System

Abstract

An object of the system according to the embodiment is to develop an autonomous thinking agent by effectively utilizing ideas gathered in a generation AI contest.SOLUTION: A system according to an embodiment includes a data collection unit, a data analysis unit, a proposal generation unit, and a plug-in use unit. The data-collecting part collects idea AI of a generation data-contest. The data analysis unit analyzes the idea data collected by the data collection unit. The proposal generation unit proposes an optimal idea according to an instruction of the user based on the data analyzed by the data analysis unit. The plug-in consumer uses the idea pdf submitted in the generation AI contest as an external source.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 has not established a way to effectively utilize the large number of ideas collected in generative AI contests, and there is room for improvement.

[0005] The system according to the embodiment aims to develop an autonomous thinking agent by effectively utilizing the ideas gathered in the generative AI contest. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a proposal generation unit, and a plug-in usage unit. The data collection unit collects idea data from the AI ​​generation contest. The data analysis unit analyzes the idea data collected by the data collection unit. The proposal generation unit proposes optimal ideas in accordance with user instructions based on the data analyzed by the data analysis unit. The plug-in usage unit uses idea PDFs submitted in the AI ​​generation contest as an external source. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize the ideas gathered in the generative AI contest to develop an autonomous thinking agent. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The autonomous thinking agent system according to an embodiment of the present invention is a system that uses the large number of ideas collected in the generative AI contest as a data source to develop an autonomous thinking agent using the function calling function of the generative AI. As a result, the autonomous thinking agent system can effectively utilize the large number of ideas collected in the generative AI contest and make optimal proposals to users.

[0029] The autonomous thinking agent system according to the embodiment includes a data collection unit, a data analysis unit, a proposal generation unit, and a plug-in usage unit. The data collection unit collects idea data from the AI ​​generation contest. For example, it collects PDF files of ideas submitted in the AI ​​generation contest and registers them in a database. The data collection unit also converts the idea content into a format that is easy for the AI ​​generation to analyze. The data analysis unit analyzes the idea data collected by the data collection unit. For example, the AI ​​generation analyzes the content of the ideas and registers them in the database. The data analysis unit can also perform sentiment analysis of the ideas, filter them based on the intensity and type of sentiment, and register them in the database. The proposal generation unit proposes optimal ideas in response to user instructions based on the data analyzed by the data analysis unit. For example, if a user requests, "Please propose a new marketing strategy," the proposal generation unit searches the database for related ideas and makes optimal proposals. The proposal generation unit can also estimate the user's sentiment and propose optimal ideas based on the sentiment. The plug-in usage unit uses PDF ideas submitted in the AI ​​generation contest as an external source. For example, when obtaining information about a specific technology, the system searches for related PDF files, analyzes their contents, and extracts the necessary information. The plug-in user unit can also automatically summarize the contents of the PDF files and propose optimal ideas based on the summarized information. This allows the autonomous thinking agent system according to the embodiment to effectively utilize the large number of ideas collected in the AI ​​generation contest and provide optimal proposals to users.

[0030] The data analysis unit can collect background information about the idea submitter, evaluate the idea based on that information, and register the idea in a database. The data analysis unit, for example, registers the expertise and industry experience of the idea submitter in a database and evaluates the idea based on that information. For example, it prioritizes saving ideas in fields in which the submitter has expertise. The data analysis unit also builds a system that evaluates the feasibility and market demand of ideas based on the submitter's industry experience. For example, it gives high marks to ideas submitted by submitters with many years of industry experience and stores them in the database. The data analysis unit also collects background information about the submitter, such as their educational background and work history, and evaluates the reliability of the idea based on that information. For example, it prioritizes saving ideas submitted by submitters with high educational backgrounds or professional qualifications. This allows ideas to be evaluated based on the submitter's background information, and highly reliable ideas to be registered in the database.

[0031] The data analysis unit can display feedback from other users on submitted ideas in real time to promote evaluation. For example, the data analysis unit adds a feedback function to the idea collection platform and displays other users' ratings and comments on submitted ideas in real time. This allows the quality and feasibility of the ideas to be evaluated. The data analysis unit also scores other users' feedback on submitted ideas and prioritizes the ideas based on the scores. Specifically, ideas with a lot of positive feedback are preferentially stored in the database. The data analysis unit also uses the feedback function to allow other users to post improvement suggestions and questions about the ideas. This promotes idea refinement and improves the quality when saved in the database. This allows the evaluation of ideas to be promoted by displaying feedback from other users in real time.

[0032] When collecting ideas, the data collection unit simultaneously collects multimodal information, including image and audio data, and can derive ideas from visual and audio information. For example, the data collection unit allows users to upload image and audio data when submitting ideas, and evaluates the ideas based on the visual and audio information. For example, prototype images and explanatory audio are used to grasp the details of the idea. The data collection unit also uses image recognition technology to extract the content of the idea from the uploaded image data and store it in a database. Specifically, it analyzes product design and sketch images to evaluate the concreteness of the idea. The data collection unit also uses audio analysis technology to automatically extract the main points of the idea from the submitted audio data and convert it into text data. This integrates the visual and audio information with the text data and stores it in a database. In this way, collecting multimodal information makes it possible to derive ideas from visual and audio information as well.

[0033] The data collection department can periodically hold workshops that bring together experts from different industries or fields and collect ideas that emerge there. For example, the data collection department can periodically hold workshops that invite experts from different industries and register the ideas that emerge there in a database. For example, a venue can be created where experts from technical, design, and marketing fields can gather together. The data collection department can also record discussions at the workshops and save them as audio data. The audio can then be analyzed to extract important ideas and opinions and save them in a database. The data collection department can also collect materials and presentation data used in the workshops and save them in a database as background information for the ideas. This makes the source and rationale of the ideas clear. By collecting ideas that emerge from workshops that bring together experts from different industries and fields, it is possible to obtain ideas from a variety of perspectives.

[0034] The proposal generation unit can learn the user's past instruction history, understand the user's preferences and tendencies, and improve the accuracy of proposals. For example, the proposal generation unit constructs a system that understands the user's preferences and tendencies by having an autonomous thinking agent learn the user's past instruction history. For example, it analyzes the content of past instructions and identifies the user's preferences. The proposal generation unit also develops an algorithm that improves the accuracy of proposals based on the user's past instruction history. For example, it learns past success stories and failure stories and makes optimal proposals. The proposal generation unit also clusters the past instruction history and identifies similar instruction patterns to understand the user's preferences and tendencies. For example, it groups instructions related to specific themes or keywords. In this way, the proposal generation unit can improve the accuracy of proposals by learning the user's past instruction history.

[0035] The proposal generation unit can evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution. For example, the proposal generation unit adds a function to evaluate the feasibility of ideas proposed to an autonomous thinking agent. For example, it performs an evaluation based on technical feasibility and market demand. The proposal generation unit also builds a system that automatically generates the resources and procedures required for the execution of proposed ideas. For example, it automatically generates the necessary human resources, materials, and schedules. The proposal generation unit also develops an algorithm that determines the priority of proposed ideas based on the results of the feasibility evaluation. For example, it prioritizes ideas with high feasibility. This makes it possible to evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution, thereby improving the efficiency of execution.

[0036] The proposal generation unit can apply the autonomous thinking agent to different industries and applications to propose optimal ideas in each field. For example, the proposal generation unit applies the autonomous thinking agent to different industries to build a system that proposes optimal ideas in each field. For example, it can support fields such as medicine, education, and entertainment. The proposal generation unit also learns specialized knowledge in each field in order to apply it to different applications. For example, it adjusts the proposal algorithm based on data from a specific industry. The proposal generation unit also integrates databases from different industries to develop a system that makes comprehensive proposals in order to propose optimal ideas in each field. For example, it reflects trends and needs from different industries. In this way, the autonomous thinking agent can be applied to different industries and applications to propose optimal ideas in each field.

[0037] The proposal generation unit can add a function to visualize the proposed ideas, allowing the user to intuitively understand them. For example, the proposal generation unit adds a function to visualize the ideas proposed to the autonomous thinking agent. For example, the proposal generation unit visually displays the ideas using graphs, charts, or 3D models. The proposal generation unit also builds a system that automatically generates visual prototypes of proposed ideas so that the user can intuitively understand them. For example, it visually displays product designs or service flows. The proposal generation unit also develops an interface that allows the user to provide feedback based on the visualized ideas. For example, it collects user opinions based on the visual prototype. In this way, the proposed ideas can be visualized so that the user can intuitively understand them.

[0038] The plug-in using unit can add a function that automatically summarizes the contents of a PDF file and suggests the best idea based on the summarized information. The plug-in using unit, for example, builds a system that automatically summarizes the contents of a PDF file and suggests the best idea based on the summarized information. For example, it generates a summary and suggests related ideas. The plug-in using unit also develops an algorithm that suggests the best idea in response to a user's instructions based on the automatically summarized information. For example, it analyzes the summary and searches for related ideas. The plug-in using unit also builds a system that automatically summarizes the contents of a PDF file and updates a database based on the summary information. For example, it registers new ideas in the database based on the summary. This allows for efficient use of information by automatically summarizing the contents of a PDF file and suggesting the best idea based on the summary information.

[0039] The plug-in user section can integrate the contents of PDF files with other data sources to provide comprehensive information. For example, the plug-in user section can integrate the contents of PDF files with patent data to build a system that analyzes technological trends and competitive situations. For example, it can correlate and display patent information in the same technical field. The plug-in user section can also integrate the contents of PDF files with market data to develop a system that discovers new business opportunities. For example, it can extract ideas that meet specific market needs. The plug-in user section can also integrate the contents of PDF files with other data sources (e.g., academic paper databases) to build a system that keeps track of the latest research trends and technological innovations. For example, it can link related papers and research results. This allows the contents of PDF files to be integrated with other data sources to provide more comprehensive information.

[0040] The plugin user section automatically translates idea PDFs into different languages, enabling idea proposals from an international perspective. For example, the plugin user section uses an automatic translation function to build a system that translates idea PDFs into different languages ​​and enables idea proposals from an international perspective. For example, it translates into multiple languages, such as English, French, and Chinese. The plugin user section also develops a system that posts the translated idea PDFs to a multilingual idea collection platform and receives feedback from users around the world. The plugin user section also builds a system that collects advice and improvement suggestions from an international perspective based on the translated idea PDFs, thereby improving the quality of the ideas. For example, it reflects feedback that takes cultural and market differences into account. This allows idea proposals to be made from an international perspective by automatically translating idea PDFs into different languages.

[0041] The plugin usage section can convert the contents of a PDF file into a visual note or mind map, making it easier to understand visually. For example, the plugin usage section can convert the contents of a PDF file into a visual note and build a system that visually displays the key points of an idea. For example, it can indicate important points using diagrams or icons. The plugin usage section can also develop a system that converts the contents of a PDF file into a mind map format and visually organizes related keywords and concepts. This allows users to understand the overall picture of an idea at a glance. The plugin usage section can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display idea summaries. For example, it can provide a function to visualize summaries using drag and drop. This allows the contents of a PDF file to be converted into a visual note or mind map, making it easier to understand visually.

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

[0043] When collecting ideas, the data collection unit simultaneously collects multimodal information, including image and audio data, and can derive ideas from visual and audio information. For example, the data collection unit allows users to upload image and audio data when submitting ideas, and evaluates the ideas based on the visual and audio information. For example, prototype images and explanatory audio are used to grasp the details of the idea. The data collection unit also uses image recognition technology to extract the content of the idea from the uploaded image data and store it in a database. Specifically, it analyzes product design and sketch images to evaluate the concreteness of the idea. The data collection unit also uses audio analysis technology to automatically extract the main points of the idea from the submitted audio data and convert it into text data. This integrates the visual and audio information with the text data and stores it in a database. In this way, collecting multimodal information makes it possible to derive ideas from visual and audio information as well.

[0044] The data analysis unit can display feedback from other users on submitted ideas in real time to promote evaluation. For example, the data analysis unit adds a feedback function to the idea collection platform and displays other users' ratings and comments on submitted ideas in real time. This allows the quality and feasibility of the ideas to be evaluated. The data analysis unit also scores other users' feedback on submitted ideas and prioritizes the ideas based on the scores. Specifically, ideas with a lot of positive feedback are preferentially stored in the database. The data analysis unit also uses the feedback function to allow other users to post improvement suggestions and questions about the ideas. This promotes idea refinement and improves the quality when saved in the database. This allows the evaluation of ideas to be promoted by displaying feedback from other users in real time.

[0045] The data collection department can periodically hold workshops that bring together experts from different industries or fields and collect ideas that emerge there. For example, the data collection department can periodically hold workshops that invite experts from different industries and register the ideas that emerge there in a database. For example, a venue can be created where experts from technical, design, and marketing fields can gather together. The data collection department can also record discussions at the workshops and save them as audio data. The audio can then be analyzed to extract important ideas and opinions and save them in a database. The data collection department can also collect materials and presentation data used in the workshops and save them in a database as background information for the ideas. This makes the source and rationale of the ideas clear. By collecting ideas that emerge from workshops that bring together experts from different industries and fields, it is possible to obtain ideas from a variety of perspectives.

[0046] The proposal generation unit can learn the user's past instruction history, understand the user's preferences and tendencies, and improve the accuracy of proposals. For example, the proposal generation unit constructs a system that understands the user's preferences and tendencies by having an autonomous thinking agent learn the user's past instruction history. For example, it analyzes the content of past instructions and identifies the user's preferences. The proposal generation unit also develops an algorithm that improves the accuracy of proposals based on the user's past instruction history. For example, it learns past success stories and failure stories and makes optimal proposals. The proposal generation unit also clusters the past instruction history and identifies similar instruction patterns to understand the user's preferences and tendencies. For example, it groups instructions related to specific themes or keywords. In this way, the proposal generation unit can improve the accuracy of proposals by learning the user's past instruction history.

[0047] The proposal generation unit can evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution. For example, the proposal generation unit adds a function to evaluate the feasibility of ideas proposed to an autonomous thinking agent. For example, it performs an evaluation based on technical feasibility and market demand. The proposal generation unit also builds a system that automatically generates the resources and procedures required for the execution of proposed ideas. For example, it automatically generates the necessary human resources, materials, and schedules. The proposal generation unit also develops an algorithm that determines the priority of proposed ideas based on the results of the feasibility evaluation. For example, it prioritizes ideas with high feasibility. This makes it possible to evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution, thereby improving the efficiency of execution.

[0048] The plugin usage section can convert the contents of a PDF file into a visual note or mind map, making it easier to understand visually. For example, the plugin usage section can convert the contents of a PDF file into a visual note and build a system that visually displays the key points of an idea. For example, it can indicate important points using diagrams or icons. The plugin usage section can also develop a system that converts the contents of a PDF file into a mind map format and visually organizes related keywords and concepts. This allows users to understand the overall picture of an idea at a glance. The plugin usage section can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display idea summaries. For example, it can provide a function to visualize summaries using drag and drop. This allows the contents of a PDF file to be converted into a visual note or mind map, making it easier to understand visually.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection unit collects idea data from the generative AI contest. For example, it collects PDF files of ideas submitted in the generative AI contest and registers them in a database. The data collection unit also converts the content of the ideas into a format that makes it easy for the generative AI to analyze. Step 2: The data analysis unit analyzes the idea data collected by the data collection unit. For example, the generation AI analyzes the content of the ideas and registers them in a database. The data analysis unit can also perform sentiment analysis of the ideas, filtering them based on the intensity and type of sentiment before registering them in the database. Step 3: The proposal generation unit proposes the optimal idea in response to the user's instructions based on the data analyzed by the data analysis unit. For example, if the user requests, "Please propose a new marketing strategy," the proposal generation unit searches the database for related ideas and makes the optimal proposal. The proposal generation unit can also estimate the user's emotions and propose the optimal idea based on those emotions. Step 4: The plugin user uses the idea PDFs submitted in the AI ​​generation contest as an external source. For example, when obtaining information about a specific technology, the plugin user searches for related PDF files, analyzes their contents, and extracts the necessary information. The plugin user can also automatically summarize the contents of the PDF files and suggest optimal ideas based on the summarized information.

[0051] (Example 2) The autonomous thinking agent system according to an embodiment of the present invention is a system that uses the large number of ideas collected in the generative AI contest as a data source to develop an autonomous thinking agent using the function calling function of the generative AI. As a result, the autonomous thinking agent system can effectively utilize the large number of ideas collected in the generative AI contest and make optimal proposals to users.

[0052] The autonomous thinking agent system according to the embodiment includes a data collection unit, a data analysis unit, a proposal generation unit, and a plug-in usage unit. The data collection unit collects idea data from the AI ​​generation contest. For example, it collects PDF files of ideas submitted in the AI ​​generation contest and registers them in a database. The data collection unit also converts the idea content into a format that is easy for the AI ​​generation to analyze. The data analysis unit analyzes the idea data collected by the data collection unit. For example, the AI ​​generation analyzes the content of the ideas and registers them in the database. The data analysis unit can also perform sentiment analysis of the ideas, filter them based on the intensity and type of sentiment, and register them in the database. The proposal generation unit proposes optimal ideas in response to user instructions based on the data analyzed by the data analysis unit. For example, if a user requests, "Please propose a new marketing strategy," the proposal generation unit searches the database for related ideas and makes optimal proposals. The proposal generation unit can also estimate the user's sentiment and propose optimal ideas based on the sentiment. The plug-in usage unit uses PDF ideas submitted in the AI ​​generation contest as an external source. For example, when obtaining information about a specific technology, the system searches for related PDF files, analyzes their contents, and extracts the necessary information. The plug-in user unit can also automatically summarize the contents of the PDF files and propose optimal ideas based on the summarized information. This allows the autonomous thinking agent system according to the embodiment to effectively utilize the large number of ideas collected in the AI ​​generation contest and provide optimal proposals to users.

[0053] The data analysis unit can perform an emotional analysis of ideas and register them in the database by filtering them based on the intensity and type of emotion. For example, the data analysis unit performs an emotional analysis on each idea and quantifies the intensity and type of emotion based on the emotional score. For example, emotions such as joy, sadness, and surprise are expressed numerically, and ideas with high specific emotional scores are preferentially stored in the database. The data analysis unit also analyzes the user's emotional state in real time when submitting an idea and automatically filters and stores ideas with strong positive emotions. Specifically, it analyzes the user's facial expression and tone of voice when entering ideas and calculates an emotional score. The data analysis unit also uses emotional analysis to build a filtering system that eliminates ideas with negative emotions. For example, ideas with high scores for anger and sadness are not stored in the database, thereby collecting only positive ideas. In this way, emotional analysis can be used to preferentially register positive ideas in the database.

[0054] The data analysis unit can collect background information about the idea submitter, evaluate the idea based on that information, and register the idea in a database. The data analysis unit, for example, registers the expertise and industry experience of the idea submitter in a database and evaluates the idea based on that information. For example, it prioritizes saving ideas in fields in which the submitter has expertise. The data analysis unit also builds a system that evaluates the feasibility and market demand of ideas based on the submitter's industry experience. For example, it gives high marks to ideas submitted by submitters with many years of industry experience and stores them in the database. The data analysis unit also collects background information about the submitter, such as their educational background and work history, and evaluates the reliability of the idea based on that information. For example, it prioritizes saving ideas submitted by submitters with high educational backgrounds or professional qualifications. This allows ideas to be evaluated based on the submitter's background information, and highly reliable ideas to be registered in the database.

[0055] The data analysis unit can display feedback from other users on submitted ideas in real time to promote evaluation. For example, the data analysis unit adds a feedback function to the idea collection platform and displays other users' ratings and comments on submitted ideas in real time. This allows the quality and feasibility of the ideas to be evaluated. The data analysis unit also scores other users' feedback on submitted ideas and prioritizes the ideas based on the scores. Specifically, ideas with a lot of positive feedback are preferentially stored in the database. The data analysis unit also uses the feedback function to allow other users to post improvement suggestions and questions about the ideas. This promotes idea refinement and improves the quality when saved in the database. This allows the evaluation of ideas to be promoted by displaying feedback from other users in real time.

[0056] When collecting ideas, the data collection unit simultaneously collects multimodal information, including image and audio data, and can derive ideas from visual and audio information. For example, the data collection unit allows users to upload image and audio data when submitting ideas, and evaluates the ideas based on the visual and audio information. For example, prototype images and explanatory audio are used to grasp the details of the idea. The data collection unit also uses image recognition technology to extract the content of the idea from the uploaded image data and store it in a database. Specifically, it analyzes product design and sketch images to evaluate the concreteness of the idea. The data collection unit also uses audio analysis technology to automatically extract the main points of the idea from the submitted audio data and convert it into text data. This integrates the visual and audio information with the text data and stores it in a database. In this way, collecting multimodal information makes it possible to derive ideas from visual and audio information as well.

[0057] The data collection department can periodically hold workshops that bring together experts from different industries or fields and collect ideas that emerge there. For example, the data collection department can periodically hold workshops that invite experts from different industries and register the ideas that emerge there in a database. For example, a venue can be created where experts from technical, design, and marketing fields can gather together. The data collection department can also record discussions at the workshops and save them as audio data. The audio can then be analyzed to extract important ideas and opinions and save them in a database. The data collection department can also collect materials and presentation data used in the workshops and save them in a database as background information for the ideas. This makes the source and rationale of the ideas clear. By collecting ideas that emerge from workshops that bring together experts from different industries and fields, it is possible to obtain ideas from a variety of perspectives.

[0058] The data collection unit equips the idea collection platform with an emotion estimation function, which can estimate the user's emotions in real time when inputting ideas and make suggestions that elicit positive emotions. The data collection unit is equipped with a function that, for example, analyzes the user's facial expressions and voice when inputting ideas and estimates the user's emotions in real time. For example, it analyzes the user's emotions using a camera or microphone and makes positive suggestions if it detects negative emotions. The data collection unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user inputs ideas. For example, it presents encouraging messages and success stories. The data collection unit also provides feedback in real time based on the emotion estimation data when the user inputs ideas and offers advice that strengthens positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This makes it possible to collect better ideas by estimating the user's emotions in real time and making suggestions that elicit positive emotions.

[0059] The proposal generation unit can estimate the user's emotions and propose optimal ideas based on those emotions. For example, the proposal generation unit equips an autonomous thinking agent with an emotion estimation function and analyzes the user's emotional state in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The proposal generation unit also builds a system that proposes optimal ideas based on the user's emotions based on the emotion estimation results. For example, if the user has strong positive emotions, it proposes challenging ideas. The proposal generation unit also learns the user's emotional data and develops an algorithm that proposes optimal ideas based on the user's past emotional state and the success rate of proposals. For example, it analyzes the relationship between emotions and proposals based on past data. This makes it possible to improve user satisfaction by proposing optimal ideas based on the user's emotions.

[0060] The proposal generation unit can learn the user's past instruction history, understand the user's preferences and tendencies, and improve the accuracy of proposals. For example, the proposal generation unit constructs a system that understands the user's preferences and tendencies by having an autonomous thinking agent learn the user's past instruction history. For example, it analyzes the content of past instructions and identifies the user's preferences. The proposal generation unit also develops an algorithm that improves the accuracy of proposals based on the user's past instruction history. For example, it learns past success stories and failure stories and makes optimal proposals. The proposal generation unit also clusters the past instruction history and identifies similar instruction patterns to understand the user's preferences and tendencies. For example, it groups instructions related to specific themes or keywords. In this way, the proposal generation unit can improve the accuracy of proposals by learning the user's past instruction history.

[0061] The proposal generation unit can evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution. For example, the proposal generation unit adds a function to evaluate the feasibility of ideas proposed to an autonomous thinking agent. For example, it performs an evaluation based on technical feasibility and market demand. The proposal generation unit also builds a system that automatically generates the resources and procedures required for the execution of proposed ideas. For example, it automatically generates the necessary human resources, materials, and schedules. The proposal generation unit also develops an algorithm that determines the priority of proposed ideas based on the results of the feasibility evaluation. For example, it prioritizes ideas with high feasibility. This makes it possible to evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution, thereby improving the efficiency of execution.

[0062] The proposal generation unit can apply the autonomous thinking agent to different industries and applications to propose optimal ideas in each field. For example, the proposal generation unit applies the autonomous thinking agent to different industries to build a system that proposes optimal ideas in each field. For example, it can support fields such as medicine, education, and entertainment. The proposal generation unit also learns specialized knowledge in each field in order to apply it to different applications. For example, it adjusts the proposal algorithm based on data from a specific industry. The proposal generation unit also integrates databases from different industries to develop a system that makes comprehensive proposals in order to propose optimal ideas in each field. For example, it reflects trends and needs from different industries. In this way, the autonomous thinking agent can be applied to different industries and applications to propose optimal ideas in each field.

[0063] The proposal generation unit can add a function to visualize the proposed ideas, allowing the user to intuitively understand them. For example, the proposal generation unit adds a function to visualize the ideas proposed to the autonomous thinking agent. For example, the proposal generation unit visually displays the ideas using graphs, charts, or 3D models. The proposal generation unit also builds a system that automatically generates visual prototypes of proposed ideas so that the user can intuitively understand them. For example, it visually displays product designs or service flows. The proposal generation unit also develops an interface that allows the user to provide feedback based on the visualized ideas. For example, it collects user opinions based on the visual prototype. In this way, the proposed ideas can be visualized so that the user can intuitively understand them.

[0064] The proposal generation unit can add a function to determine the priority of proposals based on the user's emotions using the emotion estimation function. The proposal generation unit, for example, uses the emotion estimation function to build a system that determines the priority of proposals based on the user's emotions. For example, if the user has strong positive emotions, it prioritizes challenging ideas. The proposal generation unit also develops an algorithm that dynamically adjusts the priority of proposals based on the user's emotion data. For example, it prioritizes ideas with high emotion scores. The proposal generation unit also builds a system that makes optimal proposals based on the emotion estimation results. For example, if the user has strong negative emotions, it proposes relaxing ideas. In this way, by using the emotion estimation function to determine the priority of proposals based on the user's emotions, it is possible to make optimal proposals that meet the user's needs.

[0065] The plugin using unit can use the emotion estimation function to preferentially extract ideas with positive emotions when analyzing an idea PDF. For example, the plugin using unit builds a system that uses the emotion estimation function to preferentially extract ideas with positive emotions when analyzing an idea PDF. For example, ideas with high emotion scores are preferentially displayed. The plugin using unit also develops an algorithm that automatically filters ideas with positive emotions based on the emotion estimation results. For example, ideas with strong emotions of joy or surprise are preferentially extracted. The plugin using unit also builds a system that uses the emotion estimation function to preferentially store ideas with positive emotions in a database. For example, ideas with high emotion scores are automatically registered in the database. This makes it possible to efficiently collect high-quality ideas by preferentially extracting ideas with positive emotions using the emotion estimation function.

[0066] The plug-in using unit can add a function that automatically summarizes the contents of a PDF file and suggests the best idea based on the summarized information. The plug-in using unit, for example, builds a system that automatically summarizes the contents of a PDF file and suggests the best idea based on the summarized information. For example, it generates a summary and suggests related ideas. The plug-in using unit also develops an algorithm that suggests the best idea in response to a user's instructions based on the automatically summarized information. For example, it analyzes the summary and searches for related ideas. The plug-in using unit also builds a system that automatically summarizes the contents of a PDF file and updates a database based on the summary information. For example, it registers new ideas in the database based on the summary. This allows for efficient use of information by automatically summarizing the contents of a PDF file and suggesting the best idea based on the summary information.

[0067] The plug-in user section can integrate the contents of PDF files with other data sources to provide comprehensive information. For example, the plug-in user section can integrate the contents of PDF files with patent data to build a system that analyzes technological trends and competitive situations. For example, it can correlate and display patent information in the same technical field. The plug-in user section can also integrate the contents of PDF files with market data to develop a system that discovers new business opportunities. For example, it can extract ideas that meet specific market needs. The plug-in user section can also integrate the contents of PDF files with other data sources (e.g., academic paper databases) to build a system that keeps track of the latest research trends and technological innovations. For example, it can link related papers and research results. This allows the contents of PDF files to be integrated with other data sources to provide more comprehensive information.

[0068] The plugin user section automatically translates idea PDFs into different languages, enabling idea proposals from an international perspective. For example, the plugin user section uses an automatic translation function to build a system that translates idea PDFs into different languages ​​and enables idea proposals from an international perspective. For example, it translates into multiple languages, such as English, French, and Chinese. The plugin user section also develops a system that posts the translated idea PDFs to a multilingual idea collection platform and receives feedback from users around the world. The plugin user section also builds a system that collects advice and improvement suggestions from an international perspective based on the translated idea PDFs, thereby improving the quality of the ideas. For example, it reflects feedback that takes cultural and market differences into account. This allows idea proposals to be made from an international perspective by automatically translating idea PDFs into different languages.

[0069] The plugin usage section can convert the contents of a PDF file into a visual note or mind map, making it easier to understand visually. For example, the plugin usage section can convert the contents of a PDF file into a visual note and build a system that visually displays the key points of an idea. For example, it can indicate important points using diagrams or icons. The plugin usage section can also develop a system that converts the contents of a PDF file into a mind map format and visually organizes related keywords and concepts. This allows users to understand the overall picture of an idea at a glance. The plugin usage section can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display idea summaries. For example, it can provide a function to visualize summaries using drag and drop. This allows the contents of a PDF file to be converted into a visual note or mind map, making it easier to understand visually.

[0070] The plugin use unit uses the emotion estimation function to collect users' emotional reactions to the content of PDF files and improve the accuracy of suggestions based on that data. The plugin use unit, for example, collects users' emotional reactions to the content of PDF files in real time and builds a system that improves the accuracy of suggestions based on that data. For example, it prioritizes proposing ideas that receive a large number of positive reactions. The plugin use unit also uses the emotion estimation function to collect feedback on the content of PDF files and develops a system that regenerates suggestions if a large number of negative reactions are received. The plugin use unit also analyzes users' emotional reaction data and builds a system that identifies areas for improvement in suggestions based on the results. For example, it makes suggestions to correct parts with low emotion scores. This allows the emotion estimation function to collect users' emotional reactions to the content of PDF files and improve the accuracy of suggestions based on that feedback.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When collecting ideas, the data collection unit simultaneously collects multimodal information, including image and audio data, and can derive ideas from visual and audio information. For example, the data collection unit allows users to upload image and audio data when submitting ideas, and evaluates the ideas based on the visual and audio information. For example, prototype images and explanatory audio are used to grasp the details of the idea. The data collection unit also uses image recognition technology to extract the content of the idea from the uploaded image data and store it in a database. Specifically, it analyzes product design and sketch images to evaluate the concreteness of the idea. The data collection unit also uses audio analysis technology to automatically extract the main points of the idea from the submitted audio data and convert it into text data. This integrates the visual and audio information with the text data and stores it in a database. In this way, collecting multimodal information makes it possible to derive ideas from visual and audio information as well.

[0073] The data analysis unit can display feedback from other users on submitted ideas in real time to promote evaluation. For example, the data analysis unit adds a feedback function to the idea collection platform and displays other users' ratings and comments on submitted ideas in real time. This allows the quality and feasibility of the ideas to be evaluated. The data analysis unit also scores other users' feedback on submitted ideas and prioritizes the ideas based on the scores. Specifically, ideas with a lot of positive feedback are preferentially stored in the database. The data analysis unit also uses the feedback function to allow other users to post improvement suggestions and questions about the ideas. This promotes idea refinement and improves the quality when saved in the database. This allows the evaluation of ideas to be promoted by displaying feedback from other users in real time.

[0074] The data collection department can periodically hold workshops that bring together experts from different industries or fields and collect ideas that emerge there. For example, the data collection department can periodically hold workshops that invite experts from different industries and register the ideas that emerge there in a database. For example, a venue can be created where experts from technical, design, and marketing fields can gather together. The data collection department can also record discussions at the workshops and save them as audio data. The audio can then be analyzed to extract important ideas and opinions and save them in a database. The data collection department can also collect materials and presentation data used in the workshops and save them in a database as background information for the ideas. This makes the source and rationale of the ideas clear. By collecting ideas that emerge from workshops that bring together experts from different industries and fields, it is possible to obtain ideas from a variety of perspectives.

[0075] The proposal generation unit can learn the user's past instruction history, understand the user's preferences and tendencies, and improve the accuracy of proposals. For example, the proposal generation unit constructs a system that understands the user's preferences and tendencies by having an autonomous thinking agent learn the user's past instruction history. For example, it analyzes the content of past instructions and identifies the user's preferences. The proposal generation unit also develops an algorithm that improves the accuracy of proposals based on the user's past instruction history. For example, it learns past success stories and failure stories and makes optimal proposals. The proposal generation unit also clusters the past instruction history and identifies similar instruction patterns to understand the user's preferences and tendencies. For example, it groups instructions related to specific themes or keywords. In this way, the proposal generation unit can improve the accuracy of proposals by learning the user's past instruction history.

[0076] The proposal generation unit can evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution. For example, the proposal generation unit adds a function to evaluate the feasibility of ideas proposed to an autonomous thinking agent. For example, it performs an evaluation based on technical feasibility and market demand. The proposal generation unit also builds a system that automatically generates the resources and procedures required for the execution of proposed ideas. For example, it automatically generates the necessary human resources, materials, and schedules. The proposal generation unit also develops an algorithm that determines the priority of proposed ideas based on the results of the feasibility evaluation. For example, it prioritizes ideas with high feasibility. This makes it possible to evaluate the feasibility of proposed ideas and automatically generate the resources and procedures required for their execution, thereby improving the efficiency of execution.

[0077] The data collection unit equips the idea collection platform with an emotion estimation function, which can estimate the user's emotions in real time when inputting ideas and make suggestions that elicit positive emotions. The data collection unit is equipped with a function that, for example, analyzes the user's facial expressions and voice when inputting ideas and estimates the user's emotions in real time. For example, it analyzes the user's emotions using a camera or microphone and makes positive suggestions if it detects negative emotions. The data collection unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user inputs ideas. For example, it presents encouraging messages and success stories. The data collection unit also provides feedback in real time based on the emotion estimation data when the user inputs ideas and offers advice that strengthens positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This makes it possible to collect better ideas by estimating the user's emotions in real time and making suggestions that elicit positive emotions.

[0078] The proposal generation unit can estimate the user's emotions and propose optimal ideas based on those emotions. For example, the proposal generation unit equips an autonomous thinking agent with an emotion estimation function and analyzes the user's emotional state in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The proposal generation unit also builds a system that proposes optimal ideas based on the user's emotions based on the emotion estimation results. For example, if the user has strong positive emotions, it proposes challenging ideas. The proposal generation unit also learns the user's emotional data and develops an algorithm that proposes optimal ideas based on the user's past emotional state and the success rate of proposals. For example, it analyzes the relationship between emotions and proposals based on past data. This makes it possible to improve user satisfaction by proposing optimal ideas based on the user's emotions.

[0079] The proposal generation unit can add a function to determine the priority of proposals based on the user's emotions using the emotion estimation function. The proposal generation unit, for example, uses the emotion estimation function to build a system that determines the priority of proposals based on the user's emotions. For example, if the user has strong positive emotions, it prioritizes challenging ideas. The proposal generation unit also develops an algorithm that dynamically adjusts the priority of proposals based on the user's emotion data. For example, it prioritizes ideas with high emotion scores. The proposal generation unit also builds a system that makes optimal proposals based on the emotion estimation results. For example, if the user has strong negative emotions, it proposes relaxing ideas. In this way, by using the emotion estimation function to determine the priority of proposals based on the user's emotions, it is possible to make optimal proposals that meet the user's needs.

[0080] The plugin using unit can use the emotion estimation function to preferentially extract ideas with positive emotions when analyzing an idea PDF. For example, the plugin using unit builds a system that uses the emotion estimation function to preferentially extract ideas with positive emotions when analyzing an idea PDF. For example, ideas with high emotion scores are preferentially displayed. The plugin using unit also develops an algorithm that automatically filters ideas with positive emotions based on the emotion estimation results. For example, ideas with strong emotions of joy or surprise are preferentially extracted. The plugin using unit also builds a system that uses the emotion estimation function to preferentially store ideas with positive emotions in a database. For example, ideas with high emotion scores are automatically registered in the database. This makes it possible to efficiently collect high-quality ideas by preferentially extracting ideas with positive emotions using the emotion estimation function.

[0081] The plugin usage section can convert the contents of a PDF file into a visual note or mind map, making it easier to understand visually. For example, the plugin usage section can convert the contents of a PDF file into a visual note and build a system that visually displays the key points of an idea. For example, it can indicate important points using diagrams or icons. The plugin usage section can also develop a system that converts the contents of a PDF file into a mind map format and visually organizes related keywords and concepts. This allows users to understand the overall picture of an idea at a glance. The plugin usage section can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display idea summaries. For example, it can provide a function to visualize summaries using drag and drop. This allows the contents of a PDF file to be converted into a visual note or mind map, making it easier to understand visually.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection unit collects idea data from the generative AI contest. For example, it collects PDF files of ideas submitted in the generative AI contest and registers them in a database. The data collection unit also converts the content of the ideas into a format that makes it easy for the generative AI to analyze. Step 2: The data analysis unit analyzes the idea data collected by the data collection unit. For example, the generation AI analyzes the content of the ideas and registers them in a database. The data analysis unit can also perform sentiment analysis of the ideas, filtering them based on the intensity and type of sentiment before registering them in the database. Step 3: The proposal generation unit proposes the optimal idea in response to the user's instructions based on the data analyzed by the data analysis unit. For example, if the user requests, "Please propose a new marketing strategy," the proposal generation unit searches the database for related ideas and makes the optimal proposal. The proposal generation unit can also estimate the user's emotions and propose the optimal idea based on those emotions. Step 4: The plugin user uses the idea PDFs submitted in the AI ​​generation contest as an external source. For example, when obtaining information about a specific technology, the plugin user searches for related PDF files, analyzes their contents, and extracts the necessary information. The plugin user can also automatically summarize the contents of the PDF files and suggest optimal ideas based on the summarized information.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. This is a system that uses multiple ideas collected in the Generative AI Contest as a data source to develop an autonomous thinking agent using the Function Calling function of Generative AI. A data collection department that collects idea data for the generative AI contest; a data analysis unit that analyzes the idea data collected by the data collection unit; a proposal generation unit that proposes optimal ideas in response to user instructions based on the data analyzed by the data analysis unit; A plug-in using unit that uses the idea pdf submitted in the generation AI contest as an external source. A system characterized by:

2. The data analysis unit Sentiment analysis of the ideas is performed, and the ideas are filtered based on the intensity and type of emotion before being registered in a database.

2. The system of claim 1.

3. The data analysis unit Collect background information on the person who submitted the idea, evaluate the idea based on that information, and register the idea in a database.

2. The system of claim 1.

4. The data analysis unit Displaying feedback from other users on the submitted idea in real time to encourage evaluation 2. The system of claim 1.

5. The data collection unit When collecting ideas, simultaneously collect multimodal information, including image or audio data, and derive ideas from visual and audio information.

2. The system of claim 1.

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