system

Abstract

An object of a system according to an embodiment is to quickly and efficiently perform material creation and preparation for presentation.SOLUTION: A system includes an analysis part, an interaction part, a proposal part, and a support part. The analysis unit analyzes the in-house tool and the material AI using the generated date, and estimates and proposes the purpose of the MTG and the information of the attendee. The dialogue unit organizes the configuration of the material through a conversation with the user. The suggester embodies an image in the user's head. The support unit supports organization of the user's thought.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, creating materials and preparing presentations required a lot of time and effort, making it difficult to concentrate on the actual work.

[0005] The system according to the embodiment aims to quickly and efficiently prepare materials and presentations. [Means for solving the problem]

[0006] The system according to the embodiment comprises an analysis unit, a dialogue unit, a proposal unit, and a support unit. The analysis unit uses a generation AI to analyze internal tools and document data, and infers and proposes the purpose of the meeting and information about attendees. The dialogue unit puts together the structure of the document through conversation with the user. The proposal unit materializes the image in the user's mind. The support unit helps the user organize their thoughts. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and efficiently prepare materials and presentations. [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 support system according to an embodiment of the present invention is a system that uses generative AI to quickly create documents and prepare presentations. This system analyzes internal tools and document data to estimate and suggest information about the purpose of meetings and attendees. This allows the document creation support system to enable users to quickly create documents and prepare presentations.

[0029] The document creation support system according to the embodiment includes an analysis unit, a dialogue unit, a proposal unit, and a support unit. The analysis unit uses a generation AI to analyze internal tools and document data to infer and propose meeting objectives and attendee information. For example, the generation AI analyzes past meeting minutes and attendee lists to predict the objectives and attendees of the next meeting. The generation AI can also analyze internal chat and email content to infer more detailed objectives and attendee information. The dialogue unit compiles the structure of the document through conversation with the user. For example, if a user instructs, "I want to create materials for my next presentation," the generation AI asks, "What content do you want to include?" and proposes the structure of the document based on the user's response. The dialogue unit can also analyze the user's past presentation materials and propose a document structure tailored to the user's preferences and style. The proposal unit materializes the image in the user's mind. For example, if a user instructs, "I want to include a graph on this slide," the generation AI generates an appropriate graph and inserts it into the slide. The suggestion unit can also analyze the content of user comments and automatically generate related visual materials. The support unit helps users organize their thoughts. For example, when a user asks, "What are the main points of this presentation?", the generation AI analyzes the user's past comments and materials and suggests the main points. The support unit can also analyze the content of user comments and automatically search for and suggest related information and data. This allows the document creation support system according to the embodiment to quickly prepare documents and presentations. For example, by automatically inferring and suggesting the purpose of a meeting and attendee information, users can save time. Furthermore, by summarizing the structure of documents in a conversational format, users can create documents that accurately reflect their intentions. Furthermore, by organizing their thoughts through dialogue with the generation AI, they can create more appropriate documents and presentations.

[0030] The analysis unit analyzes not only meeting minutes, but also the contents of internal chats and emails, making it possible to infer more detailed information about the purpose and attendees. For example, the analysis unit can infer more detailed information about the purpose and attendees of the next meeting by having the generation AI analyze past meeting minutes and the contents of internal chats and emails at the same time. For example, if there are many discussions about a specific project, it will suggest attendees related to that project. This makes it possible to infer more detailed information about the purpose and attendees of the meeting.

[0031] The analysis unit analyzes attendees' past comments and behavioral patterns to infer the interests and expectations of each attendee. For example, the analysis unit uses a generative AI to analyze attendees' past comments and behavioral patterns to infer the interests and expectations of each attendee. For example, it identifies attendees who frequently speak on a particular topic and suggests agenda items related to that topic. This makes it possible to infer the interests and expectations of each attendee.

[0032] The analysis unit analyzes external industry news and trend information and can propose the latest information related to the purpose and agenda of the meeting. For example, the analysis unit uses a generation AI to analyze external industry news and trend information and propose the latest information related to the purpose and agenda of the meeting. For example, the agenda is set based on the latest technological trends and market trends. This makes it possible to propose the latest information related to the purpose and agenda of the meeting.

[0033] The analysis unit can refer to meeting data from other companies and industries and propose best practices. For example, the generative AI can refer to meeting data from other companies and industries and propose best practices. For example, it can refer to the agendas and progress methods of successful meetings. This makes it possible to propose best practices from other companies and industries.

[0034] The dialogue unit can analyze the user's past presentation materials and propose a document structure that matches the user's preferences and style. For example, the dialogue unit's generation AI can analyze the user's past presentation materials and propose a document structure that matches the user's preferences and style. For example, it can create materials based on a specific format or design. This makes it possible to propose a document structure that matches the user's preferences and style.

[0035] The dialogue unit can deeply understand the intent of the user's statements and automatically search for and suggest related data and information. For example, if a user says, "I want to include sales data," the dialogue unit can deeply understand the intent of the user's statements and automatically search for and suggest related data and information. For example, if a user says, "I want to include sales data," the dialogue unit can deeply understand the intent of the user's statements and automatically search for and suggest related data and information.

[0036] The dialogue unit can refer to the success stories of other users and propose effective document structures. For example, the generation AI can refer to the success stories of other users and propose effective document structures. For example, it can refer to the formats and designs of successful presentation materials. This makes it possible to refer to the success stories of other users and propose effective document structures.

[0037] The dialogue part can refer to the document structures of different industries and fields and incorporate innovative ideas. For example, the generative AI can refer to the document structures of different industries and fields and incorporate innovative ideas. For example, the structure of technical presentation materials can be applied to marketing materials. This allows the dialogue part to refer to the document structures of different industries and fields and incorporate innovative ideas.

[0038] The suggestion unit can analyze the user's past visual designs and propose designs that match the user's preferences. For example, the suggestion unit uses a generation AI to analyze the user's past visual designs and propose designs that match the user's preferences. For example, it creates a design based on a specific color scheme or layout. This makes it possible to propose designs that match the user's preferences.

[0039] The suggestion unit can analyze the content of user comments and automatically generate related visual materials. For example, the suggestion unit uses a generation AI to analyze the content of user comments and automatically generate related visual materials. For example, if a user says, "I want to create a sales graph," an appropriate graph will be generated. This makes it possible to automatically generate visual materials based on the content of user comments.

[0040] The suggestion unit can refer to the visual designs of other users and propose effective designs. For example, the generative AI can refer to the visual designs of other users and propose effective designs. For example, it can incorporate elements of successful designs. This allows the suggestion unit to refer to the visual designs of other users and propose effective designs.

[0041] The proposal unit can refer to visual designs from different industries and fields and incorporate innovative designs. For example, the generative AI can refer to visual designs from different industries and fields and incorporate innovative designs. For example, it can apply technical designs to marketing materials. This allows the proposal unit to refer to visual designs from different industries and fields and incorporate innovative designs.

[0042] The support unit analyzes the user's past statements and materials and can systematically organize the user's thoughts. For example, the support unit uses a generation AI to analyze the user's past statements and materials and systematically organize the user's thoughts. For example, it extracts main points based on past presentation materials and meeting minutes. This allows the user's past statements and materials to be analyzed and their thoughts to be systematically organized.

[0043] The support unit can analyze the content of user statements and automatically search for and suggest related information and data. For example, the support unit uses a generation AI to analyze the content of user statements and automatically search for and suggest related information and data. For example, if a user says, "I want to include sales data," the system will suggest appropriate sales data. This makes it possible to suggest related information and data based on the content of user statements.

[0044] The support unit can refer to the success stories of other users and suggest effective ways to organize thoughts. For example, the generation AI can refer to the success stories of other users and suggest effective ways to organize thoughts. For example, it can refer to the structure and organization methods of successful presentation materials. This makes it possible to refer to the success stories of other users and suggest effective ways to organize thoughts.

[0045] The support department can refer to methods of organizing thoughts in different industries and fields and incorporate innovative ideas. For example, the generative AI can apply technical organization methods to marketing materials. This allows the support department to refer to methods of organizing thoughts in different industries and fields and incorporate innovative ideas.

[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 analysis unit can analyze a user's past document creation history and automatically suggest templates and formats that the user frequently uses. For example, if a user prefers a particular slide layout, that layout can be automatically suggested the next time the user creates a document. It can also suggest visually consistent documents based on the color and font styles the user has used in the past. It can also extract the parts of documents that were particularly well-received by the user in the past and present them as reference for the next document creation.

[0048] The analysis unit can analyze the user's schedule and calendar information to suggest the optimal timing for creating documents. For example, it can analyze the user's schedule, identify times when they are free or when they are likely to concentrate, and suggest creating documents at those times. It can also send reminders before important meetings or presentations to encourage preparation for document creation. It can also predict the optimal work time based on the user's past schedule data, supporting efficient document creation.

[0049] The analysis section can refer to external databases and online resources to incorporate the latest research findings and industry best practices into documents. For example, it can automatically search for the latest technical papers and market reports and incorporate relevant information into documents. It can also incorporate opinions and interviews with industry experts to enhance the credibility and persuasiveness of documents. It can also refer to success stories from other companies and organizations to suggest effective ways to create documents.

[0050] The dialogue unit can analyze the user's comments and automatically suggest related visual materials and design templates. For example, if a user says, "I want to include sales data," it can suggest appropriate graphs and charts. Similarly, if a user says, "I want to include an infographic on this slide," it can provide a relevant infographic template. Furthermore, it can suggest a design theme for the entire document based on the user's comments, allowing for the creation of visually consistent documents.

[0051] The dialogue section can incorporate innovative ideas by referencing the structure of documents from different industries and fields. For example, the structure of technical presentation documents can be applied to marketing documents. Also, incorporating the structure of documents from the education field into business presentations can provide a new perspective. Furthermore, it is possible to create documents with a global perspective by referencing the structure of documents from different cultures and regions. This allows users to create innovative documents that incorporate diverse perspectives.

[0052] The suggestion unit can analyze the user's past visual designs and suggest designs that match the user's preferences. For example, it can suggest visually consistent designs based on the color usage and layout the user has used in the past. Also, if the user prefers a particular design style, it can reflect that style when creating the next document. Furthermore, it can extract design elements that were particularly well-received in documents created by the user in the past and present them as reference when creating the next document.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The analysis unit uses the generation AI to analyze internal tools and document data, and infer and suggest information about the purpose and attendees of meetings. For example, the generation AI can analyze past meeting minutes and attendee lists to predict the purpose and attendees of the next meeting. The generation AI can also analyze the content of internal chats and emails to infer more detailed information about the purpose and attendees. Step 2: The dialogue unit puts together the structure of the materials through conversation with the user. For example, if the user says, "I want to create materials for my next presentation," the generation AI will ask, "What content do you want to include?" and suggest a structure for the materials based on the user's answer. The dialogue unit can also analyze the user's past presentation materials and suggest a structure for the materials that matches the user's preferences and style. Step 3: The suggestion unit materializes the image in the user's mind. For example, if the user says, "I want to include a graph on this slide," the generation AI will generate an appropriate graph and insert it into the slide. The suggestion unit can also analyze the user's comments and automatically generate related visual materials. Step 4: The support section helps the user organize their thoughts. For example, if the user asks, "What are the main points of this presentation?", the generation AI analyzes the user's past comments and materials and suggests key points. The support section can also analyze the content of the user's comments and automatically search for and suggest related information and data.

[0055] (Example 2) The document creation support system according to an embodiment of the present invention is a system that uses generative AI to quickly create documents and prepare presentations. This system analyzes internal tools and document data to estimate and suggest information about the purpose of meetings and attendees. This allows the document creation support system to enable users to quickly create documents and prepare presentations.

[0056] The document creation support system according to the embodiment includes an analysis unit, a dialogue unit, a proposal unit, and a support unit. The analysis unit uses a generation AI to analyze internal tools and document data to infer and propose meeting objectives and attendee information. For example, the generation AI analyzes past meeting minutes and attendee lists to predict the objectives and attendees of the next meeting. The generation AI can also analyze internal chat and email content to infer more detailed objectives and attendee information. The dialogue unit compiles the structure of the document through conversation with the user. For example, if a user instructs, "I want to create materials for my next presentation," the generation AI asks, "What content do you want to include?" and proposes the structure of the document based on the user's response. The dialogue unit can also analyze the user's past presentation materials and propose a document structure tailored to the user's preferences and style. The proposal unit materializes the image in the user's mind. For example, if a user instructs, "I want to include a graph on this slide," the generation AI generates an appropriate graph and inserts it into the slide. The suggestion unit can also analyze the content of user comments and automatically generate related visual materials. The support unit helps users organize their thoughts. For example, when a user asks, "What are the main points of this presentation?", the generation AI analyzes the user's past comments and materials and suggests the main points. The support unit can also analyze the content of user comments and automatically search for and suggest related information and data. This allows the document creation support system according to the embodiment to quickly prepare documents and presentations. For example, by automatically inferring and suggesting the purpose of a meeting and attendee information, users can save time. Furthermore, by summarizing the structure of documents in a conversational format, users can create documents that accurately reflect their intentions. Furthermore, by organizing their thoughts through dialogue with the generation AI, they can create more appropriate documents and presentations.

[0057] The analysis unit analyzes not only meeting minutes, but also the contents of internal chats and emails, making it possible to infer more detailed information about the purpose and attendees. For example, the analysis unit can infer more detailed information about the purpose and attendees of the next meeting by having the generation AI analyze past meeting minutes and the contents of internal chats and emails at the same time. For example, if there are many discussions about a specific project, it will suggest attendees related to that project. This makes it possible to infer more detailed information about the purpose and attendees of the meeting.

[0058] The analysis unit analyzes attendees' past comments and behavioral patterns to infer the interests and expectations of each attendee. For example, the analysis unit uses a generative AI to analyze attendees' past comments and behavioral patterns to infer the interests and expectations of each attendee. For example, it identifies attendees who frequently speak on a particular topic and suggests agenda items related to that topic. This makes it possible to infer the interests and expectations of each attendee.

[0059] The analysis unit can use the emotion estimation function to analyze the emotional reactions of attendees in past meetings and propose the agenda and progress method for the next meeting. For example, the analysis unit can use the emotion estimation function to analyze the emotional reactions of attendees in past meetings and propose the agenda and progress method for the next meeting. For example, the analysis unit can prioritize agenda items that received a lot of positive emotional reactions. This makes it possible to propose the agenda and progress method for the next meeting.

[0060] The analysis unit analyzes external industry news and trend information and can propose the latest information related to the purpose and agenda of the meeting. For example, the analysis unit uses a generation AI to analyze external industry news and trend information and propose the latest information related to the purpose and agenda of the meeting. For example, the agenda is set based on the latest technological trends and market trends. This makes it possible to propose the latest information related to the purpose and agenda of the meeting.

[0061] The analysis unit can refer to meeting data from other companies and industries and propose best practices. For example, the generative AI can refer to meeting data from other companies and industries and propose best practices. For example, it can refer to the agendas and progress methods of successful meetings. This makes it possible to propose best practices from other companies and industries.

[0062] The analysis unit uses the emotion estimation function to monitor the emotional states of attendees in real time and propose appropriate responses while the meeting is in progress. The analysis unit, for example, uses the emotion estimation function to monitor the emotional states of attendees in real time and propose appropriate responses while the meeting is in progress. For example, if an attendee is feeling stressed, the analysis unit suggests taking a break. This makes it possible to propose appropriate responses while the meeting is in progress.

[0063] The dialogue unit can analyze the user's past presentation materials and propose a document structure that matches the user's preferences and style. For example, the dialogue unit's generation AI can analyze the user's past presentation materials and propose a document structure that matches the user's preferences and style. For example, it can create materials based on a specific format or design. This makes it possible to propose a document structure that matches the user's preferences and style.

[0064] The dialogue unit can deeply understand the intent of the user's statements and automatically search for and suggest related data and information. For example, if a user says, "I want to include sales data," the dialogue unit can deeply understand the intent of the user's statements and automatically search for and suggest related data and information. For example, if a user says, "I want to include sales data," the dialogue unit can deeply understand the intent of the user's statements and automatically search for and suggest related data and information.

[0065] The dialogue unit can use the emotion estimation function to analyze the user's emotional state and propose a material structure for reducing stress. For example, the dialogue unit can use the emotion estimation function to analyze the user's emotional state and propose a material structure for reducing stress. For example, if the user is feeling stressed, a simple structure can be proposed. This makes it possible to analyze the user's emotional state and propose a material structure for reducing stress.

[0066] The dialogue unit can refer to the success stories of other users and propose effective document structures. For example, the generation AI can refer to the success stories of other users and propose effective document structures. For example, it can refer to the formats and designs of successful presentation materials. This makes it possible to refer to the success stories of other users and propose effective document structures.

[0067] The dialogue part can refer to the document structures of different industries and fields and incorporate innovative ideas. For example, the generative AI can refer to the document structures of different industries and fields and incorporate innovative ideas. For example, the structure of technical presentation materials can be applied to marketing materials. This allows the dialogue part to refer to the document structures of different industries and fields and incorporate innovative ideas.

[0068] The dialogue unit can use the emotion estimation function to adjust the document composition based on the user's emotion in real time. The dialogue unit, for example, uses the emotion estimation function to adjust the document composition based on the user's emotion in real time. For example, if the user is feeling stressed, a simple composition is suggested. This allows the document composition to be adjusted in real time based on the user's emotion.

[0069] The suggestion unit can analyze the user's past visual designs and propose designs that match the user's preferences. For example, the suggestion unit uses a generation AI to analyze the user's past visual designs and propose designs that match the user's preferences. For example, it creates a design based on a specific color scheme or layout. This makes it possible to propose designs that match the user's preferences.

[0070] The suggestion unit can analyze the content of user comments and automatically generate related visual materials. For example, the suggestion unit uses a generation AI to analyze the content of user comments and automatically generate related visual materials. For example, if a user says, "I want to create a sales graph," an appropriate graph will be generated. This makes it possible to automatically generate visual materials based on the content of user comments.

[0071] The suggestion unit can use the emotion estimation function to analyze the emotional state of the user and suggest a visual design that elicits positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze the emotional state of the user and suggest a visual design that elicits positive emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a design that helps the user relax. This makes it possible to analyze the emotional state of the user and suggest a visual design that elicits positive emotions.

[0072] The suggestion unit can refer to the visual designs of other users and propose effective designs. For example, the generative AI can refer to the visual designs of other users and propose effective designs. For example, it can incorporate elements of successful designs. This allows the suggestion unit to refer to the visual designs of other users and propose effective designs.

[0073] The proposal unit can refer to visual designs from different industries and fields and incorporate innovative designs. For example, the generative AI can refer to visual designs from different industries and fields and incorporate innovative designs. For example, it can apply technical designs to marketing materials. This allows the proposal unit to refer to visual designs from different industries and fields and incorporate innovative designs.

[0074] The suggestion unit can use the emotion estimation function to adjust the visual design based on the user's emotion in real time. The suggestion unit, for example, uses the emotion estimation function to adjust the visual design based on the user's emotion in real time. For example, if the user is feeling stressed, the suggestion unit can suggest a design that helps the user relax. This allows the visual design to be adjusted in real time based on the user's emotion.

[0075] The support unit analyzes the user's past statements and materials and can systematically organize the user's thoughts. For example, the support unit uses a generation AI to analyze the user's past statements and materials and systematically organize the user's thoughts. For example, it extracts main points based on past presentation materials and meeting minutes. This allows the user's past statements and materials to be analyzed and their thoughts to be systematically organized.

[0076] The support unit can analyze the content of user statements and automatically search for and suggest related information and data. For example, the support unit uses a generation AI to analyze the content of user statements and automatically search for and suggest related information and data. For example, if a user says, "I want to include sales data," the system will suggest appropriate sales data. This makes it possible to suggest related information and data based on the content of user statements.

[0077] The support unit can use the emotion estimation function to analyze the user's emotional state and suggest a method for organizing thoughts to reduce stress. For example, the support unit can use the emotion estimation function to analyze the user's emotional state and suggest a method for organizing thoughts to reduce stress. For example, if the user is feeling stressed, a simple organizing method can be suggested. This makes it possible to analyze the user's emotional state and suggest a method for organizing thoughts to reduce stress.

[0078] The support unit can refer to the success stories of other users and suggest effective ways to organize thoughts. For example, the generation AI can refer to the success stories of other users and suggest effective ways to organize thoughts. For example, it can refer to the structure and organization methods of successful presentation materials. This makes it possible to refer to the success stories of other users and suggest effective ways to organize thoughts.

[0079] The support department can refer to methods of organizing thoughts in different industries and fields and incorporate innovative ideas. For example, the generative AI can apply technical organization methods to marketing materials. This allows the support department to refer to methods of organizing thoughts in different industries and fields and incorporate innovative ideas.

[0080] The support unit can use the emotion estimation function to adjust in real time the way the user organizes their thoughts based on their emotions. For example, the support unit uses the emotion estimation function to adjust in real time the way the user organizes their thoughts based on their emotions. For example, if the user is feeling stressed, the support unit suggests a simple way to organize their thoughts. This allows the way the user organizes their thoughts based on their emotions to be adjusted in real time.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The analysis unit can analyze a user's past document creation history and automatically suggest templates and formats that the user frequently uses. For example, if a user prefers a particular slide layout, that layout can be automatically suggested the next time the user creates a document. It can also suggest visually consistent documents based on the color and font styles the user has used in the past. It can also extract the parts of documents that were particularly well-received by the user in the past and present them as reference for the next document creation.

[0083] The analysis unit can analyze the user's schedule and calendar information to suggest the optimal timing for creating documents. For example, it can analyze the user's schedule, identify times when they are free or when they are likely to concentrate, and suggest creating documents at those times. It can also send reminders before important meetings or presentations to encourage preparation for document creation. It can also predict the optimal work time based on the user's past schedule data, supporting efficient document creation.

[0084] The analysis unit can use the emotion estimation function to analyze the user's emotional state and provide feedback to improve motivation according to the progress of document creation. For example, if the user is tired, an encouraging message can be displayed and a break can be suggested. If the user is feeling stressed, relaxing music or short exercises to refresh the user can be suggested. Furthermore, if the user is feeling positive, positive feedback can be provided to maintain that emotion, increasing motivation for the work.

[0085] The analysis section can refer to external databases and online resources to incorporate the latest research findings and industry best practices into documents. For example, it can automatically search for the latest technical papers and market reports and incorporate relevant information into documents. It can also incorporate opinions and interviews with industry experts to enhance the credibility and persuasiveness of documents. It can also refer to success stories from other companies and organizations to suggest effective ways to create documents.

[0086] The analysis unit uses the emotion estimation function to analyze the user's emotional state and suggest breaks at appropriate times during the document creation process. For example, if the user is losing concentration, it can suggest a short break and provide an activity to refresh them. If the user is feeling stressed, it can also provide advice on creating a relaxing environment. Furthermore, if the user is feeling positive, it can suggest a short break to maintain that emotion, thereby improving work efficiency.

[0087] The dialogue unit can analyze the user's comments and automatically suggest related visual materials and design templates. For example, if a user says, "I want to include sales data," it can suggest appropriate graphs and charts. Similarly, if a user says, "I want to include an infographic on this slide," it can provide a relevant infographic template. Furthermore, it can suggest a design theme for the entire document based on the user's comments, allowing for the creation of visually consistent documents.

[0088] The dialogue unit uses its emotion estimation function to analyze the user's emotional state and provide appropriate advice during the document creation process. For example, if the user is feeling stressed, it can suggest a simple document structure to reduce the burden of the work. Alternatively, if the user is feeling positive, it can suggest a challenging design or structure to stimulate creativity. Furthermore, it can provide feedback at the appropriate time depending on the user's emotional state and support the progress of the work.

[0089] The dialogue section can incorporate innovative ideas by referencing the structure of documents from different industries and fields. For example, the structure of technical presentation documents can be applied to marketing documents. Also, incorporating the structure of documents from the education field into business presentations can provide a new perspective. Furthermore, it is possible to create documents with a global perspective by referencing the structure of documents from different cultures and regions. This allows users to create innovative documents that incorporate diverse perspectives.

[0090] The suggestion unit can analyze the user's past visual designs and suggest designs that match the user's preferences. For example, it can suggest visually consistent designs based on the color usage and layout the user has used in the past. Also, if the user prefers a particular design style, it can reflect that style when creating the next document. Furthermore, it can extract design elements that were particularly well-received in documents created by the user in the past and present them as reference when creating the next document.

[0091] The suggestion unit uses the emotion estimation function to analyze the user's emotional state and propose visual designs that elicit positive emotions. For example, if the user is feeling stressed, it can propose relaxing colors and layouts. If the user is feeling positive, it can also propose bright colors and dynamic designs to further enhance those emotions. Furthermore, it can propose appropriate visual effects and animations according to the user's emotional state to enhance the appeal of the materials.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The analysis unit uses the generation AI to analyze internal tools and document data, and infer and suggest information about the purpose and attendees of meetings. For example, the generation AI can analyze past meeting minutes and attendee lists to predict the purpose and attendees of the next meeting. The generation AI can also analyze the content of internal chats and emails to infer more detailed information about the purpose and attendees. Step 2: The dialogue unit puts together the structure of the materials through conversation with the user. For example, if the user says, "I want to create materials for my next presentation," the generation AI will ask, "What content do you want to include?" and suggest a structure for the materials based on the user's answer. The dialogue unit can also analyze the user's past presentation materials and suggest a structure for the materials that matches the user's preferences and style. Step 3: The suggestion unit materializes the image in the user's mind. For example, if the user says, "I want to include a graph on this slide," the generation AI will generate an appropriate graph and insert it into the slide. The suggestion unit can also analyze the user's comments and automatically generate related visual materials. Step 4: The support section helps the user organize their thoughts. For example, if the user asks, "What are the main points of this presentation?", the generation AI analyzes the user's past comments and materials and suggests key points. The support section can also analyze the content of the user's comments and automatically search for and suggest related information and data.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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. The analysis department uses generative AI to analyze internal tools and document data, and predicts and suggests information about the purpose of meetings and attendees. A dialogue section that organizes the materials through conversation with the user; A proposal section that materializes the image in the user's mind; a support unit that supports the user in organizing their thoughts; A system characterized by:

2. The analysis unit Analyze external industry news and trend information and propose updates relevant to the purpose and agenda of the MTG 2. The system of claim 1.

3. The dialogue unit Analyzing the user's past presentation materials and proposing a material structure that matches the user's preferences and style 2. The system of claim 1.

4. The proposal unit Analyzing the user's past visual designs and proposing designs that match the user's preferences 2. The system of claim 1.

5. The support unit Analyzing the user's past statements and the materials, and systematically organizing the user's thoughts.

2. The system of claim 1.

6. The analysis unit Analyze the emotional reactions of the attendees at past meetings and propose the agenda and procedure for the next meeting.

2. The system of claim 1.

Images