system

Abstract

The system according to this embodiment aims to efficiently evaluate ideas related to generative AI and to make it easier for excellent ideas to be reviewed and advised by experts. [Solution] The system according to the embodiment comprises a collection unit, an evaluation unit, a reception unit, and a review unit. The collection unit aggregates ideas for generation AI to a posting site. The evaluation unit evaluates the ideas aggregated by the collection unit. The reception unit receives user evaluations for the ideas evaluated by the evaluation unit. The review unit makes it easier for ideas highly rated by the reception unit to be reviewed and advised by experts.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the evaluation of ideas related to generative AI is performed by humans, there is a problem that the evaluation cannot catch up with the increase in ideas.

[0005] The system according to the embodiment aims to efficiently evaluate ideas related to generative AI and make it easier for experts to review and give advice on excellent ideas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an evaluation unit, a reception unit, and a review unit. The collection unit aggregates ideas generated by the AI ​​to a posting site. The evaluation unit evaluates the ideas aggregated by the collection unit. The reception unit receives user feedback on the ideas evaluated by the evaluation unit. The review unit makes it easier for highly-rated ideas from the reception unit to be reviewed and advised by experts. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently evaluate ideas related to generative AI and make it easier for excellent ideas to be reviewed and advised by experts. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The idea evaluation system according to an embodiment of the present invention is a system that utilizes generative AI to streamline idea evaluation and user matching. This idea evaluation system aggregates ideas generated by the generative AI on a posting site, and specialized AI agents perform patent searches, news searches, and technical evaluations to evaluate the ideas. Furthermore, user evaluations are also taken into account to determine rankings. Ideas with high evaluations are designed to have more chances of "review and advice by experts" and "adoption by project managers." The idea evaluation system also implements the following functions: for example, a playground (where various demos and tools can be safely and conveniently tried on the site), an asset store (where users can open up and sell their own prompts and agents to other users), and idea inheritance (where users can develop and utilize others' ideas and distribute any profits generated). This creates an environment where ideas are easily generated and evaluated, making efficient use of spare time and usable ideas, and enhancing creativity and consideration skills related to AI agents. For example, ideas generated by the generative AI are aggregated on a posting site. In this case, users only need to post their own ideas. Next, specialized AI agents perform patent searches, news searches, and technical evaluations to assess ideas. For example, the patent search agent checks whether the submitted idea overlaps with existing patents. The news search agent gathers the latest technology trends and competitive information to evaluate the novelty of the idea. The technical evaluation agent assesses the technical feasibility of the idea. Furthermore, user evaluations are also taken into account to determine the ranking. Users can evaluate other ideas. For example, they can provide evaluations such as "I tried it," "I inherited the idea," or "I would like this feature added." This makes it easier for ideas that meet actual needs to be evaluated. Ideas with high ratings are designed to have more chances of "expert review and advice" or "adoption by planning staff." For example, highly-rated ideas can receive detailed reviews and advice from experts. In addition, planning staff at each company can purchase ideas they like when they find them through ranking or keyword searches.This increases the feasibility of ideas and leads to business opportunities. The site will implement the following functions: For example, a playground (where users can safely and conveniently try out various demos and tools on the site). For example, users can publish their ideas as demos and have other users try them out. An asset store (where users can make their prompts and agents available to and sell to other users). For example, users can list their prompts and agents on the asset store and sell them to other users. Idea inheritance (where users can develop and utilize others' ideas and share any profits generated). For example, users can develop new ideas based on other ideas and share the profits. This will create an environment where ideas are easily generated and evaluated, allowing for efficient use of spare time and usable ideas, as well as enhancing creativity and consideration skills regarding AI agents. This will enable the idea evaluation system to efficiently collect, evaluate, accept, and review ideas for generated AI.

[0029] The idea evaluation system according to the embodiment comprises a collection unit, an evaluation unit, a reception unit, and an examination unit. The collection unit aggregates ideas generated by the generation AI to a posting site. The collection unit, for example, stores and manages user-submitted ideas in a database. The collection unit can also analyze and classify the content of ideas using the generation AI. For example, the collection unit classifies the content of ideas based on keywords and groups related ideas. The collection unit can also summarize the content of ideas using the generation AI and provide it to other users. For example, the collection unit uses the generation AI to extract the key points of an idea and generate a summary. The evaluation unit evaluates the ideas aggregated by the collection unit. The evaluation unit, for example, uses a patent search agent to check whether the submitted ideas overlap with existing patents. The evaluation unit uses a news search agent to collect the latest technology trends and competitive information and evaluate the novelty of the ideas. The evaluation unit uses a technology evaluation agent to evaluate the technical feasibility of the ideas. For example, the evaluation unit uses a patent search agent to search a patent database and confirm the novelty of the ideas. The news search agent searches news articles on the internet and collects information on competing ideas. The technology evaluation agent refers to technical literature and evaluates the technical feasibility of the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have AI perform the process by which the patent search agent searches the patent database. The reception unit receives evaluations from users for ideas evaluated by the evaluation unit. The reception unit provides an interface for users to evaluate other ideas, for example. The reception unit stores and manages user evaluations in a database. The reception unit allows users to evaluate ideas, for example, by saying "I tried it," "I inherited the idea," or "I would like this feature added." The reception unit aggregates user evaluations and determines the ranking of ideas. The reception unit aggregates user evaluations as scores and determines the ranking of ideas. The review unit makes it easier for ideas highly rated by the reception unit to be reviewed and advised by experts.The review department, for example, notifies experts of highly-rated ideas and requests detailed reviews and advice. The review department stores and manages the experts' review results and advice in a database. The review department, for example, has experts evaluate the technical details of the ideas and provide advice. The review department notifies the user of the experts' review results and provides feedback. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department has AI analyze the experts' review results and provides feedback to the user. This enables the idea evaluation system according to the embodiment to efficiently aggregate, evaluate, accept, and review ideas generated by the AI.

[0030] The collection unit aggregates ideas generated by the generative AI onto a posting site. For example, the collection unit stores and manages user-submitted ideas in a database. Specifically, when a user accesses the posting site and enters an idea, its content is automatically saved to the database. The collection unit can also analyze and classify the content of ideas using the generative AI. The generative AI uses natural language processing technology to analyze the text of submitted ideas and classify them based on keywords and themes. For example, the generative AI analyzes the content of an idea and classifies it into categories such as "environmental protection," "medical technology," and "entertainment." Furthermore, the collection unit can also use the generative AI to summarize the content of ideas and provide them to other users. The generative AI extracts the key points of an idea and generates a concise summary. This allows other users to quickly grasp the overview of the submitted idea. For example, the collection unit uses the generative AI to extract the key points of an idea and generate a summary. The summary is displayed to other users on the posting site, making it easy to understand the content of the idea. This allows the collection unit to efficiently aggregate and manage user-submitted ideas. Furthermore, by using generative AI, it becomes possible to quickly and accurately analyze, classify, and summarize the content of ideas. This allows the collection unit to effectively manage submitted ideas and provide them to other users. In addition, the collection unit can store the collected ideas in a database and link them with other systems and departments as needed. For example, the collected ideas can be made accessible to the evaluation and review departments. The collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The evaluation unit evaluates the ideas compiled by the collection unit. For example, the evaluation unit uses a patent search agent to check whether the submitted ideas overlap with existing patents. The patent search agent searches the patent database to confirm the novelty of the submitted ideas. Specifically, the patent search agent searches the patent database based on the keywords and technical content of the submitted ideas to check if similar patents exist. The evaluation unit uses a news search agent to collect the latest technology trends and competitive information and evaluate the novelty of the ideas. The news search agent searches news articles on the internet to collect the latest technology trends and competitive information related to the submitted ideas. This allows the evaluation unit to evaluate whether the submitted ideas are in line with the latest technology trends and whether competitors have similar ideas. The evaluation unit uses a technology evaluation agent to evaluate the technical feasibility of the ideas. The technology evaluation agent refers to technical literature to evaluate the technical feasibility of the submitted ideas. Specifically, the technology evaluation agent analyzes the technical content of the submitted ideas and refers to technical literature and research papers to evaluate whether the technology is feasible. Some or all of the processes described above in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have AI perform the process by which a patent search agent searches a patent database. AI can rapidly analyze large amounts of data and efficiently search for similar patents. This allows the evaluation unit to quickly and accurately evaluate the novelty and technical feasibility of submitted ideas. Furthermore, the evaluation unit can continuously improve the evaluation process by utilizing past evaluation data and feedback. This allows the evaluation unit to always provide highly accurate evaluations based on the latest information, improving the reliability and security of the entire system.

[0032] The reception department receives user feedback on ideas that have been evaluated by the evaluation department. For example, the reception department provides an interface for users to evaluate other ideas. Specifically, it provides an interface that allows users to access a posting site, view ideas posted by other users, and evaluate them. The reception department stores and manages user evaluations in a database. Users can evaluate ideas by saying things like "I tried it," "I inherited this idea," or "I'd like this feature added." The reception department then aggregates user evaluations and determines the ranking of ideas. Specifically, it aggregates user evaluations as scores to determine the ranking of ideas. For example, if a user evaluates an idea as "I tried it," that evaluation is added to the score, and the idea's ranking increases. The reception department can aggregate user evaluations in real time and update the ranking of ideas. This allows the reception department to provide fair evaluations based on user feedback and determine the ranking of ideas accordingly. Furthermore, the reception department can collect user feedback and continuously improve the evaluation process. For example, it can provide feedback after users have made evaluations and use that feedback to improve the interface and evaluation criteria. Furthermore, the reception department will publish rankings based on user ratings, allowing other users to refer to them. This will enable the reception department to efficiently manage user ratings and fairly determine the ranking of ideas.

[0033] The review department facilitates expert review and advice for ideas highly rated by the reception department. For example, the review department notifies experts of highly rated ideas and requests detailed review and advice. Specifically, the review department notifies experts of highly rated ideas, who then evaluate the technical details of the ideas and provide advice. The review department stores and manages the experts' review results and advice in a database. Experts evaluate the technical details of the ideas and provide advice on their technical feasibility and marketability. The review department notifies users of the experts' review results and provides feedback. This allows users to receive detailed review results and advice from experts. Some or all of the above processes in the review department may be performed using AI, or not. For example, the review department may have AI analyze the experts' review results and provide feedback to users. AI can analyze the experts' review results and provide feedback to users in an easily understandable format. This allows the review department to efficiently manage the experts' review results and provide users with quick and accurate feedback. Furthermore, the review department can continuously improve its review process by utilizing past review results and feedback. This allows the review department to always provide highly accurate reviews based on the latest information, thereby improving the reliability and security of the entire system.

[0034] The system includes a provisioning unit that provides a playground. The provisioning unit provides a playground. For example, the provisioning unit provides an environment where users can safely and conveniently try out various demos and tools on the site. The provisioning unit provides a function that allows users to publish their ideas as demos and have other users try them out. For example, the provisioning unit provides an interface where users can upload their ideas as demos and other users can try out those demos. The provisioning unit includes a function to manage the content of the demos and collect user feedback. For example, the provisioning unit saves the content of the demos to a database and aggregates user feedback. The provisioning unit includes a function to analyze the content of the demos and suggest improvements. For example, the provisioning unit uses generative AI to analyze the content of the demos and suggest improvements. This allows users to safely and conveniently try out various demos and tools. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can have generative AI analyze the content of the demos and have the generative AI suggest improvements.

[0035] The system includes a provisioning unit that provides an asset store. The provisioning unit provides an environment where users can make their prompts and agents available for sale to other users. The provisioning unit provides a function that allows users to list their prompts and agents on the asset store and sell them to other users. The provisioning unit provides an interface where users can upload their prompts and agents to the asset store and other users can purchase them. The provisioning unit includes a function to manage the content of assets and collect user feedback. The provisioning unit, for example, stores the content of assets in a database and aggregates user feedback. The provisioning unit includes a function to analyze the content of assets and suggest improvements. The provisioning unit, for example, uses generative AI to analyze the content of assets and suggest improvements. This allows users to make their prompts and agents available for sale to other users. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit may have generative AI analyze the content of assets and have the generative AI make suggestions for improvements.

[0036] The system includes a management unit for managing idea inheritance. The management unit manages idea inheritance. The management unit provides an environment where, for example, users can develop and utilize others' ideas and distribute any profits generated. The management unit provides a function that allows users to develop new ideas based on other ideas and distribute the profits. The management unit provides an interface for users to post new ideas based on other ideas and distribute the profits. The management unit has a function to manage the idea inheritance process and distribute profits. The management unit saves the idea inheritance process in a database and automatically distributes profits. The management unit has a function to analyze the idea inheritance process and suggest improvements. The management unit uses, for example, generative AI to analyze the idea inheritance process and suggest improvements. This allows users to develop and utilize others' ideas and distribute any profits generated. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can have generative AI analyze the idea inheritance process and have the generative AI suggest improvements.

[0037] The collection unit analyzes the user's past idea submission history and selects the optimal collection method. The collection unit can, for example, analyze the trends of ideas previously submitted by the user and encourage submissions on similar themes. The collection unit can analyze the success rate of ideas previously submitted by the user and encourage submissions on themes that are more likely to succeed. The collection unit can also analyze the evaluation of ideas previously submitted by the user and encourage submissions on themes that are more likely to receive high evaluations. In this way, the optimal collection method can be selected by analyzing the user's past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past idea submission history into a generating AI and have the generating AI select the optimal collection method.

[0038] The collection unit filters ideas based on the user's current projects and areas of interest. For example, the collection unit prioritizes collecting ideas related to the user's current projects. The collection unit can also prioritize collecting ideas related to areas the user is interested in. Furthermore, the collection unit can prioritize collecting ideas related to areas the user has shown interest in in the past. This allows for the collection of highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The collection unit prioritizes collecting highly relevant ideas by considering the user's geographical location information. For example, the collection unit can prioritize collecting ideas related to the user's current location, areas the user has visited in the past, and areas the user plans to visit in the future. This allows for the priority collection of highly relevant ideas by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant ideas.

[0040] The collection unit analyzes the user's social media activity and collects relevant ideas when collecting ideas. The collection unit can collect ideas related to themes the user has shown interest in on social media. The collection unit can collect ideas related to the content of posts from accounts the user follows on social media. The collection unit can also collect ideas related to the themes of groups the user participates in on social media. In this way, relevant ideas can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant ideas.

[0041] The evaluation unit conducts a detailed analysis of the technical feasibility of the idea during the evaluation process. The evaluation unit may, for example, refer to relevant technical literature to assess the technical feasibility of the idea. The evaluation unit may also gather expert opinions to assess the technical feasibility of the idea. Furthermore, the evaluation unit may conduct actual technical tests to assess the technical feasibility of the idea. This improves the accuracy of the evaluation by conducting a detailed analysis of the technical feasibility of the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit may have a generating AI perform tasks such as referring to technical literature or gathering expert opinions.

[0042] The evaluation unit improves the accuracy of its evaluation by considering the marketability of the idea during the evaluation process. The evaluation unit improves the accuracy of its evaluation by considering the marketability of the idea during the evaluation process. For example, the evaluation unit may refer to relevant market research data to evaluate the marketability of the idea. The evaluation unit may collect consumer opinions to evaluate the marketability of the idea. The evaluation unit may also analyze competing products to evaluate the marketability of the idea. This improves the accuracy of the evaluation by considering the marketability of the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may have a generating AI perform tasks such as referring to market research data or collecting consumer opinions.

[0043] The evaluation unit considers the geographical distribution of ideas during the evaluation process. For example, the evaluation unit may refer to relevant geographical data to evaluate the geographical distribution of ideas. The evaluation unit may collect regional market research data to evaluate the geographical distribution of ideas. The evaluation unit may also collect regional consumer opinions to evaluate the geographical distribution of ideas. This improves the accuracy of the evaluation by considering the geographical distribution of ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit may have a generating AI perform tasks such as referencing geographical data or collecting market research data.

[0044] The evaluation unit improves the accuracy of its evaluation by referring to relevant literature on the idea during the evaluation process. The evaluation unit can, for example, understand the technical background by referring to relevant literature on the idea. The evaluation unit can compare the idea with existing technologies by referring to relevant literature on the idea. The evaluation unit can also clarify technical challenges by referring to relevant literature on the idea. This improves the accuracy of the evaluation by referring to relevant literature on the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have a generative AI perform the tasks of referring to relevant literature and understanding the technical background.

[0045] The reception department selects the optimal reception method by referring to the user's past evaluation history when receiving a request. The reception department may, for example, prioritize suggesting evaluation methods that the user has frequently used in the past. The reception department may refer to evaluation methods for ideas that the user has previously received high ratings for. The reception department may also predict and suggest evaluation methods to be used during specific time periods based on the user's past evaluation history. This allows the reception department to select the optimal reception method by referring to the user's past evaluation history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the user's past evaluation history into a generating AI and have the generating AI select the optimal reception method.

[0046] The reception unit selects the optimal reception method at the time of reception, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method adapted to the screen size. If the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. If the user is using a smartwatch, the reception unit can also provide a simple and highly visible reception method. This allows the reception unit to select the optimal reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's device information into a generating AI and have the generating AI select the optimal reception method.

[0047] The review department improves the accuracy of its review by delving deeper into the technical details of the idea during the review process. The review department improves the accuracy of its review by delving deeper into the technical details of the idea during the review process. For example, the review department may refer to relevant technical literature to evaluate the technical details of the idea. The review department may gather expert opinions to evaluate the technical details of the idea. The review department may also conduct actual technical tests to evaluate the technical details of the idea. This improves the accuracy of the review by delving deeper into the technical details of the idea. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department may have a generative AI perform tasks such as referring to technical literature or gathering expert opinions.

[0048] The review department considers the geographical distribution of ideas during the review process. For example, the review department may refer to relevant geographical data to assess the geographical distribution of ideas. The review department may collect regional market research data to assess the geographical distribution of ideas. The review department may also collect regional consumer opinions to assess the geographical distribution of ideas. This improves the accuracy of the review process by considering the geographical distribution of ideas. Some or all of the above processes in the review department may be performed using AI, for example, or not. For example, the review department may have a generative AI perform tasks such as referencing geographical data or collecting market research data.

[0049] The review department improves the accuracy of its review by referring to relevant literature on the idea during the review process. The review department improves the accuracy of its review by referring to relevant literature on the idea during the review process. For example, the review department can refer to relevant literature on the idea to understand the technical background. The review department can refer to relevant literature on the idea to compare it with existing technologies. The review department can also refer to relevant literature on the idea to clarify the technical challenges. In this way, the accuracy of the review is improved by referring to relevant literature on the idea. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department can have a generative AI perform the tasks of referring to relevant literature and understanding the technical background.

[0050] The service provider selects the most suitable demos and tools by referring to the user's past usage history at the time of delivery. The service provider selects the most suitable demos and tools by referring to the user's past usage history at the time of delivery. For example, the service provider proposes the most suitable demos and tools based on the demos and tools the user has used in the past. The service provider can prioritize providing demos and tools that the user has used frequently based on the user's past usage history. The service provider can also analyze the user's past usage history and provide demos and tools that are tailored to the purpose of use. This allows the service provider to select the most suitable demos and tools by referring to the user's past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past usage history into a generating AI and have the generating AI select the most suitable demos and tools.

[0051] The service provider will provide the most suitable demos and tools at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide demos and tools adapted to the screen size. If the user is using a tablet, the service provider can provide demos and tools optimized for a larger screen. If the user is using a smartwatch, the service provider can also provide concise and highly visible demos and tools. In this way, the service provider can provide the most suitable demos and tools by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI perform the task of providing the most suitable demos and tools.

[0052] The management department selects the optimal idea inheritance method by referring to the user's past idea inheritance history during management. The management department can, for example, analyze the trends of ideas inherited by the user in the past and encourage inheritance on similar themes. The management department can refer to the user's past successful inheritance methods. The management department can also predict and suggest inheritance methods to be used at specific times based on the user's past inheritance history. This allows the optimal inheritance method to be selected by referring to the user's past idea inheritance history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past idea inheritance history into a generating AI and have the generating AI select the optimal inheritance method.

[0053] The management unit provides the optimal inheritance method during management, taking into account the user's device information. For example, if the user is using a smartphone, the management unit can provide an inheritance method adapted to the screen size. If the user is using a tablet, the management unit can provide an inheritance method optimized for a larger screen. If the user is using a smartwatch, the management unit can also provide a concise and highly visible inheritance method. This allows the management unit to provide the optimal inheritance method by considering the user's device information. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal inheritance method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The collection unit can also analyze a user's past idea submission history and select the optimal collection method. For example, it can analyze the trends of ideas a user has submitted in the past and encourage submissions on similar themes. It can analyze the success rate of ideas a user has submitted in the past and encourage submissions on themes that are more likely to succeed. It can also analyze the evaluations of ideas a user has submitted in the past and encourage submissions on themes that are more likely to receive high evaluations. In this way, the optimal collection method can be selected by analyzing a user's past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input a user's past idea submission history into a generating AI and have the generating AI select the optimal collection method.

[0056] The collection unit can also filter ideas based on the user's current projects and areas of interest. For example, it can prioritize collecting ideas related to the user's current projects, ideas related to areas of interest, and ideas related to areas the user has shown interest in in the past. This allows for the collection of highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0057] The evaluation unit can also conduct a detailed analysis of the technical feasibility of an idea during the evaluation process. For example, it can refer to relevant technical literature to assess the technical feasibility of an idea. It can also gather expert opinions to assess the technical feasibility of an idea. It can even conduct actual technical tests to assess the technical feasibility of an idea. This improves the accuracy of the evaluation by allowing for a detailed analysis of the technical feasibility of an idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can have a generating AI perform tasks such as referring to technical literature or gathering expert opinions.

[0058] The reception department can also select the optimal reception method by referring to the user's past evaluation history at the time of reception. For example, it can prioritize suggesting evaluation methods that the user has frequently used in the past. It can also refer to evaluation methods for ideas that the user has previously received high ratings for. It can also predict and suggest evaluation methods to be used during specific time periods based on the user's past evaluation history. In this way, the optimal reception method can be selected by referring to the user's past evaluation history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's past evaluation history into a generating AI and have the generating AI select the optimal reception method.

[0059] The service provider can also provide optimal demos and tools by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, it can provide demos and tools that are adapted to the screen size. If the user is using a tablet, it can provide demos and tools optimized for a larger screen. If the user is using a smartwatch, it can provide concise and highly visible demos and tools. In this way, the service provider can provide optimal demos and tools by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI execute the provision of optimal demos and tools.

[0060] The management department can also select the optimal idea inheritance method by referring to the user's past idea inheritance history during management. For example, it can analyze the trends of ideas the user has inherited in the past and encourage inheritance on similar themes. Users can refer to inheritance methods that have been successful in the past. It can also predict and suggest inheritance methods to be used at specific times based on the user's past inheritance history. In this way, the optimal inheritance method can be selected by referring to the user's past idea inheritance history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past idea inheritance history into a generating AI and have the generating AI select the optimal inheritance method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit aggregates the generated AI ideas on the posting site. The collection unit stores and manages the user-submitted ideas in a database. Furthermore, it can analyze and classify the content of the ideas using the generated AI. For example, it can classify the content of ideas based on keywords and group related ideas. It can also summarize the content of ideas using the generated AI and provide it to other users. Step 2: The evaluation unit evaluates the ideas aggregated by the collection unit. The evaluation unit uses a patent search agent to check whether the submitted ideas overlap with existing patents. It also uses a news search agent to collect the latest technology trends and competitive information and evaluates the novelty of the ideas. Furthermore, it uses a technology evaluation agent to evaluate the technical feasibility of the ideas. These processes may or may not be performed using AI. Step 3: The reception department receives user feedback on ideas evaluated by the evaluation department. The reception department provides an interface for users to evaluate other ideas, and stores and manages user evaluations in a database. For example, users can evaluate ideas with comments such as "I tried it," "I inherited this idea," or "I'd like to see this feature added." The reception department compiles user evaluations and determines the ranking of the ideas. Step 4: The review department makes highly-rated ideas from the reception department more likely to receive expert review and advice. The review department notifies experts of highly-rated ideas and requests detailed review and advice. Expert review results and advice are stored and managed in a database. The review department notifies users of the expert review results and provides feedback. These processes may or may not be performed using AI.

[0063] (Example of form 2) The idea evaluation system according to an embodiment of the present invention is a system that utilizes generative AI to streamline idea evaluation and user matching. This idea evaluation system aggregates ideas generated by the generative AI on a posting site, and specialized AI agents perform patent searches, news searches, and technical evaluations to evaluate the ideas. Furthermore, user evaluations are also taken into account to determine rankings. Ideas with high evaluations are designed to have more chances of "review and advice by experts" and "adoption by project managers." The idea evaluation system also implements the following functions: for example, a playground (where various demos and tools can be safely and conveniently tried on the site), an asset store (where users can open up and sell their own prompts and agents to other users), and idea inheritance (where users can develop and utilize others' ideas and distribute any profits generated). This creates an environment where ideas are easily generated and evaluated, making efficient use of spare time and usable ideas, and enhancing creativity and consideration skills related to AI agents. For example, ideas generated by the generative AI are aggregated on a posting site. In this case, users only need to post their own ideas. Next, specialized AI agents perform patent searches, news searches, and technical evaluations to assess ideas. For example, the patent search agent checks whether the submitted idea overlaps with existing patents. The news search agent gathers the latest technology trends and competitive information to evaluate the novelty of the idea. The technical evaluation agent assesses the technical feasibility of the idea. Furthermore, user evaluations are also taken into account to determine the ranking. Users can evaluate other ideas. For example, they can provide evaluations such as "I tried it," "I inherited the idea," or "I would like this feature added." This makes it easier for ideas that meet actual needs to be evaluated. Ideas with high ratings are designed to have more chances of "expert review and advice" or "adoption by planning staff." For example, highly-rated ideas can receive detailed reviews and advice from experts. In addition, planning staff at each company can purchase ideas they like when they find them through ranking or keyword searches.This increases the feasibility of ideas and leads to business opportunities. The site will implement the following functions: For example, a playground (where users can safely and conveniently try out various demos and tools on the site). For example, users can publish their ideas as demos and have other users try them out. An asset store (where users can make their prompts and agents available to and sell to other users). For example, users can list their prompts and agents on the asset store and sell them to other users. Idea inheritance (where users can develop and utilize others' ideas and share any profits generated). For example, users can develop new ideas based on other ideas and share the profits. This will create an environment where ideas are easily generated and evaluated, allowing for efficient use of spare time and usable ideas, as well as enhancing creativity and consideration skills regarding AI agents. This will enable the idea evaluation system to efficiently collect, evaluate, accept, and review ideas for generated AI.

[0064] The idea evaluation system according to the embodiment comprises a collection unit, an evaluation unit, a reception unit, and an examination unit. The collection unit aggregates ideas generated by the generation AI to a posting site. The collection unit, for example, stores and manages user-submitted ideas in a database. The collection unit can also analyze and classify the content of ideas using the generation AI. For example, the collection unit classifies the content of ideas based on keywords and groups related ideas. The collection unit can also summarize the content of ideas using the generation AI and provide it to other users. For example, the collection unit uses the generation AI to extract the key points of an idea and generate a summary. The evaluation unit evaluates the ideas aggregated by the collection unit. The evaluation unit, for example, uses a patent search agent to check whether the submitted ideas overlap with existing patents. The evaluation unit uses a news search agent to collect the latest technology trends and competitive information and evaluate the novelty of the ideas. The evaluation unit uses a technology evaluation agent to evaluate the technical feasibility of the ideas. For example, the evaluation unit uses a patent search agent to search a patent database and confirm the novelty of the ideas. The news search agent searches news articles on the internet and collects information on competing ideas. The technology evaluation agent refers to technical literature and evaluates the technical feasibility of the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have AI perform the process by which the patent search agent searches the patent database. The reception unit receives evaluations from users for ideas evaluated by the evaluation unit. The reception unit provides an interface for users to evaluate other ideas, for example. The reception unit stores and manages user evaluations in a database. The reception unit allows users to evaluate ideas, for example, by saying "I tried it," "I inherited the idea," or "I would like this feature added." The reception unit aggregates user evaluations and determines the ranking of ideas. The reception unit aggregates user evaluations as scores and determines the ranking of ideas. The review unit makes it easier for ideas highly rated by the reception unit to be reviewed and advised by experts.The review department, for example, notifies experts of highly-rated ideas and requests detailed reviews and advice. The review department stores and manages the experts' review results and advice in a database. The review department, for example, has experts evaluate the technical details of the ideas and provide advice. The review department notifies the user of the experts' review results and provides feedback. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department has AI analyze the experts' review results and provides feedback to the user. This enables the idea evaluation system according to the embodiment to efficiently aggregate, evaluate, accept, and review ideas generated by the AI.

[0065] The collection unit aggregates ideas generated by the generative AI onto a posting site. For example, the collection unit stores and manages user-submitted ideas in a database. Specifically, when a user accesses the posting site and enters an idea, its content is automatically saved to the database. The collection unit can also analyze and classify the content of ideas using the generative AI. The generative AI uses natural language processing technology to analyze the text of submitted ideas and classify them based on keywords and themes. For example, the generative AI analyzes the content of an idea and classifies it into categories such as "environmental protection," "medical technology," and "entertainment." Furthermore, the collection unit can also use the generative AI to summarize the content of ideas and provide them to other users. The generative AI extracts the key points of an idea and generates a concise summary. This allows other users to quickly grasp the overview of the submitted idea. For example, the collection unit uses the generative AI to extract the key points of an idea and generate a summary. The summary is displayed to other users on the posting site, making it easy to understand the content of the idea. This allows the collection unit to efficiently aggregate and manage user-submitted ideas. Furthermore, by using generative AI, it becomes possible to quickly and accurately analyze, classify, and summarize the content of ideas. This allows the collection unit to effectively manage submitted ideas and provide them to other users. In addition, the collection unit can store the collected ideas in a database and link them with other systems and departments as needed. For example, the collected ideas can be made accessible to the evaluation and review departments. The collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The evaluation unit evaluates the ideas compiled by the collection unit. For example, the evaluation unit uses a patent search agent to check whether the submitted ideas overlap with existing patents. The patent search agent searches the patent database to confirm the novelty of the submitted ideas. Specifically, the patent search agent searches the patent database based on the keywords and technical content of the submitted ideas to check if similar patents exist. The evaluation unit uses a news search agent to collect the latest technology trends and competitive information and evaluate the novelty of the ideas. The news search agent searches news articles on the internet to collect the latest technology trends and competitive information related to the submitted ideas. This allows the evaluation unit to evaluate whether the submitted ideas are in line with the latest technology trends and whether competitors have similar ideas. The evaluation unit uses a technology evaluation agent to evaluate the technical feasibility of the ideas. The technology evaluation agent refers to technical literature to evaluate the technical feasibility of the submitted ideas. Specifically, the technology evaluation agent analyzes the technical content of the submitted ideas and refers to technical literature and research papers to evaluate whether the technology is feasible. Some or all of the processes described above in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have AI perform the process by which a patent search agent searches a patent database. AI can rapidly analyze large amounts of data and efficiently search for similar patents. This allows the evaluation unit to quickly and accurately evaluate the novelty and technical feasibility of submitted ideas. Furthermore, the evaluation unit can continuously improve the evaluation process by utilizing past evaluation data and feedback. This allows the evaluation unit to always provide highly accurate evaluations based on the latest information, improving the reliability and security of the entire system.

[0067] The reception department receives user feedback on ideas that have been evaluated by the evaluation department. For example, the reception department provides an interface for users to evaluate other ideas. Specifically, it provides an interface that allows users to access a posting site, view ideas posted by other users, and evaluate them. The reception department stores and manages user evaluations in a database. Users can evaluate ideas by saying things like "I tried it," "I inherited this idea," or "I'd like this feature added." The reception department then aggregates user evaluations and determines the ranking of ideas. Specifically, it aggregates user evaluations as scores to determine the ranking of ideas. For example, if a user evaluates an idea as "I tried it," that evaluation is added to the score, and the idea's ranking increases. The reception department can aggregate user evaluations in real time and update the ranking of ideas. This allows the reception department to provide fair evaluations based on user feedback and determine the ranking of ideas accordingly. Furthermore, the reception department can collect user feedback and continuously improve the evaluation process. For example, it can provide feedback after users have made evaluations and use that feedback to improve the interface and evaluation criteria. Furthermore, the reception department will publish rankings based on user ratings, allowing other users to refer to them. This will enable the reception department to efficiently manage user ratings and fairly determine the ranking of ideas.

[0068] The review department facilitates expert review and advice for ideas highly rated by the reception department. For example, the review department notifies experts of highly rated ideas and requests detailed review and advice. Specifically, the review department notifies experts of highly rated ideas, who then evaluate the technical details of the ideas and provide advice. The review department stores and manages the experts' review results and advice in a database. Experts evaluate the technical details of the ideas and provide advice on their technical feasibility and marketability. The review department notifies users of the experts' review results and provides feedback. This allows users to receive detailed review results and advice from experts. Some or all of the above processes in the review department may be performed using AI, or not. For example, the review department may have AI analyze the experts' review results and provide feedback to users. AI can analyze the experts' review results and provide feedback to users in an easily understandable format. This allows the review department to efficiently manage the experts' review results and provide users with quick and accurate feedback. Furthermore, the review department can continuously improve its review process by utilizing past review results and feedback. This allows the review department to always provide highly accurate reviews based on the latest information, thereby improving the reliability and security of the entire system.

[0069] The system includes a provisioning unit that provides a playground. The provisioning unit provides a playground. For example, the provisioning unit provides an environment where users can safely and conveniently try out various demos and tools on the site. The provisioning unit provides a function that allows users to publish their ideas as demos and have other users try them out. For example, the provisioning unit provides an interface where users can upload their ideas as demos and other users can try out those demos. The provisioning unit includes a function to manage the content of the demos and collect user feedback. For example, the provisioning unit saves the content of the demos to a database and aggregates user feedback. The provisioning unit includes a function to analyze the content of the demos and suggest improvements. For example, the provisioning unit uses generative AI to analyze the content of the demos and suggest improvements. This allows users to safely and conveniently try out various demos and tools. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can have generative AI analyze the content of the demos and have the generative AI suggest improvements.

[0070] The system includes a provisioning unit that provides an asset store. The provisioning unit provides an environment where users can make their prompts and agents available for sale to other users. The provisioning unit provides a function that allows users to list their prompts and agents on the asset store and sell them to other users. The provisioning unit provides an interface where users can upload their prompts and agents to the asset store and other users can purchase them. The provisioning unit includes a function to manage the content of assets and collect user feedback. The provisioning unit, for example, stores the content of assets in a database and aggregates user feedback. The provisioning unit includes a function to analyze the content of assets and suggest improvements. The provisioning unit, for example, uses generative AI to analyze the content of assets and suggest improvements. This allows users to make their prompts and agents available for sale to other users. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit may have generative AI analyze the content of assets and have the generative AI make suggestions for improvements.

[0071] The system includes a management unit for managing idea inheritance. The management unit manages idea inheritance. The management unit provides an environment where, for example, users can develop and utilize others' ideas and distribute any profits generated. The management unit provides a function that allows users to develop new ideas based on other ideas and distribute the profits. The management unit provides an interface for users to post new ideas based on other ideas and distribute the profits. The management unit has a function to manage the idea inheritance process and distribute profits. The management unit saves the idea inheritance process in a database and automatically distributes profits. The management unit has a function to analyze the idea inheritance process and suggest improvements. The management unit uses, for example, generative AI to analyze the idea inheritance process and suggest improvements. This allows users to develop and utilize others' ideas and distribute any profits generated. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can have generative AI analyze the idea inheritance process and have the generative AI suggest improvements.

[0072] The collection unit estimates the user's emotions and adjusts the timing of idea collection based on the estimated emotions. The collection unit estimates the user's emotions and adjusts the timing of idea collection based on the estimated emotions. For example, if the user is stressed, the collection unit can delay the collection timing so that the user can post ideas in a relaxed state. If the user is relaxed, the collection unit can advance the collection timing to encourage them to post ideas proactively. If the user is excited, the collection unit can adjust the collection timing so that the user can post ideas in a calm state. In this way, by adjusting the timing of idea collection according to the user's emotions, ideas can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The collection unit analyzes the user's past idea submission history and selects the optimal collection method. The collection unit can, for example, analyze the trends of ideas previously submitted by the user and encourage submissions on similar themes. The collection unit can analyze the success rate of ideas previously submitted by the user and encourage submissions on themes that are more likely to succeed. The collection unit can also analyze the evaluation of ideas previously submitted by the user and encourage submissions on themes that are more likely to receive high evaluations. In this way, the optimal collection method can be selected by analyzing the user's past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past idea submission history into a generating AI and have the generating AI select the optimal collection method.

[0074] The collection unit filters ideas based on the user's current projects and areas of interest. For example, the collection unit prioritizes collecting ideas related to the user's current projects. The collection unit can also prioritize collecting ideas related to areas the user is interested in. Furthermore, the collection unit can prioritize collecting ideas related to areas the user has shown interest in in the past. This allows for the collection of highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The collection unit estimates the user's emotions and determines the priority of ideas to collect based on the estimated emotions. For example, if the user is stressed, the collection unit may prioritize collecting relaxing ideas. If the user is relaxed, the collection unit may prioritize collecting challenging ideas. If the user is excited, the collection unit may prioritize collecting ideas that encourage calm thinking. This allows for the collection of more appropriate ideas by prioritizing ideas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collection unit may be performed using AI, or not. For example, the collection unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The collection unit prioritizes collecting highly relevant ideas by considering the user's geographical location information. For example, the collection unit can prioritize collecting ideas related to the user's current location, areas the user has visited in the past, and areas the user plans to visit in the future. This allows for the priority collection of highly relevant ideas by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant ideas.

[0077] The collection unit analyzes the user's social media activity and collects relevant ideas when collecting ideas. The collection unit can collect ideas related to themes the user has shown interest in on social media. The collection unit can collect ideas related to the content of posts from accounts the user follows on social media. The collection unit can also collect ideas related to the themes of groups the user participates in on social media. In this way, relevant ideas can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant ideas.

[0078] The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit may relax the evaluation criteria to make the evaluation easier. If the user is relaxed, the evaluation unit may tighten the evaluation criteria to perform a more detailed evaluation. If the user is excited, the evaluation unit may also adjust the evaluation criteria to encourage a calmer evaluation. By adjusting the evaluation criteria according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The evaluation unit conducts a detailed analysis of the technical feasibility of the idea during the evaluation process. The evaluation unit may, for example, refer to relevant technical literature to assess the technical feasibility of the idea. The evaluation unit may also gather expert opinions to assess the technical feasibility of the idea. Furthermore, the evaluation unit may conduct actual technical tests to assess the technical feasibility of the idea. This improves the accuracy of the evaluation by conducting a detailed analysis of the technical feasibility of the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit may have a generating AI perform tasks such as referring to technical literature or gathering expert opinions.

[0080] The evaluation unit improves the accuracy of its evaluation by considering the marketability of the idea during the evaluation process. The evaluation unit improves the accuracy of its evaluation by considering the marketability of the idea during the evaluation process. For example, the evaluation unit may refer to relevant market research data to evaluate the marketability of the idea. The evaluation unit may collect consumer opinions to evaluate the marketability of the idea. The evaluation unit may also analyze competing products to evaluate the marketability of the idea. This improves the accuracy of the evaluation by considering the marketability of the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may have a generating AI perform tasks such as referring to market research data or collecting consumer opinions.

[0081] The evaluation unit estimates the user's emotions and adjusts the order in which the evaluation results are displayed based on the estimated emotions. The evaluation unit estimates the user's emotions and adjusts the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is stressed, the evaluation unit can display the evaluation results concisely. If the user is relaxed, the evaluation unit can display the evaluation results in detail. If the user is excited, the evaluation unit can also display the evaluation results calmly. In this way, by adjusting the order in which the evaluation results are displayed according to the user's emotions, more appropriate evaluation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The evaluation unit considers the geographical distribution of ideas during the evaluation process. For example, the evaluation unit may refer to relevant geographical data to evaluate the geographical distribution of ideas. The evaluation unit may collect regional market research data to evaluate the geographical distribution of ideas. The evaluation unit may also collect regional consumer opinions to evaluate the geographical distribution of ideas. This improves the accuracy of the evaluation by considering the geographical distribution of ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit may have a generating AI perform tasks such as referencing geographical data or collecting market research data.

[0083] The evaluation unit improves the accuracy of its evaluation by referring to relevant literature on the idea during the evaluation process. The evaluation unit can, for example, understand the technical background by referring to relevant literature on the idea. The evaluation unit can compare the idea with existing technologies by referring to relevant literature on the idea. The evaluation unit can also clarify technical challenges by referring to relevant literature on the idea. This improves the accuracy of the evaluation by referring to relevant literature on the idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can have a generative AI perform the tasks of referring to relevant literature and understanding the technical background.

[0084] The reception unit estimates the user's emotions and adjusts the evaluation process based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the evaluation process. If the user is relaxed, the reception unit can provide detailed evaluation options and suggest a customizable evaluation method. If the user is in a hurry, the reception unit can prioritize voice input to allow for a quick evaluation. This allows for more appropriate evaluation by adjusting the evaluation process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception department selects the optimal reception method by referring to the user's past evaluation history when receiving a request. The reception department may, for example, prioritize suggesting evaluation methods that the user has frequently used in the past. The reception department may refer to evaluation methods for ideas that the user has previously received high ratings for. The reception department may also predict and suggest evaluation methods to be used during specific time periods based on the user's past evaluation history. This allows the reception department to select the optimal reception method by referring to the user's past evaluation history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the user's past evaluation history into a generating AI and have the generating AI select the optimal reception method.

[0086] The reception unit estimates the user's emotions and adjusts the order of evaluations based on the estimated emotions. The reception unit simplifies the evaluation order if the user is stressed. If the user is relaxed, the reception unit can provide a detailed evaluation order. If the user is in a hurry, the reception unit can also adjust the order to allow for quick evaluation. This allows for more appropriate evaluations by adjusting the order of evaluations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception unit selects the optimal reception method at the time of reception, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method adapted to the screen size. If the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. If the user is using a smartwatch, the reception unit can also provide a simple and highly visible reception method. This allows the reception unit to select the optimal reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's device information into a generating AI and have the generating AI select the optimal reception method.

[0088] The review unit estimates the user's emotions and adjusts the review criteria based on the estimated emotions. The review unit estimates the user's emotions and adjusts the review criteria based on the estimated emotions. For example, if the user is stressed, the review unit may relax the review criteria to make the review easier. If the user is relaxed, the review unit may tighten the review criteria to conduct a more detailed review. If the user is agitated, the review unit may adjust the review criteria to encourage a calmer review. This allows for more appropriate reviews by adjusting the review criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the review unit may be performed using AI, for example, or not using AI. For example, the review unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The review department improves the accuracy of its review by delving deeper into the technical details of the idea during the review process. The review department improves the accuracy of its review by delving deeper into the technical details of the idea during the review process. For example, the review department may refer to relevant technical literature to evaluate the technical details of the idea. The review department may gather expert opinions to evaluate the technical details of the idea. The review department may also conduct actual technical tests to evaluate the technical details of the idea. This improves the accuracy of the review by delving deeper into the technical details of the idea. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department may have a generative AI perform tasks such as referring to technical literature or gathering expert opinions.

[0090] The review unit estimates the user's emotions and adjusts the order in which the review results are displayed based on the estimated emotions. The review unit estimates the user's emotions and adjusts the order in which the review results are displayed based on the estimated emotions. For example, if the user is stressed, the review unit may display the review results concisely. If the user is relaxed, the review unit may display the review results in detail. If the user is excited, the review unit may also display the review results calmly. This allows for more appropriate review results to be provided by adjusting the order in which the review results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the review unit may be performed using AI, for example, or not using AI. For example, the review unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The review department considers the geographical distribution of ideas during the review process. For example, the review department may refer to relevant geographical data to assess the geographical distribution of ideas. The review department may collect regional market research data to assess the geographical distribution of ideas. The review department may also collect regional consumer opinions to assess the geographical distribution of ideas. This improves the accuracy of the review process by considering the geographical distribution of ideas. Some or all of the above processes in the review department may be performed using AI, for example, or not. For example, the review department may have a generative AI perform tasks such as referencing geographical data or collecting market research data.

[0092] The review department improves the accuracy of its review by referring to relevant literature on the idea during the review process. The review department improves the accuracy of its review by referring to relevant literature on the idea during the review process. For example, the review department can refer to relevant literature on the idea to understand the technical background. The review department can refer to relevant literature on the idea to compare it with existing technologies. The review department can also refer to relevant literature on the idea to clarify the technical challenges. In this way, the accuracy of the review is improved by referring to relevant literature on the idea. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department can have a generative AI perform the tasks of referring to relevant literature and understanding the technical background.

[0093] The service provider estimates the user's emotions and adjusts the content of the demos and tools provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple demos and tools. If the user is relaxed, the service provider can provide detailed demos and tools. If the user is excited, the service provider can provide visually stimulating demos and tools. This allows for the provision of more appropriate demos and tools by adjusting the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider selects the most suitable demos and tools by referring to the user's past usage history at the time of delivery. The service provider selects the most suitable demos and tools by referring to the user's past usage history at the time of delivery. For example, the service provider proposes the most suitable demos and tools based on the demos and tools the user has used in the past. The service provider can prioritize providing demos and tools that the user has used frequently based on the user's past usage history. The service provider can also analyze the user's past usage history and provide demos and tools that are tailored to the purpose of use. This allows the service provider to select the most suitable demos and tools by referring to the user's past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past usage history into a generating AI and have the generating AI select the most suitable demos and tools.

[0095] The service provider estimates the user's emotions and adjusts the order of demos and tools offered based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize simple demos and tools. If the user is relaxed, the service provider may prioritize detailed demos and tools. If the user is excited, the service provider may prioritize visually stimulating demos and tools. This allows for the provision of more appropriate demos and tools by adjusting the order of demos and tools according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The service provider will provide the most suitable demos and tools at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide demos and tools adapted to the screen size. If the user is using a tablet, the service provider can provide demos and tools optimized for a larger screen. If the user is using a smartwatch, the service provider can also provide concise and highly visible demos and tools. In this way, the service provider can provide the most suitable demos and tools by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI perform the task of providing the most suitable demos and tools.

[0097] The management unit estimates the user's emotions and adjusts the idea transmission method based on the estimated user emotions. The management unit estimates the user's emotions and adjusts the idea transmission method based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple transmission method. If the user is relaxed, the management unit can provide a detailed transmission method. If the user is excited, the management unit can also provide a visually stimulating transmission method. This allows for the provision of a more appropriate transmission method by adjusting the idea transmission method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The management department selects the optimal idea inheritance method by referring to the user's past idea inheritance history during management. The management department can, for example, analyze the trends of ideas inherited by the user in the past and encourage inheritance on similar themes. The management department can refer to the user's past successful inheritance methods. The management department can also predict and suggest inheritance methods to be used at specific times based on the user's past inheritance history. This allows the optimal inheritance method to be selected by referring to the user's past idea inheritance history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past idea inheritance history into a generating AI and have the generating AI select the optimal inheritance method.

[0099] The management unit estimates the user's emotions and adjusts the order of idea inheritance based on the estimated emotions. The management unit estimates the user's emotions and adjusts the order of idea inheritance based on the estimated emotions. For example, if the user is stressed, the management unit may simplify the inheritance order. If the user is relaxed, the management unit may provide a detailed inheritance order. If the user is in a hurry, the management unit may also adjust the order to allow for quick inheritance. This allows for more appropriate inheritance by adjusting the order of idea inheritance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The management unit provides the optimal inheritance method during management, taking into account the user's device information. For example, if the user is using a smartphone, the management unit can provide an inheritance method adapted to the screen size. If the user is using a tablet, the management unit can provide an inheritance method optimized for a larger screen. If the user is using a smartwatch, the management unit can also provide a concise and highly visible inheritance method. This allows the management unit to provide the optimal inheritance method by considering the user's device information. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal inheritance method.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The evaluation unit can also estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation criteria can be relaxed to make the evaluation easier. If the user is relaxed, the evaluation criteria can be made stricter to allow for a more detailed evaluation. If the user is excited, the evaluation criteria can be adjusted to encourage a calmer evaluation. By adjusting the evaluation criteria according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The collection unit can also analyze a user's past idea submission history and select the optimal collection method. For example, it can analyze the trends of ideas a user has submitted in the past and encourage submissions on similar themes. It can analyze the success rate of ideas a user has submitted in the past and encourage submissions on themes that are more likely to succeed. It can also analyze the evaluations of ideas a user has submitted in the past and encourage submissions on themes that are more likely to receive high evaluations. In this way, the optimal collection method can be selected by analyzing a user's past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input a user's past idea submission history into a generating AI and have the generating AI select the optimal collection method.

[0104] The service provider can also estimate the user's emotions and adjust the content of the demos and tools offered based on the estimated emotions. For example, if the user is stressed, a simple demo or tool can be offered. If the user is relaxed, a detailed demo or tool can be offered. If the user is excited, a visually stimulating demo or tool can be offered. This allows for the provision of more appropriate demos and tools by adjusting the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The management unit can also estimate the user's emotions and adjust the idea transfer method based on the estimated emotions. For example, if the user is stressed, a simple transfer method can be provided. If the user is relaxed, a detailed transfer method can be provided. If the user is excited, a visually stimulating transfer method can be provided. This allows for a more appropriate transfer method to be provided by adjusting the idea transfer method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The review unit can also estimate the user's emotions and adjust the review criteria based on the estimated emotions. For example, if the user is stressed, the review criteria can be relaxed to make the review easier. If the user is relaxed, the review criteria can be made stricter to allow for a more detailed review. If the user is excited, the review criteria can be adjusted to encourage a calmer review. By adjusting the review criteria according to the user's emotions, a more appropriate review becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the review unit may be performed using AI, or not using AI. For example, the review unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The collection unit can also filter ideas based on the user's current projects and areas of interest. For example, it can prioritize collecting ideas related to the user's current projects, ideas related to areas of interest, and ideas related to areas the user has shown interest in in the past. This allows for the collection of highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0108] The evaluation unit can also conduct a detailed analysis of the technical feasibility of an idea during the evaluation process. For example, it can refer to relevant technical literature to assess the technical feasibility of an idea. It can also gather expert opinions to assess the technical feasibility of an idea. It can even conduct actual technical tests to assess the technical feasibility of an idea. This improves the accuracy of the evaluation by allowing for a detailed analysis of the technical feasibility of an idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can have a generating AI perform tasks such as referring to technical literature or gathering expert opinions.

[0109] The reception department can also select the optimal reception method by referring to the user's past evaluation history at the time of reception. For example, it can prioritize suggesting evaluation methods that the user has frequently used in the past. It can also refer to evaluation methods for ideas that the user has previously received high ratings for. It can also predict and suggest evaluation methods to be used during specific time periods based on the user's past evaluation history. In this way, the optimal reception method can be selected by referring to the user's past evaluation history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's past evaluation history into a generating AI and have the generating AI select the optimal reception method.

[0110] The service provider can also provide optimal demos and tools by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, it can provide demos and tools that are adapted to the screen size. If the user is using a tablet, it can provide demos and tools optimized for a larger screen. If the user is using a smartwatch, it can provide concise and highly visible demos and tools. In this way, the service provider can provide optimal demos and tools by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI execute the provision of optimal demos and tools.

[0111] The management department can also select the optimal idea inheritance method by referring to the user's past idea inheritance history during management. For example, it can analyze the trends of ideas the user has inherited in the past and encourage inheritance on similar themes. Users can refer to inheritance methods that have been successful in the past. It can also predict and suggest inheritance methods to be used at specific times based on the user's past inheritance history. In this way, the optimal inheritance method can be selected by referring to the user's past idea inheritance history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past idea inheritance history into a generating AI and have the generating AI select the optimal inheritance method.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit aggregates the generated AI ideas on the posting site. The collection unit stores and manages the user-submitted ideas in a database. Furthermore, it can analyze and classify the content of the ideas using the generated AI. For example, it can classify the content of ideas based on keywords and group related ideas. It can also summarize the content of ideas using the generated AI and provide it to other users. Step 2: The evaluation unit evaluates the ideas aggregated by the collection unit. The evaluation unit uses a patent search agent to check whether the submitted ideas overlap with existing patents. It also uses a news search agent to collect the latest technology trends and competitive information and evaluates the novelty of the ideas. Furthermore, it uses a technology evaluation agent to evaluate the technical feasibility of the ideas. These processes may or may not be performed using AI. Step 3: The reception department receives user feedback on ideas evaluated by the evaluation department. The reception department provides an interface for users to evaluate other ideas, and stores and manages user evaluations in a database. For example, users can evaluate ideas with comments such as "I tried it," "I inherited this idea," or "I'd like to see this feature added." The reception department compiles user evaluations and determines the ranking of the ideas. Step 4: The review department makes highly-rated ideas from the reception department more likely to receive expert review and advice. The review department notifies experts of highly-rated ideas and requests detailed review and advice. Expert review results and advice are stored and managed in a database. The review department notifies users of the expert review results and provides feedback. These processes may or may not be performed using AI.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the collection unit, evaluation unit, reception unit, examination unit, provision unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14, which stores and manages user-submitted ideas in a database. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which verifies the novelty of ideas using a patent search agent. The reception unit is implemented by, for example, the control unit 46A of the smart device 14, which receives user evaluations and stores them in a database. The examination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which notifies experts of highly-rated ideas and requests examination and advice. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, which provides a playground and asset store. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which manages the inheritance of ideas. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 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.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the collection unit, evaluation unit, reception unit, review unit, provision unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214, which stores and manages user-submitted ideas in a database. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which verifies the novelty of ideas using a patent search agent. The reception unit is implemented, for example, by the control unit 46A of the smart glasses 214, which receives user evaluations and stores them in a database. The review unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which notifies experts of highly-rated ideas and requests review and advice. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides a Playground and an asset store. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which manages the inheritance of ideas. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, evaluation unit, reception unit, examination unit, provision unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, which stores and manages user-submitted ideas in a database. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which verifies the novelty of ideas using a patent search agent. The reception unit is implemented by, for example, the control unit 46A of the headset terminal 314, which receives user evaluations and stores them in a database. The examination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which notifies experts of highly-rated ideas and requests examination and advice. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides playgrounds and asset stores. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which manages idea inheritance. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, evaluation unit, reception unit, examination unit, provision unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414, which stores and manages user-submitted ideas in a database. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which verifies the novelty of ideas using a patent search agent. The reception unit is implemented by, for example, the control unit 46A of the robot 414, which receives user evaluations and stores them in a database. The examination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which notifies experts of highly-rated ideas and requests examination and advice. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides a playground and asset store. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which manages the inheritance of ideas. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A collection unit that gathers ideas for generational AI on a posting site, An evaluation unit that evaluates the ideas collected by the aforementioned collection unit, A receiving unit that receives user feedback on the ideas evaluated by the aforementioned evaluation unit, The system includes a review department that facilitates expert review and advice for ideas that receive high evaluations from the aforementioned reception department. A system characterized by the following features. (Note 2) It includes a section that provides a playground. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a provisioning unit that provides an asset store. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a management department to manage the transfer of ideas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of idea collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past idea submission history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting ideas, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user emotions and prioritize the ideas to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting ideas, the system prioritizes collecting highly relevant ideas by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering ideas, we analyze users' social media activity and collect relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit described above, During evaluation, the technical feasibility of the idea will be analyzed in detail. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit described above, During evaluation, we improve the accuracy of the evaluation by considering the marketability of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit described above, During the evaluation process, the geographical distribution of ideas will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit described above, During evaluation, refer to relevant literature related to the idea to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is We estimate the user's emotions and adjust the evaluation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is During the registration process, the system will refer to the user's past evaluation history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is The system estimates the user's emotions and adjusts the order in which ratings are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is At the time of registration, the optimal registration method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned review department, We estimate the user's emotions and adjust the review criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned review department, During the review process, we will delve deeper into the technical details of the ideas to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned review department, The system estimates the user's emotions and adjusts the order in which the review results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned review department, During the review process, the geographical distribution of ideas will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned review department, During the review process, we refer to relevant literature related to the idea to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the demos and tools provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the system selects the most suitable demo and tools by referring to the user's past usage history. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order of demos and tools provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we will consider the user's device information to deliver the most suitable demos and tools. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned management department, It estimates the user's emotions and adjusts how ideas are passed on based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 31) The aforementioned management department, During management, the system selects the optimal inheritance method by referring to the user's past idea inheritance history. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned management department, It estimates the user's emotions and adjusts the order in which ideas are passed down based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned management department, During management, the system provides the optimal inheritance method, taking into account the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that gathers ideas for generational AI on a posting site, An evaluation unit that evaluates the ideas collected by the aforementioned collection unit, A receiving unit that receives user feedback on the ideas evaluated by the aforementioned evaluation unit, The system includes a review department that facilitates expert review and advice for highly-rated ideas received by the aforementioned reception department. A system characterized by the following features.

2. It includes a section that provides a playground. The system according to feature 1.

3. It includes a provisioning department that provides an asset store. The system according to feature 1.

4. It has a management department to manage the transfer of ideas. The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of idea collection based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past idea submission history to select the optimal collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting ideas, filter them based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is We estimate user emotions and prioritize the ideas to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting ideas, the system prioritizes collecting highly relevant ideas by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When gathering ideas, we analyze users' social media activity and collect relevant ideas. The system according to feature 1.

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