quality of the document.

[0033] The back-and-forth unit can cross-reference the contents of a document with other related documents to check for consistency. For example, the generation AI cross-references the contents of a document with other related documents to check for consistency. For example, it compares a business proposal with past project reports to check for consistency. The generation AI also compares the contents of a document with other related documents to check for consistency. For example, it compares an RFP with past proposals to check for consistency in requirements. The generation AI also cross-references the contents of a document with other related documents to check for consistency. For example, it compares a project plan with past project plans to check for consistency in resource allocation. By checking the consistency of the document in this way, the reliability of the document is improved.

[0034] The bouncing-back unit can provide feedback from different cultural and regional perspectives. In a bouncing-back unit, for example, the generation AI provides feedback from different cultural and regional perspectives. For example, in a business proposal, it evaluates the acceptability in different cultural spheres. The generation AI also provides feedback from different regional perspectives. For example, in an RFP, it makes proposals that take into account the legal regulations and market needs of different regions. The generation AI also provides feedback from different cultural and regional perspectives. For example, in a project proposal, it proposes risk management and communication methods in different cultural spheres. In this way, providing feedback from different cultural and regional perspectives improves the diversity and acceptability of the materials.

[0035] The bouncing-back unit can visualize the contents of documents and provide feedback that is visually easy to understand. In the bouncing-back unit, for example, the generation AI visualizes the contents of documents and provides feedback that is visually easy to understand. For example, in a business proposal, risk management items are visualized using graphs and charts. The generation AI also visualizes the contents of documents and provides feedback that is visually easy to understand. For example, in an RFP, requirements and conditions are shown in a flowchart. The generation AI also visualizes the contents of documents and provides feedback that is visually easy to understand. For example, in a project plan, tasks and milestones are shown in a Gantt chart. In this way, by visualizing the contents of the documents, it is possible to provide feedback that is visually easy to understand.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The document generation system can further include a voice input unit. The voice input unit allows the user to give instructions by voice, and the generation AI analyzes the voice instructions to generate documents. For example, if the user gives a voice command such as "Create minutes for the next meeting," the generation AI will automatically generate minutes based on that command. The voice input unit also allows the user to give voice commands for bouncing ideas off each other or for polishing the document. For example, if the user gives a voice command such as "Make this part more specific," the generation AI will polish the document according to that command. This allows the user to operate the document generation system using only voice, without using their hands, improving convenience.

[0038] The document generation system can further include a translation unit. The translation unit can automatically translate the generated documents into multiple languages. For example, a business proposal generated by the generation AI in Japanese can be translated into English or Chinese. The translation unit can also translate documents into a specific language specified by the user. For example, if the user instructs the generation AI to "translate this document into French," the AI ​​will translate the document into French according to that instruction. Furthermore, the translation unit can check the appropriateness of the grammar and expressions of the translated documents and make corrections as necessary. This makes the document generation system effective for international projects and tasks that require multilingual support.

[0039] The document generation system can further include a data visualization unit. The data visualization unit can display the contents of the generated document in a visual format, such as a graph or chart. For example, when the generation AI generates a business proposal, it displays sales forecasts in a graph. The data visualization unit can also visualize data specified by the user. For example, if the user instructs the generation AI to "display this data in a pie chart," the generation AI will display the data in a pie chart in accordance with the instruction. Furthermore, the data visualization unit enables interactive manipulation of the visualized data, allowing the user to analyze the data in detail. This enables the document generation system to deepen understanding of the data and support decision-making.

[0040] The document generation system can further refer to the user's past project data and customize the documents to suit the user's specific needs and style. For example, the generation AI analyzes the user's past project data and customizes the generated documents to suit the user's specific needs and style. For example, a specific format used in the user's past projects can be reflected in a business flow diagram. The generation AI also refers to the user's past project data and reflects the user's preferences and style in the generated documents. For example, the requirements specification document can use specific terms and expressions that the user has used in the past. The generation AI also customizes the generated documents based on the user's past project data. For example, a manual can reflect specific procedures and methods used in the user's past projects. This makes it possible to generate documents that suit the user's specific needs and style.

[0041] The document generation system can also reference the user's past successes and failures to automatically incorporate risk avoidance measures. For example, the generation AI analyzes past successes and failures and automatically incorporates risk avoidance measures into the generated documents. For example, in a business proposal, it adds risk management items learned from past failures. The generation AI also references failures and describes specific risk avoidance measures in the generated documents. For example, in an RFP, it proposes risk avoidance measures based on issues that occurred in past projects. The generation AI also compares successes and failures and incorporates the most appropriate risk avoidance measures into the generated documents. For example, in a project plan, lessons learned from past failures can be reflected. In this way, the automatic incorporation of risk avoidance measures improves the reliability of the documents.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The document generation unit uses the generation AI to automatically generate the documents the user needs. For example, the generation AI analyzes the knowledge of predecessors around the world and similar projects and business documents accumulated in the past to automatically generate new documents the user needs. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates the documents based on that prompt. Step 2: The Discussion Unit automatically bounces ideas off the materials generated by the Material Generation Unit. For example, the generation AI automatically bounces ideas off the generated materials, checks their contents, and points out areas for improvement or shortcomings. Step 3: The brush-up section automatically brushes up the materials based on the points for improvement pointed out by the feedback section. For example, the generation AI automatically brushes up the materials based on the results of the feedback, improving the content of the materials and making them more complete. Step 4: The chat provider provides services to users in the form of chat. For example, users can issue instructions to the AI ​​through chat and check the generated materials, the results of discussions, and the details of improvements in real time.

[0044] (Example 2) The document creation system according to an embodiment of the present invention utilizes the knowledge of people around the world and similar projects and business documents accumulated in the past to automatically create new documents and provide automatic feedback and refinement in a chat format before the approver reviews. This allows the document creation system to efficiently create high-quality documents.

[0045] A document generation system according to an embodiment includes a document generation unit, a feedback unit, a brush-up unit, and a chat provision unit. The document generation unit automatically generates documents required by a user using a generation AI. For example, the generation AI analyzes the knowledge of predecessors around the world and similar project and business documents accumulated in the past to automatically generate new documents required by the user. The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates the documents based on the prompt. The feedback unit automatically provides feedback on the documents generated by the document generation unit. For example, the generation AI automatically provides feedback on the generated documents, reviews the contents of the documents, and points out areas for improvement or deficiencies. The brush-up unit automatically brushes up the documents based on the improvements pointed out by the feedback unit. For example, the generation AI automatically brushes up the documents based on the results of the feedback, improving the content of the documents and making them more complete. The chat provision unit provides services to users via chat. For example, a user can issue instructions to the generation AI via chat and check the generated documents, the results of the feedback, and the brush-up content in real time. This allows the material generation system to enable the user to efficiently create high-quality materials.

[0046] The document generation unit can automatically incorporate risk avoidance measures by referring to past success stories and failure stories. For example, the generation AI in the document generation unit analyzes past success stories and failure stories and automatically incorporates risk avoidance measures into the documents it generates. For example, in a business proposal, it adds risk management items learned from past failure stories. The generation AI also refers to failure stories and describes specific risk avoidance measures in the documents it generates. For example, in an RFP, it proposes risk avoidance measures based on problems that occurred in past projects. The generation AI also compares success stories and failure stories and incorporates the most appropriate risk avoidance measures into the documents it generates. For example, in a project plan, it reflects lessons learned from past failure stories. In this way, the reliability of the documents is improved by automatically incorporating risk avoidance measures.

[0047] The document generation unit can refer to the user's past project data and customize the documents to suit the user's specific needs and style. For example, the generation AI analyzes the user's past project data and customizes the documents it generates to suit the user's specific needs and style. For example, it reflects the specific format used in the user's past projects in a business flow diagram. The generation AI also refers to the user's past project data and reflects the user's preferences and style in the documents it generates. For example, it uses specific terms and expressions that the user used in the past in a requirements specification document. The generation AI also customizes the documents it generates based on the user's past project data. For example, it reflects the specific procedures and methods used in the user's past projects in a manual. This makes it possible to generate documents that suit the user's specific needs and style.

[0048] The document generation unit can use the emotion estimation function to analyze the user's current emotional state and generate documents in a tone and style corresponding to that emotion. The document generation unit, for example, uses the emotion estimation function to analyze the user's current emotional state and generate documents in a tone and style corresponding to that emotion. For example, if the user is feeling stressed, a manual is created in a relaxed tone. The emotion estimation function is also used to generate documents in a tone and style corresponding to the user's emotional state. For example, if the user is feeling positive, a business proposal is created in a bright tone. The emotion estimation function is also used to analyze the user's emotional state in real time and generate documents based on the results. For example, if the user is feeling impatient, an RFP is created in a concise and easy-to-understand tone. This makes it possible to generate documents in a tone and style corresponding to the user's emotional state.

[0049] The bouncing back and forth unit can analyze the logical structure of a document and point out logical contradictions and inconsistencies. For example, the generation AI analyzes the logical structure of a document and points out logical contradictions and inconsistencies. For example, in a business proposal, if there is a contradiction between the goal setting and the execution plan, it will point this out. The generation AI also analyzes the content of the document and identifies logical inconsistencies. For example, in an RFP, if there is a contradiction between the requirements and the proposal content, it will point this out. The generation AI also analyzes the logical structure of a document and points out logical inconsistencies. For example, in a project plan, if there is an inconsistency between risk management and resource allocation, it will point this out. In this way, pointing out logical contradictions and inconsistencies in the document improves the quality of the document.

[0050] The back-and-forth unit can cross-reference the contents of a document with other related documents to check for consistency. For example, the generation AI cross-references the contents of a document with other related documents to check for consistency. For example, it compares a business proposal with past project reports to check for consistency. The generation AI also compares the contents of a document with other related documents to check for consistency. For example, it compares an RFP with past proposals to check for consistency in requirements. The generation AI also cross-references the contents of a document with other related documents to check for consistency. For example, it compares a project plan with past project plans to check for consistency in resource allocation. By checking the consistency of the document in this way, the reliability of the document is improved.

[0051] The bouncing unit can use the emotion estimation function to analyze the impact that the content of a document has on the user's emotions and point out parts that cause negative emotions. The bouncing unit, for example, uses the emotion estimation function to analyze the impact that the content of a document has on the user's emotions and point out parts that cause negative emotions. For example, in a business proposal, it identifies parts that make the user feel anxious. It also uses the emotion estimation function to analyze the impact that the content of a document has on the user's emotions and point out parts that cause negative emotions. For example, in an RFP, it identifies parts that confuse the user. It also uses the emotion estimation function to analyze the impact that the content of a document has on the user's emotions and point out parts that cause negative emotions. For example, in a project proposal, it identifies parts that make the user feel stressed. In this way, the impact that the content of a document has on the user's emotions and point out parts that cause negative emotions can be analyzed, thereby improving the quality of the document.

[0052] The bouncing-back unit can provide feedback from different cultural and regional perspectives. In a bouncing-back unit, for example, the generation AI provides feedback from different cultural and regional perspectives. For example, in a business proposal, it evaluates the acceptability in different cultural spheres. The generation AI also provides feedback from different regional perspectives. For example, in an RFP, it makes proposals that take into account the legal regulations and market needs of different regions. The generation AI also provides feedback from different cultural and regional perspectives. For example, in a project proposal, it proposes risk management and communication methods in different cultural spheres. In this way, providing feedback from different cultural and regional perspectives improves the diversity and acceptability of the materials.

[0053] The bouncing-back unit can visualize the contents of documents and provide feedback that is visually easy to understand. In the bouncing-back unit, for example, the generation AI visualizes the contents of documents and provides feedback that is visually easy to understand. For example, in a business proposal, risk management items are visualized using graphs and charts. The generation AI also visualizes the contents of documents and provides feedback that is visually easy to understand. For example, in an RFP, requirements and conditions are shown in a flowchart. The generation AI also visualizes the contents of documents and provides feedback that is visually easy to understand. For example, in a project plan, tasks and milestones are shown in a Gantt chart. In this way, by visualizing the contents of the documents, it is possible to provide feedback that is visually easy to understand.

[0054] The feedback unit can use the emotion estimation function to provide feedback that makes the user feel the most positive emotions. The feedback unit, for example, uses the emotion estimation function to provide feedback that makes the user feel the most positive emotions. For example, in a business proposal, it adds encouraging comments that make the user feel confident. Also, it uses the emotion estimation function to provide feedback that makes the user feel positive emotions. For example, in an RFP, it makes specific improvement suggestions that make the user feel at ease. Also, it uses the emotion estimation function to provide feedback that makes the user feel the most positive emotions. For example, in a project proposal, it adds comments that motivate the user. In this way, by providing feedback that makes the user feel the most positive emotions, user satisfaction is improved.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The document generation system can further include a voice input unit. The voice input unit allows the user to give instructions by voice, and the generation AI analyzes the voice instructions to generate documents. For example, if the user gives a voice command such as "Create minutes for the next meeting," the generation AI will automatically generate minutes based on that command. The voice input unit also allows the user to give voice commands for bouncing ideas off each other or for polishing the document. For example, if the user gives a voice command such as "Make this part more specific," the generation AI will polish the document according to that command. This allows the user to operate the document generation system using only voice, without using their hands, improving convenience.

[0057] The document generation system can further include a translation unit. The translation unit can automatically translate the generated documents into multiple languages. For example, a business proposal generated by the generation AI in Japanese can be translated into English or Chinese. The translation unit can also translate documents into a specific language specified by the user. For example, if the user instructs the generation AI to "translate this document into French," the AI ​​will translate the document into French according to that instruction. Furthermore, the translation unit can check the appropriateness of the grammar and expressions of the translated documents and make corrections as necessary. This makes the document generation system effective for international projects and tasks that require multilingual support.

[0058] The document generation system can further include a data visualization unit. The data visualization unit can display the contents of the generated document in a visual format, such as a graph or chart. For example, when the generation AI generates a business proposal, it displays sales forecasts in a graph. The data visualization unit can also visualize data specified by the user. For example, if the user instructs the generation AI to "display this data in a pie chart," the generation AI will display the data in a pie chart in accordance with the instruction. Furthermore, the data visualization unit enables interactive manipulation of the visualized data, allowing the user to analyze the data in detail. This enables the document generation system to deepen understanding of the data and support decision-making.

[0059] The material generation system can further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on those emotions. The feedback unit estimates the user's emotions and provides feedback according to those emotions. For example, if the user is feeling stressed, it provides feedback that helps the user to relax. The feedback unit can also adjust the content of the materials based on the user's emotions. For example, if the user is feeling impatient, it generates materials in a concise and easy-to-understand tone. Furthermore, the feedback unit analyzes the user's emotions in real time and provides feedback based on the results. This provides appropriate feedback according to the user's emotions, thereby improving user satisfaction.

[0060] The document generation system can further include a tone adjustment unit that estimates the user's emotions and adjusts the tone and style of the document based on those emotions. The tone adjustment unit estimates the user's emotions and generates documents in a tone and style that corresponds to those emotions. For example, if the user has positive emotions, a business proposal is created in a bright tone. The tone adjustment unit can also adjust the content of the document based on the user's emotions. For example, if the user is feeling anxious, the document is generated in a tone that gives a sense of security. Furthermore, the tone adjustment unit analyzes the user's emotions in real time and adjusts the tone and style of the document based on the results. This makes it possible to generate documents in a tone and style that corresponds to the user's emotions.

[0061] The material generation system can further include an advice unit that estimates the user's emotions and provides appropriate advice based on those emotions. The advice unit estimates the user's emotions and provides advice according to those emotions. For example, if the user is feeling stressed, it provides advice to help the user relax. The advice unit can also adjust the content of the materials based on the user's emotions. For example, if the user is feeling impatient, it generates materials in a concise and easy-to-understand tone. Furthermore, the advice unit analyzes the user's emotions in real time and provides advice based on the results. This provides appropriate advice according to the user's emotions, thereby improving user satisfaction.

[0062] The document generation system can further include a content adjustment unit that estimates the user's emotions and adjusts the content of the documents based on those emotions. The content adjustment unit estimates the user's emotions and generates documents with content that corresponds to those emotions. For example, if the user has positive emotions, the content adjustment unit creates a business proposal with a bright tone. The content adjustment unit can also adjust the content of the documents based on the user's emotions. For example, if the user is feeling anxious, the content adjustment unit generates documents with a tone that gives a sense of security. Furthermore, the content adjustment unit analyzes the user's emotions in real time and adjusts the content of the documents based on the results. This makes it possible to generate documents with content that corresponds to the user's emotions.

[0063] The document generation system can further include a support unit that estimates the user's emotions and provides appropriate support based on those emotions. The support unit estimates the user's emotions and provides support according to those emotions. For example, if the user is feeling stressed, it provides support that helps the user relax. The support unit can also adjust the content of the documents based on the user's emotions. For example, if the user is feeling impatient, it generates documents in a concise and easy-to-understand tone. Furthermore, the support unit analyzes the user's emotions in real time and provides support based on the results. This provides appropriate support according to the user's emotions, thereby improving user satisfaction.

[0064] The document generation system can further refer to the user's past project data and customize the documents to suit the user's specific needs and style. For example, the generation AI analyzes the user's past project data and customizes the generated documents to suit the user's specific needs and style. For example, a specific format used in the user's past projects can be reflected in a business flow diagram. The generation AI also refers to the user's past project data and reflects the user's preferences and style in the generated documents. For example, the requirements specification document can use specific terms and expressions that the user has used in the past. The generation AI also customizes the generated documents based on the user's past project data. For example, a manual can reflect specific procedures and methods used in the user's past projects. This makes it possible to generate documents that suit the user's specific needs and style.

[0065] The document generation system can also reference the user's past successes and failures to automatically incorporate risk avoidance measures. For example, the generation AI analyzes past successes and failures and automatically incorporates risk avoidance measures into the generated documents. For example, in a business proposal, it adds risk management items learned from past failures. The generation AI also references failures and describes specific risk avoidance measures in the generated documents. For example, in an RFP, it proposes risk avoidance measures based on issues that occurred in past projects. The generation AI also compares successes and failures and incorporates the most appropriate risk avoidance measures into the generated documents. For example, in a project plan, lessons learned from past failures can be reflected. In this way, the automatic incorporation of risk avoidance measures improves the reliability of the documents.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The document generation unit uses the generation AI to automatically generate the documents the user needs. For example, the generation AI analyzes the knowledge of predecessors around the world and similar projects and business documents accumulated in the past to automatically generate new documents the user needs. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates the documents based on that prompt. Step 2: The Discussion Unit automatically bounces ideas off the materials generated by the Material Generation Unit. For example, the generation AI automatically bounces ideas off the generated materials, checks their contents, and points out areas for improvement or shortcomings. Step 3: The brush-up section automatically brushes up the materials based on the points for improvement pointed out by the feedback section. For example, the generation AI automatically brushes up the materials based on the results of the feedback, improving the content of the materials and making them more complete. Step 4: The chat provider provides services to users in the form of chat. For example, users can issue instructions to the AI ​​through chat and check the generated materials, the results of discussions, and the details of improvements in real time.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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 generation unit that automatically generates materials required by users using a generation AI; a hitting unit that automatically hits the material generated by the material generation unit; a brush-up unit that automatically brushes up the materials based on the points for improvement pointed out by the bulletin board unit; a chat providing unit that provides services to users in a chat format; A system characterized by:

2. The material generation unit Referencing past successes and failures to automatically incorporate risk avoidance measures 2. The system of claim 1.

3. The wall striking section is Analyze the logical structure of the material and point out logical contradictions and inconsistencies 2. The system of claim 1.

4. The brush-up unit Compare the contents of the above materials with the latest industry trends and technical information to reflect the latest information.

2. The system of claim 1.

5. The chat providing unit Analyze the chat-style exchanges and introduce natural language processing technology to more accurately understand the user's intentions and needs.

2. The system of claim 1.

6. The material generation unit Analyzing the user's current emotional state and generating materials in a tone and style that corresponds to that emotion 2. The system of claim 1.

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