System

Abstract

An object of a system according to an embodiment is to evaluate an idea of a user and connect the idea to a business.SOLUTION: A system includes an idea evaluation part, a purchase part, a generation part, a sales part, an evidence creation part, and a notification part. The idea evaluation unit evaluates an idea from a user. The purchase part purchases the idea evaluated by the idea evaluation part. The generation unit analyzes big data of the idea bought by the buying unit to create a new business idea. The sales department sells the business idea created by the creation department to the company. The evidence creation unit creates evidence required by the company. The notification unit notifies an inventor having a specific idea.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 technologies do not have a well-established process for effectively evaluating users' ideas and linking them to business, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate users' ideas and connect them to business. [Means for solving the problem]

[0006] The system according to the embodiment includes an idea evaluation unit, a purchasing unit, a generation unit, a sales unit, an evidence creation unit, and a notification unit. The idea evaluation unit evaluates ideas submitted by users. The purchasing unit purchases the ideas evaluated by the idea evaluation unit. The generation unit analyzes big data of the ideas purchased by the purchasing unit to create new business ideas. The sales unit sells the business ideas created by the generation unit to companies. The evidence creation unit creates evidence required by companies. The notification unit notifies the originators of specific ideas. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the ideas of users and connect them to business. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the idea evaluation system according to an embodiment of the present invention, AI evaluates and purchases ideas submitted by users, and then a generation AI creates new business ideas based on the big data and sells them to companies. This idea evaluation system can improve corporate value, increase creativity and employment flexibility in society as a whole.

[0029] The idea evaluation system according to the embodiment includes an idea evaluation unit, a purchasing unit, a generation unit, a sales unit, an evidence creation unit, and a notification unit. The idea evaluation unit evaluates ideas submitted by users. For example, AI evaluates the market value and technical feasibility of ideas submitted by users. The purchasing unit purchases the ideas evaluated by the idea evaluation unit. For example, AI purchases the ideas at an appropriate price. The generation unit creates new business ideas by analyzing big data of the ideas purchased by the purchasing unit. For example, the generation AI proposes new business models based on ideas submitted by users, taking into account technical feasibility and market needs. The sales unit sells the business ideas created by the generation unit to companies. For example, the business ideas proposed by the generation AI are sold to companies, and the companies obtain patents and commercialize the ideas. The evidence creation unit creates evidence required by companies. For example, the generation AI creates and conducts surveys to provide market demand and consumer opinions. The notification unit notifies the originators of specific ideas. For example, the notification unit notifies the originators of idea purchase companies and, if requested, offers job-based employment to the originators. In this way, the idea evaluation system can improve corporate value and increase creativity and employment flexibility throughout society.

[0030] The idea evaluation unit can refer to past successes or failures and evaluate based on similarity. For example, the idea evaluation unit uses AI to refer to past successes and failures in a database and analyze the similarity with the submitted idea. For example, if the submitted idea is similar to a past successful idea, it will be given a high rating. In this way, by referring to past successes and failures, the accuracy of idea evaluation can be improved.

[0031] The idea evaluation unit can improve the accuracy of evaluation by taking into account the submitter's expertise or experience. For example, the idea evaluation unit registers the submitter's expertise and experience in a database and evaluates ideas based on that information. For example, ideas submitted by submitters with extensive expertise are given a high rating. In this way, the accuracy of idea evaluation is improved by taking into account the submitter's expertise and experience.

[0032] The idea evaluation department can incorporate evaluations by experts from different industries and conduct evaluations from multiple perspectives. For example, the idea evaluation department can form an evaluation team that brings together experts from different industries and evaluate submitted ideas from multiple perspectives. For example, experts in technology, design, and marketing can jointly conduct the evaluation. In this way, by incorporating evaluations by experts from different industries, the accuracy of idea evaluations can be improved.

[0033] The idea evaluation unit can improve the quality of the user's idea by feeding back the evaluation results to the user and suggesting improvements. The idea evaluation unit, for example, constructs a system that feeds back the evaluation results of ideas to the user and suggests specific improvements. For example, it provides advice on technical improvements and market needs. In this way, the quality of the user's idea is improved by feeding back the evaluation results and suggesting improvements.

[0034] When creating ideas, the generation unit can integrate different data sources and generate ideas from a more multifaceted perspective. For example, when the generative AI creates ideas, the generation unit can integrate different data sources such as social media and news articles to generate ideas from a multifaceted perspective. For example, it can reflect the latest trends and user opinions. In this way, by integrating different data sources, ideas can be generated from a more multifaceted perspective.

[0035] When creating ideas, the generation unit can refer to past patent data and market data and take into account technical feasibility or market needs. For example, when the generative AI creates ideas, the generation unit can refer to past patent data and market data and take into account technical feasibility and market needs. For example, it can evaluate technical feasibility based on a patent database. In this way, by referring to past patent data and market data, it is possible to create ideas that take into account technical feasibility and market needs.

[0036] The generation unit can apply the ideas it creates to different industries or applications to discover new business opportunities. For example, the generation unit builds a system that applies the ideas created by the generative AI to different industries or applications to discover new business opportunities. For example, it generates ideas that combine the technology field with the consumer market. This allows new business opportunities to be discovered by applying them to different industries or applications.

[0037] The generation unit can implement the created ideas as prototypes and introduce agile methods to improve them based on user feedback. For example, the generation unit can implement the ideas created by the generative AI as prototypes and introduce agile methods to improve them based on user feedback. For example, a prototype can be developed in a short period of time and user opinions can be reflected. This improves the quality of the ideas by implementing the prototype and improving it based on user feedback.

[0038] In the idea sales process, the sales department can analyze past sales data, extract successful patterns, and optimize the content of proposals. For example, the sales department can analyze past idea sales data and build a system that extracts successful patterns. For example, the content of proposals can be optimized based on the characteristics of successful sales. In this way, the content of proposals can be optimized by analyzing past sales data and extracting successful patterns.

[0039] The sales department can automate the patent acquisition procedures or legal advice when selling an idea, thereby reducing the burden on the company. For example, the sales department can build a system that automates the patent acquisition procedures when selling an idea. For example, it can automatically generate and submit patent application documents. This can reduce the burden on the company by automating the patent acquisition procedures and legal advice.

[0040] When selling ideas, the sales department can make proposals to companies in different industries or regions, thereby expanding global business opportunities. For example, the sales department can build a system that allows them to make proposals to companies in different industries or regions when selling ideas. For example, they can make proposals that meet global market needs. This allows them to make proposals to companies in different industries or regions, thereby expanding global business opportunities.

[0041] The sales department can collect feedback from companies as a follow-up after selling an idea and reflect it in the next proposal. For example, the sales department can build a system to collect feedback from companies as a follow-up after selling an idea. For example, they can conduct online surveys or interviews. In this way, they can collect feedback from companies and reflect it in the next proposal, thereby optimizing the content of the proposal.

[0042] When creating evidence, the evidence creation unit can integrate different data sources and generate evidence from a more multifaceted perspective. For example, when the generation AI creates evidence, the evidence creation unit integrates different data sources such as social media and news articles to generate evidence from a multifaceted perspective. For example, it can reflect the latest trends and user opinions. In this way, by integrating different data sources, evidence can be generated from a more multifaceted perspective.

[0043] The evidence creation unit can provide highly reliable evidence by referencing past research data and market data during the evidence creation process. For example, when the generation AI creates evidence, the evidence creation unit can provide highly reliable evidence by referencing past research data and market data. For example, evidence is generated based on past data. In this way, highly reliable evidence can be provided by referencing past research data and market data.

[0044] The evidence creation department can apply evidence to different industries or applications to discover new business opportunities. For example, the evidence creation department will build a system that applies evidence created by the generative AI to different industries or applications to discover new business opportunities. For example, it will generate evidence that combines the technology field with the consumer market. This will enable the discovery of new business opportunities by applying it to different industries or applications.

[0045] The evidence creation department can collect feedback from companies as a follow-up after evidence creation and reflect it in the next evidence creation. For example, the evidence creation department can build a system to collect feedback from companies as a follow-up after evidence creation. For example, they can conduct online surveys and interviews. In this way, feedback from companies can be collected and reflected in the next evidence creation, improving the quality of the evidence.

[0046] The notification unit can optimize matching with companies in the job-based employment process by taking into account the past achievements or skill set of the proposer. For example, the notification unit registers the past achievements and skill set of the proposer in a database and builds a system that optimizes matching with companies based on that information. For example, it provides employment opportunities according to the proposer's skills. This makes it possible to optimize matching with companies by taking into account the past achievements and skill set of the proposer.

[0047] The notification unit can suggest flexible working styles such as remote work or flextime when employing a job-type employee, thereby improving the working environment for the person who proposed the proposal. The notification unit, for example, builds a system that suggests flexible working styles such as remote work or flextime when employing a job-type employee. For example, it provides a working style that suits the lifestyle of the person who proposed the proposal. This improves the working environment for the person who proposed the proposal by suggesting flexible working styles such as remote work or flextime.

[0048] The notification department can provide job-type employment opportunities to different industries or regions, thereby expanding global employment opportunities. The notification department, for example, builds a system that provides job-type employment opportunities to different industries and regions. For example, it provides employment opportunities that meet global market needs. This makes it possible to expand global employment opportunities by providing job-type employment opportunities to different industries and regions.

[0049] The notification department can collect feedback from the proposer and the company as a follow-up after job-type employment and reflect it in the next employment opportunity. The notification department, for example, builds a system for collecting feedback from the proposer and the company as a follow-up after job-type employment. For example, it conducts online questionnaires and interviews. In this way, feedback from the proposer and the company can be collected and reflected in the next employment opportunity, thereby improving the quality of employment opportunities.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The idea evaluation unit can evaluate the environmental impact of ideas submitted by users. For example, AI can analyze the carbon dioxide emissions and energy consumption associated with implementing an idea and quantify the environmental impact. This allows environmentally friendly ideas to be given a high rating. Based on the evaluation results, it can also make improvement proposals to reduce the environmental impact. Furthermore, the results of the environmental evaluation can be provided to companies, allowing them to use them as reference information for building sustainable business models.

[0052] The idea evaluation department can evaluate the social impact of submitted ideas. For example, AI can analyze the social benefits and risks of realizing an idea and quantify the social impact. This allows socially beneficial ideas to be given a high rating. Based on the evaluation results, it can also make improvement proposals to reduce social risks. Furthermore, the social impact evaluation results can be provided to companies as reference information for them to fulfill their social responsibilities.

[0053] The idea evaluation department can evaluate the ethical aspects of submitted ideas. For example, AI can analyze ethical issues associated with realizing an idea and quantify the ethical evaluation. This allows ideas that are ethically sound to be given a high rating. Based on the evaluation results, it can also make improvement proposals to resolve ethical issues. Furthermore, the results of the ethical evaluation can be provided to companies, allowing them to use them as reference information for building ethically sound business models.

[0054] The idea evaluation department can evaluate the economic impact of submitted ideas. For example, AI can analyze the economic benefits and risks of realizing an idea and quantify the economic impact. This allows economically beneficial ideas to be given a high rating. Based on the evaluation results, it can also make improvement suggestions to reduce economic risks. Furthermore, the results of the economic impact evaluation can be provided to companies, allowing them to use them as reference information for building economically sound business models.

[0055] The idea evaluation department can evaluate the technical feasibility of submitted ideas. For example, AI can analyze the technical requirements necessary to realize an idea and quantify its technical feasibility. This allows technically feasible ideas to be given a high rating. Based on the evaluation results, it can also propose improvements to solve technical problems. Furthermore, the results of the technical feasibility evaluation can be provided to companies, who can use them as reference information to build technically feasible business models.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The idea evaluation unit evaluates ideas submitted by users. For example, AI evaluates the market value and technical feasibility of ideas submitted by users. Step 2: The purchasing department purchases the ideas evaluated by the idea evaluation department. For example, AI purchases the ideas at an appropriate price. Step 3: The Generation Department analyzes the big data of ideas purchased by the Purchase Department to create new business ideas. For example, the Generation AI proposes new business models based on ideas submitted by users, taking into account technical feasibility and market needs. Step 4: The sales department sells the business ideas created by the generation department to companies. For example, the business ideas proposed by the generation AI are sold to companies, and the companies acquire patent rights and commercialize them. Step 5: The evidence creation department creates the evidence required by the company. For example, the generation AI creates and conducts surveys to provide market demand and consumer opinions. Step 6: The notification department notifies the originator of a specific idea. For example, the originator may notify an idea-buying company, and if the company requests it, the originator may be offered job-based employment.

[0058] (Example 2) In the idea evaluation system according to an embodiment of the present invention, AI evaluates and purchases ideas submitted by users, and then a generation AI creates new business ideas based on the big data and sells them to companies. This idea evaluation system can improve corporate value, increase creativity and employment flexibility in society as a whole.

[0059] The idea evaluation system according to the embodiment includes an idea evaluation unit, a purchasing unit, a generation unit, a sales unit, an evidence creation unit, and a notification unit. The idea evaluation unit evaluates ideas submitted by users. For example, AI evaluates the market value and technical feasibility of ideas submitted by users. The purchasing unit purchases the ideas evaluated by the idea evaluation unit. For example, AI purchases the ideas at an appropriate price. The generation unit creates new business ideas by analyzing big data of the ideas purchased by the purchasing unit. For example, the generation AI proposes new business models based on ideas submitted by users, taking into account technical feasibility and market needs. The sales unit sells the business ideas created by the generation unit to companies. For example, the business ideas proposed by the generation AI are sold to companies, and the companies obtain patents and commercialize the ideas. The evidence creation unit creates evidence required by companies. For example, the generation AI creates and conducts surveys to provide market demand and consumer opinions. The notification unit notifies the originators of specific ideas. For example, the notification unit notifies the originators of idea purchase companies and, if requested, offers job-based employment to the originators. In this way, the idea evaluation system can improve corporate value and increase creativity and employment flexibility throughout society.

[0060] The idea evaluation unit can use the emotion estimation function to add the user's passion or interest to the evaluation criteria. For example, when the AI ​​evaluates an idea, the idea evaluation unit analyzes the emotional data at the time of submission by the user and quantifies the passion or interest. For example, it analyzes the user's facial expression and tone of voice when entering ideas and calculates an emotion score. By adding the user's passion and interest to the evaluation criteria, more valuable ideas can be evaluated.

[0061] The idea evaluation unit can refer to past successes or failures and evaluate based on similarity. For example, the idea evaluation unit uses AI to refer to past successes and failures in a database and analyze the similarity with the submitted idea. For example, if the submitted idea is similar to a past successful idea, it will be given a high rating. In this way, by referring to past successes and failures, the accuracy of idea evaluation can be improved.

[0062] The idea evaluation unit can improve the accuracy of evaluation by taking into account the submitter's expertise or experience. For example, the idea evaluation unit registers the submitter's expertise and experience in a database and evaluates ideas based on that information. For example, ideas submitted by submitters with extensive expertise are given a high rating. In this way, the accuracy of idea evaluation is improved by taking into account the submitter's expertise and experience.

[0063] The idea evaluation department can incorporate evaluations by experts from different industries and conduct evaluations from multiple perspectives. For example, the idea evaluation department can form an evaluation team that brings together experts from different industries and evaluate submitted ideas from multiple perspectives. For example, experts in technology, design, and marketing can jointly conduct the evaluation. In this way, by incorporating evaluations by experts from different industries, the accuracy of idea evaluations can be improved.

[0064] The idea evaluation unit can improve the quality of the user's idea by feeding back the evaluation results to the user and suggesting improvements. The idea evaluation unit, for example, constructs a system that feeds back the evaluation results of ideas to the user and suggests specific improvements. For example, it provides advice on technical improvements and market needs. In this way, the quality of the user's idea is improved by feeding back the evaluation results and suggesting improvements.

[0065] The idea evaluation unit uses an emotion estimation function to analyze the emotions of users when they submit ideas in real time and make suggestions that elicit positive emotions. The idea evaluation unit is equipped with a function that analyzes the user's facial expressions and voice when they submit ideas and estimates their emotions in real time. For example, the unit analyzes the user's emotions using a camera or microphone and makes positive suggestions if it detects negative emotions. This allows the unit to analyze the user's emotions in real time and elicit positive emotions, thereby improving the quality of ideas.

[0066] When analyzing big data, the generation unit can use an emotion estimation function to take into account the user's emotional data and prioritize the creation of ideas that are likely to resonate emotionally. For example, when the generation AI analyzes big data, the generation unit takes into account the user's emotional data and prioritizes the creation of ideas that are likely to resonate emotionally. For example, it prioritizes the suggestion of ideas that evoke strong positive emotions. In this way, by taking into account the emotional data, it is possible to prioritize the creation of ideas that are likely to resonate emotionally.

[0067] When creating ideas, the generation unit can integrate different data sources and generate ideas from a more multifaceted perspective. For example, when the generative AI creates ideas, the generation unit can integrate different data sources such as social media and news articles to generate ideas from a multifaceted perspective. For example, it can reflect the latest trends and user opinions. In this way, by integrating different data sources, ideas can be generated from a more multifaceted perspective.

[0068] When creating ideas, the generation unit can refer to past patent data and market data and take into account technical feasibility or market needs. For example, when the generative AI creates ideas, the generation unit can refer to past patent data and market data and take into account technical feasibility and market needs. For example, it can evaluate technical feasibility based on a patent database. In this way, by referring to past patent data and market data, it is possible to create ideas that take into account technical feasibility and market needs.

[0069] The generation unit can apply the ideas it creates to different industries or applications to discover new business opportunities. For example, the generation unit builds a system that applies the ideas created by the generative AI to different industries or applications to discover new business opportunities. For example, it generates ideas that combine the technology field with the consumer market. This allows new business opportunities to be discovered by applying them to different industries or applications.

[0070] The generation unit can implement the created ideas as prototypes and introduce agile methods to improve them based on user feedback. For example, the generation unit can implement the ideas created by the generative AI as prototypes and introduce agile methods to improve them based on user feedback. For example, a prototype can be developed in a short period of time and user opinions can be reflected. This improves the quality of the ideas by implementing the prototype and improving it based on user feedback.

[0071] The generation unit uses the emotion estimation function to monitor the user's emotional response to the ideas created in real time, and can continuously search for optimal ideas. The generation unit will develop a system that uses the emotion estimation function to monitor the user's emotional response to ideas created by the generation AI in real time. For example, it will analyze the user's facial expressions and voice and calculate an emotional score. This will enable the system to continuously search for optimal ideas by monitoring the user's emotional response in real time.

[0072] When selling an idea to a company, the sales department can use the emotion estimation function to evaluate the company's level of interest or expectations and make the most appropriate proposal. For example, the sales department can use the emotion estimation function to analyze the facial expressions and voice of the company's representative and quantify the level of interest and expectations. For example, the sales department can analyze reactions during a presentation in real time and adjust the content of the proposal. This allows the sales department to evaluate the company's level of interest and expectations and make the most appropriate proposal.

[0073] In the idea sales process, the sales department can analyze past sales data, extract successful patterns, and optimize the content of proposals. For example, the sales department can analyze past idea sales data and build a system that extracts successful patterns. For example, the content of proposals can be optimized based on the characteristics of successful sales. In this way, the content of proposals can be optimized by analyzing past sales data and extracting successful patterns.

[0074] The sales department can automate the patent acquisition procedures or legal advice when selling an idea, thereby reducing the burden on the company. For example, the sales department can build a system that automates the patent acquisition procedures when selling an idea. For example, it can automatically generate and submit patent application documents. This can reduce the burden on the company by automating the patent acquisition procedures and legal advice.

[0075] When selling ideas, the sales department can make proposals to companies in different industries or regions, thereby expanding global business opportunities. For example, the sales department can build a system that allows them to make proposals to companies in different industries or regions when selling ideas. For example, they can make proposals that meet global market needs. This allows them to make proposals to companies in different industries or regions, thereby expanding global business opportunities.

[0076] The sales department can collect feedback from companies as a follow-up after selling an idea and reflect it in the next proposal. For example, the sales department can build a system to collect feedback from companies as a follow-up after selling an idea. For example, they can conduct online surveys or interviews. In this way, they can collect feedback from companies and reflect it in the next proposal, thereby optimizing the content of the proposal.

[0077] The sales department can use the emotion estimation function to identify the idea that the company is most interested in and strengthen its proposal for that idea. For example, the sales department can use the emotion estimation function to analyze the facial expressions and voice of the company's representative to identify the idea that the company is most interested in. For example, the sales department can analyze reactions during a presentation in real time and adjust the content of the proposal. In this way, the success rate of the proposal can be improved by identifying the idea that the company is most interested in and strengthening the proposal for that idea.

[0078] When creating evidence, the evidence creation unit uses an emotion estimation function to collect user emotion data and provide evidence that is easily emotionally relatable. For example, when the generation AI creates evidence, the evidence creation unit collects user emotion data and provides evidence that is easily emotionally relatable. For example, data with strong positive emotions is used preferentially. In this way, by collecting emotion data, it is possible to provide evidence that is easily emotionally relatable.

[0079] When creating evidence, the evidence creation unit can integrate different data sources and generate evidence from a more multifaceted perspective. For example, when the generation AI creates evidence, the evidence creation unit integrates different data sources such as social media and news articles to generate evidence from a multifaceted perspective. For example, it can reflect the latest trends and user opinions. In this way, by integrating different data sources, evidence can be generated from a more multifaceted perspective.

[0080] The evidence creation unit can provide highly reliable evidence by referencing past research data and market data during the evidence creation process. For example, when the generation AI creates evidence, the evidence creation unit can provide highly reliable evidence by referencing past research data and market data. For example, evidence is generated based on past data. In this way, highly reliable evidence can be provided by referencing past research data and market data.

[0081] The evidence creation department can apply evidence to different industries or applications to discover new business opportunities. For example, the evidence creation department will build a system that applies evidence created by the generative AI to different industries or applications to discover new business opportunities. For example, it will generate evidence that combines the technology field with the consumer market. This will enable the discovery of new business opportunities by applying it to different industries or applications.

[0082] The evidence creation department can collect feedback from companies as a follow-up after evidence creation and reflect it in the next evidence creation. For example, the evidence creation department can build a system to collect feedback from companies as a follow-up after evidence creation. For example, they can conduct online surveys and interviews. In this way, feedback from companies can be collected and reflected in the next evidence creation, improving the quality of the evidence.

[0083] The evidence creation unit uses the emotion estimation function to monitor the user's emotional response to evidence in real time, and can continuously provide optimal evidence. The evidence creation unit develops a system that uses the emotion estimation function to monitor the user's emotional response to evidence in real time, for example, by analyzing the user's facial expressions and voice and calculating an emotion score. This makes it possible to continuously provide optimal evidence by monitoring the user's emotional response in real time.

[0084] The notification unit can use the emotion estimation function to evaluate the motivation or aptitude of the proposer when hiring the proposer for job-based employment, and provide optimal employment opportunities. The notification unit, for example, uses the emotion estimation function to build a system for evaluating the motivation and aptitude of the proposer. For example, the notification unit analyzes the facial expression and voice of the proposer and calculates an emotion score. This allows the proposalr's motivation and aptitude to be evaluated, and optimal employment opportunities to be provided.

[0085] The notification unit can optimize matching with companies in the job-based employment process by taking into account the past achievements or skill set of the proposer. For example, the notification unit registers the past achievements and skill set of the proposer in a database and builds a system that optimizes matching with companies based on that information. For example, it provides employment opportunities according to the proposer's skills. This makes it possible to optimize matching with companies by taking into account the past achievements and skill set of the proposer.

[0086] The notification unit can suggest flexible working styles such as remote work or flextime when employing a job-type employee, thereby improving the working environment for the person who proposed the proposal. The notification unit, for example, builds a system that suggests flexible working styles such as remote work or flextime when employing a job-type employee. For example, it provides a working style that suits the lifestyle of the person who proposed the proposal. This improves the working environment for the person who proposed the proposal by suggesting flexible working styles such as remote work or flextime.

[0087] The notification department can provide job-type employment opportunities to different industries or regions, thereby expanding global employment opportunities. The notification department, for example, builds a system that provides job-type employment opportunities to different industries and regions. For example, it provides employment opportunities that meet global market needs. This makes it possible to expand global employment opportunities by providing job-type employment opportunities to different industries and regions.

[0088] The notification department can collect feedback from the proposer and the company as a follow-up after job-type employment and reflect it in the next employment opportunity. The notification department, for example, builds a system for collecting feedback from the proposer and the company as a follow-up after job-type employment. For example, it conducts online questionnaires and interviews. In this way, feedback from the proposer and the company can be collected and reflected in the next employment opportunity, thereby improving the quality of employment opportunities.

[0089] The notification unit can use the emotion estimation function to identify the employment opportunity that the proposer is most interested in and strengthen the proposal for that opportunity. For example, the notification unit uses the emotion estimation function to analyze the facial expressions and voice of the proposer to identify the employment opportunity that the proposer is most interested in. For example, the notification unit analyzes reactions during an interview in real time and adjusts the proposal content. In this way, the employment opportunity that the proposer is most interested in can be identified and the proposal for that opportunity can be strengthened, thereby improving the success rate of hiring.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The idea evaluation unit can evaluate the environmental impact of ideas submitted by users. For example, AI can analyze the carbon dioxide emissions and energy consumption associated with implementing an idea and quantify the environmental impact. This allows environmentally friendly ideas to be given a high rating. Based on the evaluation results, it can also make improvement proposals to reduce the environmental impact. Furthermore, the results of the environmental evaluation can be provided to companies, allowing them to use them as reference information for building sustainable business models.

[0092] The idea evaluation unit can use the emotion estimation function to add the user's stress level to the evaluation criteria. For example, AI can analyze the user's facial expressions and voice when submitting an idea and quantify the stress level. This allows ideas submitted when the user's stress level is low to be given a higher rating. In addition, if the stress level is high, suggestions for relaxation can be made. Furthermore, based on the stress level data, it can also identify the best time to submit an idea when the user is in the most creative state.

[0093] The idea evaluation department can evaluate the social impact of submitted ideas. For example, AI can analyze the social benefits and risks of realizing an idea and quantify the social impact. This allows socially beneficial ideas to be given a high rating. Based on the evaluation results, it can also make improvement proposals to reduce social risks. Furthermore, the social impact evaluation results can be provided to companies as reference information for them to fulfill their social responsibilities.

[0094] The idea evaluation unit can use emotion estimation functionality to add user motivation to the evaluation criteria. For example, AI can analyze the user's facial expressions and voice when submitting an idea and quantify their motivation. This allows ideas submitted at a high level of motivation to be given a high rating. In addition, if motivation is low, suggestions can be made to improve motivation. Furthermore, based on motivation data, it can also identify the best time to submit an idea when the user is most motivated.

[0095] The idea evaluation department can evaluate the ethical aspects of submitted ideas. For example, AI can analyze ethical issues associated with realizing an idea and quantify the ethical evaluation. This allows ideas that are ethically sound to be given a high rating. Based on the evaluation results, it can also make improvement proposals to resolve ethical issues. Furthermore, the results of the ethical evaluation can be provided to companies, allowing them to use them as reference information for building ethically sound business models.

[0096] The idea evaluation unit can use emotion estimation functionality to add a user's creativity to the evaluation criteria. For example, AI can analyze the user's facial expressions and voice when submitting an idea and quantify the creativity. This allows ideas submitted with high creativity to be given a high rating. In addition, if creativity is low, suggestions can be made to improve creativity. Furthermore, based on creativity data, it can also identify the best time to submit an idea when the user is at their most creative.

[0097] The idea evaluation department can evaluate the economic impact of submitted ideas. For example, AI can analyze the economic benefits and risks of realizing an idea and quantify the economic impact. This allows economically beneficial ideas to be given a high rating. Based on the evaluation results, it can also make improvement suggestions to reduce economic risks. Furthermore, the results of the economic impact evaluation can be provided to companies, allowing them to use them as reference information for building economically sound business models.

[0098] The idea evaluation unit can use emotion estimation functionality to add user satisfaction to the evaluation criteria. For example, AI can analyze the user's facial expressions and voice when submitting an idea and quantify the level of satisfaction. This allows ideas submitted with high satisfaction to be given a high rating. In addition, if satisfaction is low, suggestions can be made to improve satisfaction. Furthermore, based on satisfaction data, it can also identify the best time to submit an idea when the user is most satisfied.

[0099] The idea evaluation department can evaluate the technical feasibility of submitted ideas. For example, AI can analyze the technical requirements necessary to realize an idea and quantify its technical feasibility. This allows technically feasible ideas to be given a high rating. Based on the evaluation results, it can also propose improvements to solve technical problems. Furthermore, the results of the technical feasibility evaluation can be provided to companies, who can use them as reference information to build technically feasible business models.

[0100] The idea evaluation unit can use the emotion estimation function to add the user's happiness level to the evaluation criteria. For example, AI can analyze the user's facial expressions and voice when submitting an idea and quantify their happiness level. This allows ideas submitted with a high happiness level to be given a high rating. In addition, if the happiness level is low, suggestions can be made to improve the happiness level. Furthermore, based on the happiness level data, it can also identify the timing when the user can submit an idea when they are in the happiest state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The idea evaluation unit evaluates ideas submitted by users. For example, AI evaluates the market value and technical feasibility of ideas submitted by users. Step 2: The purchasing department purchases the ideas evaluated by the idea evaluation department. For example, AI purchases the ideas at an appropriate price. Step 3: The Generation Department analyzes the big data of ideas purchased by the Purchase Department to create new business ideas. For example, the Generation AI proposes new business models based on ideas submitted by users, taking into account technical feasibility and market needs. Step 4: The sales department sells the business ideas created by the generation department to companies. For example, the business ideas proposed by the generation AI are sold to companies, and the companies acquire patent rights and commercialize them. Step 5: The evidence creation department creates the evidence required by the company. For example, the generation AI creates and conducts surveys to provide market demand and consumer opinions. Step 6: The notification department notifies the originator of a specific idea. For example, the originator may notify an idea-buying company, and if the company requests it, the originator may be offered job-based employment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an idea evaluation unit that evaluates ideas submitted by users; a purchasing unit that purchases the ideas evaluated by the idea evaluation unit; a generation unit that analyzes the big data of ideas purchased by the purchase unit to create new business ideas; a sales department that sells the business ideas created by the creation department to companies; The Evidence Creation Department creates the evidence required by companies. Have a notification department that notifies originators of specific ideas A system characterized by:

2. The idea evaluation unit Using an emotion estimation function, the user's passion or interest level is added to the evaluation criteria.

2. The system of claim 1.

3. The generation unit When analyzing the big data, an emotion estimation function is used to consider the user's emotion data, and ideas that are likely to be emotionally relatable are preferentially created.

2. The system of claim 1.

4. The sales department When selling an idea to a company, the emotion estimation function is used to evaluate the company's level of interest or expectations and make the optimal proposal.

2. The system of claim 1.

5. The evidence creation unit When creating the evidence, an emotion estimation function is used to collect emotion data of the user, and evidence that is likely to be emotionally relatable is provided.

2. The system of claim 1.

6. The notification unit When employing the proposer in a job-based employment role, the motivation or aptitude of the proposer is evaluated using an emotion estimation function, and the most suitable employment opportunity is provided.

2. The system of claim 1.

7. The generation unit When generating the ideas, the different data sources are integrated to generate ideas from a more multifaceted perspective.

2. The system of claim 1.

8. The sales department In the idea sales process, analyze past sales data, extract successful patterns, and optimize proposal content.

2. The system of claim 1.

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