System

Abstract

An object of the system according to the embodiment is to make the process of idea generation and brainstorming efficient and to produce high-quality ideas.SOLUTION: A system includes an idea input part, an idea expansion part, and a brainstorming support part. The idea input unit inputs a seed of an idea possessed by a user. The idea extension unit extends the idea based on the seed of the idea input by the idea input unit. The brain storming support unit supports brain storming based on the idea generated by the idea extension unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the idea generation and brainstorming process is inefficient, making it difficult to produce high-quality ideas.

[0005] The system according to the embodiment aims to streamline the idea generation and brainstorming process and produce high-quality ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes an idea input unit, an idea expansion unit, and a brainstorming support unit. The idea input unit inputs seed ideas that a user has. The idea expansion unit expands ideas based on the seed ideas input by the idea input unit. The brainstorming support unit supports brainstorming based on ideas generated by the idea expansion unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the idea generation and brainstorming process and produce high quality ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The idea generation support system according to an embodiment of the present invention is a system in which a user inputs the seed of an idea, and a generation AI expands on the seed and cultivates the seeds of various ideas. As a result, the idea generation support system can support the entire idea cycle, from brainstorming ideas to realizing them and even analyzing potential market needs.

[0029] An idea generation support system according to an embodiment includes an idea input unit, an idea expansion unit, and a brainstorming support unit. The idea input unit inputs a user's idea seed. For example, the user may input an idea such as "an idea for a new eco-friendly product." The idea input unit can also send the user's input idea seed as a prompt to the generation AI. For example, the idea input unit instructs the generation AI to "expand this idea." The idea expansion unit expands ideas based on the idea seed input by the idea input unit. For example, the generation AI generates specific ideas such as "household appliances that use renewable energy" or "fashion items made from recycled materials" from the seed "eco-friendly product." The generation AI can also propose various ideas based on user input using a pre-finished model. For example, the generation AI can refer to past success stories and failure stories to perform risk assessment. The brainstorming support unit supports brainstorming based on ideas generated by the idea expansion unit. For example, if the generation AI proposes the idea "household appliances that use renewable energy," the brainstorming support unit proposes additional ideas and improvements related to the idea. The brainstorming support unit also allows the user to provide feedback on ideas proposed by the generation AI. For example, the user may provide feedback such as, "This idea is good, but I'd like a more specific implementation plan." This allows the idea generation support system according to the embodiment to streamline the idea generation process and improve its quality. For example, the user can quickly and effectively generate and materialize ideas for new products and services. The generation AI can also suggest optimal ideas based on the user's input.

[0030] The idea expansion unit can refer to past success and failure cases for idea seeds and perform risk assessment. For example, for an idea seed entered by a user, the generation AI searches a database for past success and failure cases and performs risk assessment. For example, it analyzes whether similar ideas have been successful or failed in the past and presents the results to the user. The idea expansion unit can also suggest improvements to the idea based on the results of the risk assessment by the generation AI. For example, for an idea that is determined to be high risk, it will suggest improvements to reduce the risk. In this way, by performing risk assessment of ideas, the probability of success can be increased.

[0031] When a user inputs the seed of an idea, the idea input unit allows the generation AI to automatically search for related patent information and clearly show the differences from existing technologies. For example, when a user inputs the seed of an idea, the idea input unit allows the generation AI to automatically search for related patent information and clearly show the differences from existing technologies. For example, it may extract related patents from a patent database and evaluate the novelty of the idea. The idea input unit also allows the generation AI to suggest improvements to the idea based on patent information. For example, it may suggest improvements to emphasize the differences from existing technologies. This allows the novelty of the idea to be evaluated and the possibility of obtaining a patent increased.

[0032] When a user inputs an idea seed, the idea input unit allows the generation AI to provide trend information from different industries and fields, thereby promoting idea diversity. For example, when a user inputs an idea seed, the idea input unit allows the generation AI to provide trend information from different industries and fields, thereby promoting idea diversity. For example, the latest technological trends and market trends are presented. The idea input unit also allows the generation AI to suggest improvements to ideas based on trend information. For example, the idea input unit can suggest ideas that combine technologies from different industries. This allows the generation AI to provide trend information from different industries and fields, thereby promoting idea diversity.

[0033] When a user inputs the seed of an idea, the idea input unit allows the generative AI to present related academic papers and research results, thereby providing scientific support. For example, when a user inputs the seed of an idea, the idea input unit allows the generative AI to present related academic papers and research results, thereby providing scientific support. For example, it displays summaries of related papers. The idea input unit also allows the generative AI to suggest improvements to the idea based on academic papers and research results. For example, it suggests improvements to strengthen the scientific support. This makes it possible to provide scientific support for the idea by presenting related academic papers and research results.

[0034] The idea expansion unit can refer to past market data when the generation AI expands an idea and prioritize generating ideas with a high probability of success. For example, when the generation AI expands an idea, the idea expansion unit can refer to past market data and prioritize generating ideas with a high probability of success. For example, it can analyze past market trends and suggest ideas with a high probability of success. The idea expansion unit can also suggest improvements to the idea that the generation AI makes based on market data. For example, it can suggest improvements to increase the probability of success. This makes it possible to increase the feasibility of ideas by prioritizing the generation of ideas with a high probability of success.

[0035] The idea expansion unit can perform an environmental impact assessment when the generation AI expands an idea, and propose highly sustainable ideas. For example, the idea expansion unit can perform an environmental impact assessment when the generation AI expands an idea, and propose highly sustainable ideas. For example, it can propose environmentally friendly materials and manufacturing methods. The idea expansion unit can also suggest improvements to the idea based on the results of the generation AI's environmental impact assessment. For example, it can suggest improvements to reduce environmental impact. This makes it possible to generate environmentally friendly ideas by proposing highly sustainable ideas.

[0036] The idea expansion unit allows the generation AI to generate ideas from a global perspective, taking into account the needs of different cultural spheres and regions when expanding ideas. For example, the idea expansion unit can propose ideas that are tailored to the culture and customs of each region when expanding ideas. The idea expansion unit can also suggest improvements to ideas based on the needs of different cultural spheres and regions. For example, it can propose improvements based on market research for each region. This makes it possible to generate ideas from a global perspective by taking into account the needs of different cultural spheres and regions.

[0037] The idea expansion unit can incorporate the opinions of experts in different technical fields when the generative AI expands an idea, and propose crossover ideas. For example, the idea expansion unit can incorporate the opinions of experts in different technical fields when the generative AI expands an idea, and propose crossover ideas. For example, it can propose an idea that combines technologies in the medical and IT fields. The idea expansion unit can also suggest improvements to an idea based on the opinions of experts. For example, it can propose new application fields by combining different technologies. This makes it possible to propose crossover ideas by incorporating the opinions of experts in different technical fields.

[0038] When the generation AI supports brainstorming, the brainstorming support unit can refer to data from past brainstorming sessions and suggest effective idea generation methods. For example, when the generation AI supports brainstorming, the brainstorming support unit can refer to data from past brainstorming sessions and suggest effective idea generation methods. For example, it can suggest idea generation methods based on past success stories. The brainstorming support unit can also suggest improvements to ideas based on data from the generation AI. For example, it can suggest improvements based on past data. In this way, by referring to data from past brainstorming sessions, it is possible to suggest effective idea generation methods.

[0039] The brainstorming support unit allows the generative AI to provide feedback in real time during the brainstorming process, thereby improving the quality of ideas. For example, the brainstorming support unit allows the generative AI to provide feedback in real time during the brainstorming process, thereby improving the quality of ideas. For example, it provides feedback on the concreteness and feasibility of ideas. The brainstorming support unit can also suggest improvements to ideas based on feedback provided by the generative AI in real time. For example, it can suggest improvements based on feedback. In this way, the quality of ideas can be improved by providing feedback in real time.

[0040] The brainstorming support unit can encourage the diversity of ideas by having the generative AI present success stories from different industries during brainstorming. For example, the brainstorming support unit can encourage the diversity of ideas by having the generative AI present success stories from different industries during brainstorming. For example, the brainstorming support unit can propose ideas based on success stories from different industries. The brainstorming support unit can also encourage the generative AI to suggest improvements to ideas based on success stories. For example, the brainstorming support unit can propose improvements based on success stories. In this way, the diversity of ideas can be promoted by presenting success stories from different industries.

[0041] The brainstorming support unit allows the generating AI to automatically translate into different languages ​​during the brainstorming process and obtain feedback from an international perspective. For example, the brainstorming support unit allows the generating AI to automatically translate into different languages ​​during the brainstorming process and obtain feedback from an international perspective. For example, the brainstorming support unit translates ideas into multiple languages ​​and collects international feedback. The brainstorming support unit can also suggest improvements to ideas based on the translation results of the generating AI. For example, the brainstorming support unit can suggest improvements that incorporate an international perspective. This allows the generating AI to automatically translate into different languages ​​and obtain feedback from an international perspective.

[0042] The idea materialization unit can evaluate manufacturing costs and technical feasibility when the generative AI materializes an idea and propose an optimal implementation plan. For example, when the generative AI materializes an idea, the idea materialization unit can evaluate manufacturing costs and technical feasibility and propose an optimal implementation plan. For example, it can propose methods to reduce manufacturing costs. The idea materialization unit can also suggest improvements to the idea based on the generative AI's technical feasibility. For example, it can propose improvements to overcome technical constraints. In this way, it is possible to propose an optimal implementation plan by evaluating manufacturing costs and technical feasibility.

[0043] The idea materialization unit can ensure compliance by taking into account legal, regulatory, and ethical perspectives when the generative AI materializes an idea. The idea materialization unit can, for example, ensure compliance by taking into account legal, regulatory, and ethical perspectives when the generative AI materializes an idea. For example, it can propose ways to comply with relevant legal regulations. The idea materialization unit can also suggest improvements to the idea by the generative AI based on ethical perspectives. For example, it can propose improvements that take into account social impacts. In this way, compliance can be ensured by taking into account legal, regulatory, and ethical perspectives.

[0044] The idea materialization unit can create new value by combining technologies from different industries when the generative AI materializes an idea. For example, when the generative AI materializes an idea, the idea materialization unit can create new value by combining technologies from different industries. For example, it can propose an idea that combines technologies from the medical and IT fields. The idea materialization unit can also propose improvements to an idea based on technologies from different industries. For example, it can propose new application fields by combining different technologies. This makes it possible to create new value by combining technologies from different industries.

[0045] The idea materialization unit can implement the materialized idea as a prototype and introduce an agile method of improving it based on user feedback. The idea materialization unit, for example, implements the materialized idea as a prototype and introduces an agile method of improving it based on user feedback. For example, a prototype can be developed in a short period of time and user opinions can be reflected. The idea materialization unit can also use the generation AI to suggest improvements to the idea based on user feedback. For example, the AI ​​can suggest improvements based on user feedback. This makes it possible to improve the quality of the idea by implementing a prototype and improving it based on user feedback.

[0046] The market needs analysis unit can predict future market trends by referring to past market data and trend information when the generation AI analyzes market needs. For example, when the generation AI analyzes market needs, the market needs analysis unit can predict future market trends by referring to past market data and trend information. For example, it predicts future market trends based on past data. The market needs analysis unit can also suggest improvements to the idea based on the generation AI's market trend prediction results. For example, it can suggest improvements to meet future market needs. In this way, future market trends can be predicted by referring to past market data and trend information.

[0047] In analyzing market needs, the market needs analysis department allows the generative AI to monitor competitors' trends in real time and propose strategies to ensure a competitive advantage. For example, when the generative AI analyzes market needs, the market needs analysis department monitors competitors' trends in real time and proposes strategies to ensure a competitive advantage. For example, it analyzes competitors' products and services and proposes a differentiation strategy. The market needs analysis department also allows the generative AI to propose improvements to ideas based on the trends of competitors. For example, it proposes improvements to increase competitive advantage. In this way, by monitoring competitors' trends in real time, it is possible to propose strategies to ensure a competitive advantage.

[0048] In analyzing market needs, the market needs analysis department allows the generative AI to compare consumer needs in different regions and cultural spheres and propose market strategies from a global perspective. For example, when the generative AI analyzes market needs, the market needs analysis department compares consumer needs in different regions and cultural spheres and proposes market strategies from a global perspective. For example, it proposes products and services that meet the consumer needs of each region. The market needs analysis department can also suggest improvements to ideas based on the consumer needs of each region and cultural sphere. For example, it proposes improvements that incorporate a global perspective. This makes it possible to propose market strategies from a global perspective by comparing consumer needs in different regions and cultural spheres.

[0049] The market needs analysis unit can integrate the results of the market needs analysis with data from different industries to discover new market opportunities. The market needs analysis unit, for example, can integrate the results of the market needs analysis with data from different industries to discover new market opportunities. For example, it can propose market opportunities based on data from different industries. The market needs analysis unit can also use the generative AI to propose improvements to ideas based on data from different industries. For example, it can propose improvements to meet new market needs. In this way, new market opportunities can be discovered by integrating the results of the market needs analysis with data from different industries.

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

[0051] The idea generation support system can also refer to the user's past idea history and suggest ideas based on the user's tendencies and preferences. For example, if a user has previously shown interest in eco-friendly products, the generation AI can take that tendency into account when suggesting new eco-friendly ideas. It can also analyze the patterns of the user's past successful ideas and prioritize suggesting ideas with similar success patterns. This makes it possible to utilize the user's past history to suggest more personalized ideas.

[0052] The idea generation support system can also generate ideas from a global perspective, taking into account the needs of different cultural spheres and regions. For example, it can propose ideas that are tailored to the culture and customs of each region. The generation AI can also suggest improvements based on market research for each region. This makes it possible to generate ideas from a global perspective by taking into account the needs of different cultural spheres and regions.

[0053] The idea generation support system can also incorporate the opinions of experts in different technical fields to propose crossover ideas. For example, it can propose an idea that combines technologies from the medical and IT fields. The generative AI can also propose improvements to ideas based on the opinions of experts. For example, it can propose new application fields that combine different technologies. In this way, by incorporating the opinions of experts from different technical fields, it is possible to propose crossover ideas.

[0054] The idea generation support system can also perform an environmental impact assessment when the generation AI expands on an idea, and propose highly sustainable ideas. For example, it can suggest environmentally friendly materials and manufacturing methods. The generation AI can also suggest improvements to the idea based on the results of the environmental impact assessment. For example, it can suggest improvements to reduce environmental impact. This makes it possible to generate environmentally friendly ideas by proposing highly sustainable ideas.

[0055] The idea generation support system can also ensure compliance by having the generative AI consider legal and ethical considerations when concretizing ideas. For example, it can suggest ways to comply with relevant laws and regulations. The generative AI can also suggest improvements to ideas based on ethical considerations. For example, it can suggest improvements that take into account social impacts. This ensures compliance by taking legal and ethical considerations into account.

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

[0057] Step 1: The idea input unit inputs the seed of an idea that the user has. For example, the user may input an idea in the form of "an idea for a new eco-friendly product." The idea input unit can also send the seed of the idea that the user inputs as a prompt to the generation AI. For example, the idea input unit can instruct the generation AI to "expand this idea." Step 2: The idea expansion unit expands ideas based on the idea seeds input by the idea input unit. For example, from the seed "eco-friendly products," the generative AI generates specific ideas such as "household appliances that use renewable energy" and "fashion items made from recycled materials." The generative AI can also use a pre-fine-tuned model to propose a variety of ideas based on user input. For example, the generative AI can refer to past successes and failures to perform risk assessments. Step 3: The brainstorming support unit supports brainstorming based on the ideas generated by the idea expansion unit. For example, if the generation AI proposes the idea of ​​"household appliances that use renewable energy," the brainstorming support unit will propose additional ideas and improvements related to that idea. The brainstorming support unit also allows users to provide feedback on ideas proposed by the generation AI. For example, the user could provide feedback such as, "This idea is good, but I'd like a more specific implementation plan."

[0058] (Example 2) The idea generation support system according to an embodiment of the present invention is a system in which a user inputs the seed of an idea, and a generation AI expands on the seed and cultivates the seeds of various ideas. As a result, the idea generation support system can support the entire idea cycle, from brainstorming ideas to realizing them and even analyzing potential market needs.

[0059] An idea generation support system according to an embodiment includes an idea input unit, an idea expansion unit, and a brainstorming support unit. The idea input unit inputs a user's idea seed. For example, the user may input an idea such as "an idea for a new eco-friendly product." The idea input unit can also send the user's input idea seed as a prompt to the generation AI. For example, the idea input unit instructs the generation AI to "expand this idea." The idea expansion unit expands ideas based on the idea seed input by the idea input unit. For example, the generation AI generates specific ideas such as "household appliances that use renewable energy" or "fashion items made from recycled materials" from the seed "eco-friendly product." The generation AI can also propose various ideas based on user input using a pre-finished model. For example, the generation AI can refer to past success stories and failure stories to perform risk assessment. The brainstorming support unit supports brainstorming based on ideas generated by the idea expansion unit. For example, if the generation AI proposes the idea "household appliances that use renewable energy," the brainstorming support unit proposes additional ideas and improvements related to the idea. The brainstorming support unit also allows the user to provide feedback on ideas proposed by the generation AI. For example, the user may provide feedback such as, "This idea is good, but I'd like a more specific implementation plan." This allows the idea generation support system according to the embodiment to streamline the idea generation process and improve its quality. For example, the user can quickly and effectively generate and materialize ideas for new products and services. The generation AI can also suggest optimal ideas based on the user's input.

[0060] The idea expansion unit can refer to past success and failure cases for idea seeds and perform risk assessment. For example, for an idea seed entered by a user, the generation AI searches a database for past success and failure cases and performs risk assessment. For example, it analyzes whether similar ideas have been successful or failed in the past and presents the results to the user. The idea expansion unit can also suggest improvements to the idea based on the results of the risk assessment by the generation AI. For example, for an idea that is determined to be high risk, it will suggest improvements to reduce the risk. In this way, by performing risk assessment of ideas, the probability of success can be increased.

[0061] When a user inputs the seed of an idea, the idea input unit allows the generation AI to automatically search for related patent information and clearly show the differences from existing technologies. For example, when a user inputs the seed of an idea, the idea input unit allows the generation AI to automatically search for related patent information and clearly show the differences from existing technologies. For example, it may extract related patents from a patent database and evaluate the novelty of the idea. The idea input unit also allows the generation AI to suggest improvements to the idea based on patent information. For example, it may suggest improvements to emphasize the differences from existing technologies. This allows the novelty of the idea to be evaluated and the possibility of obtaining a patent increased.

[0062] The idea input unit can use the emotion estimation function to analyze the emotions of the user when inputting the seed of an idea and provide feedback to elicit positive emotions. For example, when the user inputs the seed of an idea, the idea input unit has the generation AI use the emotion estimation function to analyze the user's emotions and provide feedback to elicit positive emotions. For example, an encouraging message is displayed according to the user's input. The idea input unit can also have the generation AI analyze the user's emotions and make suggestions to elicit positive emotions. For example, if the user is feeling negative emotions, the generation AI can suggest relaxation techniques. This can elicit positive emotions from the user and increase motivation to generate ideas.

[0063] When a user inputs an idea seed, the idea input unit allows the generation AI to provide trend information from different industries and fields, thereby promoting idea diversity. For example, when a user inputs an idea seed, the idea input unit allows the generation AI to provide trend information from different industries and fields, thereby promoting idea diversity. For example, the latest technological trends and market trends are presented. The idea input unit also allows the generation AI to suggest improvements to ideas based on trend information. For example, the idea input unit can suggest ideas that combine technologies from different industries. This allows the generation AI to provide trend information from different industries and fields, thereby promoting idea diversity.

[0064] When a user inputs the seed of an idea, the idea input unit allows the generative AI to present related academic papers and research results, thereby providing scientific support. For example, when a user inputs the seed of an idea, the idea input unit allows the generative AI to present related academic papers and research results, thereby providing scientific support. For example, it displays summaries of related papers. The idea input unit also allows the generative AI to suggest improvements to the idea based on academic papers and research results. For example, it suggests improvements to strengthen the scientific support. This makes it possible to provide scientific support for the idea by presenting related academic papers and research results.

[0065] The idea input unit can use the emotion estimation function to monitor the user's emotions in real time when they input idea seeds and make suggestions to alleviate negative emotions. For example, when a user inputs an idea seed, the idea input unit can have the generation AI use the emotion estimation function to monitor the user's emotions in real time and make suggestions to alleviate negative emotions. For example, the generation AI can display an encouraging message based on the user's input. The idea input unit can also have the generation AI analyze the user's emotions and suggest relaxation techniques to alleviate negative emotions. This can alleviate the user's negative emotions and facilitate the idea generation process.

[0066] The idea expansion unit can refer to past market data when the generation AI expands an idea and prioritize generating ideas with a high probability of success. For example, when the generation AI expands an idea, the idea expansion unit can refer to past market data and prioritize generating ideas with a high probability of success. For example, it can analyze past market trends and suggest ideas with a high probability of success. The idea expansion unit can also suggest improvements to the idea that the generation AI makes based on market data. For example, it can suggest improvements to increase the probability of success. This makes it possible to increase the feasibility of ideas by prioritizing the generation of ideas with a high probability of success.

[0067] The idea expansion unit can perform an environmental impact assessment when the generation AI expands an idea, and propose highly sustainable ideas. For example, the idea expansion unit can perform an environmental impact assessment when the generation AI expands an idea, and propose highly sustainable ideas. For example, it can propose environmentally friendly materials and manufacturing methods. The idea expansion unit can also suggest improvements to the idea based on the results of the generation AI's environmental impact assessment. For example, it can suggest improvements to reduce environmental impact. This makes it possible to generate environmentally friendly ideas by proposing highly sustainable ideas.

[0068] The idea expansion unit can analyze the user's emotional response to ideas generated using the emotion estimation function and prioritize ideas that elicit positive emotions. The idea expansion unit, for example, analyzes the user's emotional response to generated ideas using the emotion estimation function and prioritizes ideas that elicit positive emotions. For example, it evaluates ideas based on the user's emotion score. The idea expansion unit can also suggest improvements to the idea based on the generation AI's emotional response. For example, it can suggest improvements that will elicit positive emotions. This can improve the acceptability of ideas by prioritizing ideas that elicit positive emotions from the user.

[0069] The idea expansion unit allows the generation AI to generate ideas from a global perspective, taking into account the needs of different cultural spheres and regions when expanding ideas. For example, the idea expansion unit can propose ideas that are tailored to the culture and customs of each region when expanding ideas. The idea expansion unit can also suggest improvements to ideas based on the needs of different cultural spheres and regions. For example, it can propose improvements based on market research for each region. This makes it possible to generate ideas from a global perspective by taking into account the needs of different cultural spheres and regions.

[0070] The idea expansion unit can incorporate the opinions of experts in different technical fields when the generative AI expands an idea, and propose crossover ideas. For example, the idea expansion unit can incorporate the opinions of experts in different technical fields when the generative AI expands an idea, and propose crossover ideas. For example, it can propose an idea that combines technologies in the medical and IT fields. The idea expansion unit can also suggest improvements to an idea based on the opinions of experts. For example, it can propose new application fields by combining different technologies. This makes it possible to propose crossover ideas by incorporating the opinions of experts in different technical fields.

[0071] The idea expansion unit can monitor the user's emotional reactions to ideas generated using the emotion estimation function in real time and continuously generate optimal ideas. The idea expansion unit, for example, monitors the user's emotional reactions to generated ideas in real time using the emotion estimation function and continuously generates optimal ideas. For example, it evaluates ideas based on the user's emotion score and prioritizes the generation of ideas that receive a large number of positive reactions. The idea expansion unit can also suggest improvements to the idea based on the generation AI's emotional reactions. For example, it can suggest improvements to elicit positive emotions. This allows the quality of ideas to be improved by monitoring the user's emotional reactions in real time and continuously generating optimal ideas.

[0072] When the generation AI supports brainstorming, the brainstorming support unit can refer to data from past brainstorming sessions and suggest effective idea generation methods. For example, when the generation AI supports brainstorming, the brainstorming support unit can refer to data from past brainstorming sessions and suggest effective idea generation methods. For example, it can suggest idea generation methods based on past success stories. The brainstorming support unit can also suggest improvements to ideas based on data from the generation AI. For example, it can suggest improvements based on past data. In this way, by referring to data from past brainstorming sessions, it is possible to suggest effective idea generation methods.

[0073] The brainstorming support unit allows the generative AI to provide feedback in real time during the brainstorming process, thereby improving the quality of ideas. For example, the brainstorming support unit allows the generative AI to provide feedback in real time during the brainstorming process, thereby improving the quality of ideas. For example, it provides feedback on the concreteness and feasibility of ideas. The brainstorming support unit can also suggest improvements to ideas based on feedback provided by the generative AI in real time. For example, it can suggest improvements based on feedback. In this way, the quality of ideas can be improved by providing feedback in real time.

[0074] The brainstorming support unit can use the emotion estimation function to analyze the emotions of participants during brainstorming and suggest interactions to elicit positive emotions. For example, the brainstorming support unit can use the emotion estimation function to analyze the emotions of participants during brainstorming and suggest interactions to elicit positive emotions. For example, it can display encouraging messages based on the participants' emotion scores. The brainstorming support unit can also use the generation AI to analyze the emotions of participants and make suggestions to elicit positive emotions. For example, it can suggest relaxation techniques. This can improve the effectiveness of brainstorming by suggesting interactions that elicit positive emotions from participants.

[0075] The brainstorming support unit can encourage the diversity of ideas by having the generative AI present success stories from different industries during brainstorming. For example, the brainstorming support unit can encourage the diversity of ideas by having the generative AI present success stories from different industries during brainstorming. For example, the brainstorming support unit can propose ideas based on success stories from different industries. The brainstorming support unit can also encourage the generative AI to suggest improvements to ideas based on success stories. For example, the brainstorming support unit can propose improvements based on success stories. In this way, the diversity of ideas can be promoted by presenting success stories from different industries.

[0076] The brainstorming support unit allows the generating AI to automatically translate into different languages ​​during the brainstorming process and obtain feedback from an international perspective. For example, the brainstorming support unit allows the generating AI to automatically translate into different languages ​​during the brainstorming process and obtain feedback from an international perspective. For example, the brainstorming support unit translates ideas into multiple languages ​​and collects international feedback. The brainstorming support unit can also suggest improvements to ideas based on the translation results of the generating AI. For example, the brainstorming support unit can suggest improvements that incorporate an international perspective. This allows the generating AI to automatically translate into different languages ​​and obtain feedback from an international perspective.

[0077] The brainstorming support unit can use the emotion estimation function to monitor the emotions of participants during brainstorming in real time and make suggestions to reduce negative emotions. For example, the brainstorming support unit can use the emotion estimation function to monitor the emotions of participants during brainstorming in real time and make suggestions to reduce negative emotions. For example, the brainstorming support unit can display encouraging messages based on participants' emotion scores. The brainstorming support unit can also use the generation AI to analyze participants' emotions and suggest relaxation techniques to reduce negative emotions. This can improve the effectiveness of brainstorming by making suggestions to reduce participants' negative emotions.

[0078] The idea materialization unit can evaluate manufacturing costs and technical feasibility when the generative AI materializes an idea and propose an optimal implementation plan. For example, when the generative AI materializes an idea, the idea materialization unit can evaluate manufacturing costs and technical feasibility and propose an optimal implementation plan. For example, it can propose methods to reduce manufacturing costs. The idea materialization unit can also suggest improvements to the idea based on the generative AI's technical feasibility. For example, it can propose improvements to overcome technical constraints. In this way, it is possible to propose an optimal implementation plan by evaluating manufacturing costs and technical feasibility.

[0079] The idea materialization unit can ensure compliance by taking into account legal, regulatory, and ethical perspectives when the generative AI materializes an idea. The idea materialization unit can, for example, ensure compliance by taking into account legal, regulatory, and ethical perspectives when the generative AI materializes an idea. For example, it can propose ways to comply with relevant legal regulations. The idea materialization unit can also suggest improvements to the idea by the generative AI based on ethical perspectives. For example, it can propose improvements that take into account social impacts. In this way, compliance can be ensured by taking into account legal, regulatory, and ethical perspectives.

[0080] The idea concretization unit can analyze the user's emotional response to the idea concretized using the emotion estimation function and suggest improvements to elicit positive emotions. The idea concretization unit, for example, analyzes the user's emotional response to the idea concretized using the emotion estimation function and suggests improvements to elicit positive emotions. For example, it improves the idea based on the user's emotion score. The idea concretization unit can also suggest improvements to the idea based on the user's emotional response using the generation AI. For example, it suggests improvements to elicit positive emotions. This makes it possible to improve the acceptability of the concretized idea by suggesting improvements that elicit positive emotions from the user.

[0081] The idea materialization unit can create new value by combining technologies from different industries when the generative AI materializes an idea. For example, when the generative AI materializes an idea, the idea materialization unit can create new value by combining technologies from different industries. For example, it can propose an idea that combines technologies from the medical and IT fields. The idea materialization unit can also propose improvements to an idea based on technologies from different industries. For example, it can propose new application fields by combining different technologies. This makes it possible to create new value by combining technologies from different industries.

[0082] The idea materialization unit can implement the materialized idea as a prototype and introduce an agile method of improving it based on user feedback. The idea materialization unit, for example, implements the materialized idea as a prototype and introduces an agile method of improving it based on user feedback. For example, a prototype can be developed in a short period of time and user opinions can be reflected. The idea materialization unit can also use the generation AI to suggest improvements to the idea based on user feedback. For example, the AI ​​can suggest improvements based on user feedback. This makes it possible to improve the quality of the idea by implementing a prototype and improving it based on user feedback.

[0083] The idea materialization unit monitors in real time the user's emotional response to an idea materialized using the emotion estimation function and can continuously make optimal improvements. The idea materialization unit, for example, monitors in real time the user's emotional response to an idea materialized using the emotion estimation function and can continuously make optimal improvements. For example, it improves the idea based on the user's emotional score. The idea materialization unit can also suggest improvements to the idea based on the user's emotional response using the generation AI. For example, it can suggest improvements to elicit positive emotions. This makes it possible to improve the quality of ideas by monitoring the user's emotional response in real time and continuously making optimal improvements.

[0084] The market needs analysis unit can predict future market trends by referring to past market data and trend information when the generation AI analyzes market needs. For example, when the generation AI analyzes market needs, the market needs analysis unit can predict future market trends by referring to past market data and trend information. For example, it predicts future market trends based on past data. The market needs analysis unit can also suggest improvements to the idea based on the generation AI's market trend prediction results. For example, it can suggest improvements to meet future market needs. In this way, future market trends can be predicted by referring to past market data and trend information.

[0085] In analyzing market needs, the market needs analysis department allows the generative AI to monitor competitors' trends in real time and propose strategies to ensure a competitive advantage. For example, when the generative AI analyzes market needs, the market needs analysis department monitors competitors' trends in real time and proposes strategies to ensure a competitive advantage. For example, it analyzes competitors' products and services and proposes a differentiation strategy. The market needs analysis department also allows the generative AI to propose improvements to ideas based on the trends of competitors. For example, it proposes improvements to increase competitive advantage. In this way, by monitoring competitors' trends in real time, it is possible to propose strategies to ensure a competitive advantage.

[0086] The market needs analysis unit can analyze consumers' emotional responses to ideas generated using the emotion estimation function and propose a marketing strategy to elicit positive emotions. The market needs analysis unit, for example, analyzes consumers' emotional responses to ideas generated using the emotion estimation function and proposes a marketing strategy to elicit positive emotions. For example, it formulates a marketing strategy based on consumers' emotional scores. The market needs analysis unit can also suggest improvements to the marketing strategy based on the consumer's emotional responses from the generation AI. For example, it can suggest improvements to elicit positive emotions. In this way, it is possible to propose a marketing strategy to elicit positive emotions by analyzing consumers' emotional responses.

[0087] In analyzing market needs, the market needs analysis department allows the generative AI to compare consumer needs in different regions and cultural spheres and propose market strategies from a global perspective. For example, when the generative AI analyzes market needs, the market needs analysis department compares consumer needs in different regions and cultural spheres and proposes market strategies from a global perspective. For example, it proposes products and services that meet the consumer needs of each region. The market needs analysis department can also suggest improvements to ideas based on the consumer needs of each region and cultural sphere. For example, it proposes improvements that incorporate a global perspective. This makes it possible to propose market strategies from a global perspective by comparing consumer needs in different regions and cultural spheres.

[0088] The market needs analysis unit can integrate the results of the market needs analysis with data from different industries to discover new market opportunities. The market needs analysis unit, for example, can integrate the results of the market needs analysis with data from different industries to discover new market opportunities. For example, it can propose market opportunities based on data from different industries. The market needs analysis unit can also use the generative AI to propose improvements to ideas based on data from different industries. For example, it can propose improvements to meet new market needs. In this way, new market opportunities can be discovered by integrating the results of the market needs analysis with data from different industries.

[0089] The market needs analysis unit can use the emotion estimation function to monitor consumers' emotional reactions to the market needs analysis results in real time and continuously adjust the optimal market strategy. The market needs analysis unit, for example, uses the emotion estimation function to monitor consumers' emotional reactions to the market needs analysis results in real time and continuously adjust the optimal market strategy. For example, it adjusts the market strategy based on consumers' emotion scores. The market needs analysis unit can also use the generative AI to suggest improvements to the market strategy based on consumers' emotional reactions. For example, it can suggest improvements to elicit positive emotions. In this way, the quality of ideas can be improved by monitoring consumers' emotional reactions in real time and continuously adjusting the optimal market strategy.

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

[0091] The idea generation support system can also refer to the user's past idea history and suggest ideas based on the user's tendencies and preferences. For example, if a user has previously shown interest in eco-friendly products, the generation AI can take that tendency into account when suggesting new eco-friendly ideas. It can also analyze the patterns of the user's past successful ideas and prioritize suggesting ideas with similar success patterns. This makes it possible to utilize the user's past history to suggest more personalized ideas.

[0092] The idea generation support system can also estimate the user's emotions and suggest ideas based on the estimated emotions. For example, if the user is expressing positive emotions when entering ideas, the system can suggest ideas that will further enhance those emotions. Also, if the user is expressing negative emotions, the system can provide relaxation techniques or encouraging messages to alleviate those emotions. This makes it possible to suggest appropriate ideas based on the user's emotions.

[0093] The idea generation support system can also generate ideas from a global perspective, taking into account the needs of different cultural spheres and regions. For example, it can propose ideas that are tailored to the culture and customs of each region. The generation AI can also suggest improvements based on market research for each region. This makes it possible to generate ideas from a global perspective by taking into account the needs of different cultural spheres and regions.

[0094] The idea generation support system can also use its emotion estimation function to analyze participants' emotions during brainstorming sessions and suggest interactions to elicit positive emotions. For example, it can display encouraging messages based on participants' emotion scores. The generation AI can also analyze participants' emotions and make suggestions to elicit positive emotions. This can improve the effectiveness of brainstorming sessions by suggesting interactions that elicit positive emotions from participants.

[0095] The idea generation support system can also incorporate the opinions of experts in different technical fields to propose crossover ideas. For example, it can propose an idea that combines technologies from the medical and IT fields. The generative AI can also propose improvements to ideas based on the opinions of experts. For example, it can propose new application fields that combine different technologies. In this way, by incorporating the opinions of experts from different technical fields, it is possible to propose crossover ideas.

[0096] The idea generation support system also uses an emotion estimation function to monitor the user's emotional response to generated ideas in real time, allowing it to continuously generate optimal ideas. For example, it can evaluate ideas based on the user's emotional score and prioritize the generation of ideas that receive the most positive responses. The generation AI can also suggest improvements to ideas based on the user's emotional response. This allows the system to monitor the user's emotional response in real time and continuously generate optimal ideas, thereby improving the quality of ideas.

[0097] The idea generation support system can also perform an environmental impact assessment when the generation AI expands on an idea, and propose highly sustainable ideas. For example, it can suggest environmentally friendly materials and manufacturing methods. The generation AI can also suggest improvements to the idea based on the results of the environmental impact assessment. For example, it can suggest improvements to reduce environmental impact. This makes it possible to generate environmentally friendly ideas by proposing highly sustainable ideas.

[0098] The idea generation support system can also use its emotion estimation function to analyze the user's emotional response to the concretized idea and suggest improvements to elicit positive emotions. For example, the idea can be improved based on the user's emotion score. The generation AI can also suggest improvements to the idea based on the user's emotional response. This makes it possible to improve the acceptability of the concretized idea by suggesting improvements that will elicit positive emotions from the user.

[0099] The idea generation support system can also ensure compliance by having the generative AI consider legal and ethical considerations when concretizing ideas. For example, it can suggest ways to comply with relevant laws and regulations. The generative AI can also suggest improvements to ideas based on ethical considerations. For example, it can suggest improvements that take into account social impacts. This ensures compliance by taking legal and ethical considerations into account.

[0100] The idea generation support system also uses an emotion estimation function to monitor consumers' emotional reactions to the results of market needs analysis in real time and continuously adjust the optimal market strategy. For example, the market strategy can be adjusted based on consumers' emotional scores. The generation AI can also suggest improvements to the market strategy based on consumers' emotional reactions. This allows the system to monitor consumers' emotional reactions in real time and continuously adjust the optimal market strategy, improving the quality of ideas.

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

[0102] Step 1: The idea input unit inputs the seed of an idea that the user has. For example, the user may input an idea in the form of "an idea for a new eco-friendly product." The idea input unit can also send the seed of the idea that the user inputs as a prompt to the generation AI. For example, the idea input unit can instruct the generation AI to "expand this idea." Step 2: The idea expansion unit expands ideas based on the idea seeds input by the idea input unit. For example, from the seed "eco-friendly products," the generative AI generates specific ideas such as "household appliances that use renewable energy" and "fashion items made from recycled materials." The generative AI can also use a pre-fine-tuned model to propose a variety of ideas based on user input. For example, the generative AI can refer to past successes and failures to perform risk assessments. Step 3: The brainstorming support unit supports brainstorming based on the ideas generated by the idea expansion unit. For example, if the generation AI proposes the idea of ​​"household appliances that use renewable energy," the brainstorming support unit will propose additional ideas and improvements related to that idea. The brainstorming support unit also allows users to provide feedback on ideas proposed by the generation AI. For example, the user could provide feedback such as, "This idea is good, but I'd like a more specific implementation plan."

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0137] 7, 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. an idea input section for inputting seeds of ideas that a user has; an idea expansion unit that expands an idea based on the seed of an idea input by the idea input unit; a brainstorming support unit that supports brainstorming based on the ideas generated by the idea expansion unit. A system characterized by:

2. The idea input unit When the idea seed is input, the generating AI automatically searches for related patent information and clearly shows the differences between the idea seed and existing technologies.

2. The system of claim 1.

3. The idea input unit When inputting the idea seeds, the AI ​​generator provides trend information from different industries and fields, promoting the diversity of the ideas.

2. The system of claim 1.

4. The idea extension unit When the generation AI expands the idea, it refers to market data and prioritizes generating ideas with a high probability of success.

2. The system of claim 1.

5. The brainstorming support unit When the generative AI supports the brainstorming, it refers to data from past brainstorming sessions and suggests effective idea generation methods.

2. The system of claim 1.

6. The idea realization department When the generative AI concretizes the idea, it evaluates manufacturing costs and technical feasibility and proposes the optimal implementation plan.

2. The system of claim 1.

7. The Market Needs Analysis Department When analyzing market needs, the generative AI refers to past market data and trend information to predict future market trends.

2. The system of claim 1.

8. The idea input unit Using an emotion estimation function, the emotion of the user when inputting the idea seed is analyzed, and feedback is provided to elicit the positive emotion.

2. The system of claim 1.

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