system

Abstract

The system according to this embodiment aims to analyze patent information and generate and share customizable business ideas. [Solution] The system according to the embodiment comprises an analysis unit, a customization unit, a sharing unit, and a feedback unit. The analysis unit analyzes patent information. The customization unit customizes the business ideas generated by the analysis unit. The sharing unit shares the customized ideas with other users. The feedback unit receives feedback on the ideas shared by the sharing unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating business ideas by utilizing patent information and sharing and improving them among users is not efficiently carried out.

[0005] The system according to the embodiment aims to analyze patent information and generate and share customizable business ideas.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an analysis unit, a customization unit, a sharing unit, and a feedback unit. The analysis unit analyzes patent information. The customization unit customizes the business ideas generated by the analysis unit. The sharing unit shares the customized ideas with other users. The feedback unit receives feedback on the ideas shared by the sharing unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze patent information and generate and share customizable business ideas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business idea generation platform according to an embodiment of the present invention is a system that analyzes patent information specific to a particular industry or application and generates customizable business ideas. This system also includes a community function that allows users to share ideas and collaboratively improve and expand them. The business idea generation platform uses AI to analyze publicly available patents specific to a particular industry or application. The AI ​​analyzes patent documents, performs automatic classification, and generates business ideas for a specific industry. For example, the AI ​​analyzes patent documents to grasp trends in new technologies and inventions and generates new business ideas based on that. Next, the generated business ideas are provided to the user. The user can customize the provided ideas and improve them to suit their own business. For example, the user can receive an idea to develop a new product using a specific technology and adjust that idea to suit their own resources and market needs. Furthermore, the user can share the generated ideas within the community. It is possible to improve and expand ideas in cooperation with other users in the community. For example, a user can share an idea and improve it with feedback from other users. This platform enables the efficient creation of new business ideas specific to a particular industry or application, and the feasibility of the ideas is increased through collaboration among users. For example, the development of new products and services for specific industries can be accelerated, increasing the probability of success. Furthermore, this platform targets both corporations and individuals, supporting new business creation for small and medium-sized enterprises, large corporations, startups, R&D departments, and entrepreneurs in specific industries. For instance, companies, startups, and individual entrepreneurs aiming to create new businesses in specific industries can use this platform to generate new business ideas. Thus, a platform that analyzes patent information specific to particular industries and applications and generates customizable business ideas can improve the probability of success for new businesses through idea sharing and collaboration among users.This allows the business idea generation platform to efficiently create new business ideas tailored to specific industries and applications, and to enhance the feasibility of those ideas through collaboration among users.

[0029] The business idea generation platform according to this embodiment comprises an analysis unit, a customization unit, a sharing unit, and a feedback unit. The analysis unit analyzes patent information. For example, the analysis unit analyzes patent documents and performs automatic classification. The analysis unit can use AI to analyze patent documents and grasp trends in new technologies and inventions. For example, the analysis unit analyzes patent documents using text mining technology and extracts information related to a specific technology field. The analysis unit can also use natural language processing technology to analyze patent documents and grasp technological advancements and invention trends. Furthermore, the analysis unit can use machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. For example, the analysis unit classifies patent documents using clustering technology and groups related technologies and inventions. The customization unit customizes the generated business ideas. For example, the customization unit improves the business ideas to suit the user's business. The customization unit can use AI to customize business ideas. For example, the customization unit adjusts business ideas based on the user's business needs. The customization unit can also improve business ideas to match the user's market needs. Furthermore, the customization section can optimize business ideas based on the user's resources. For example, the customization section adjusts business ideas considering the user's resources to make them feasible. The sharing section allows users to share customized ideas with each other. For example, the sharing section enables users to share their generated business ideas within a community. The sharing section can use AI to support idea sharing. For example, the sharing section suggests the best way to share an idea when the user shares it. The sharing section can also facilitate collaboration between users when the user shares an idea. In addition, the sharing section can help users receive feedback when they share an idea. For example, the sharing section provides a platform for users to share ideas and receive feedback from other users. The feedback section receives feedback on the shared ideas.The feedback unit, for example, collects user feedback and uses it to improve ideas. The feedback unit can use AI to assist in collecting feedback. For example, the feedback unit collects user comments and evaluation scores and uses them to improve ideas. The feedback unit can also collect user survey results and use them to improve ideas. Furthermore, the feedback unit can analyze user feedback and identify areas for improvement in ideas. For example, the feedback unit can analyze user feedback using text mining technology and extract areas for improvement in ideas. As a result, the business idea generation platform according to this embodiment can analyze patent information, generate customizable business ideas, share them among users, and receive feedback.

[0030] The analysis unit analyzes patent information. For example, the analysis unit analyzes patent documents and performs automatic classification. The analysis unit can use AI to analyze patent documents and grasp trends in new technologies and inventions. Specifically, the analysis unit analyzes patent documents using text mining technology and extracts information related to specific technical fields. Text mining technology is a method for extracting useful information from the vast amount of text data in patent documents, and it uses natural language processing technology to understand the content of documents and identify important keywords and phrases. For example, it can extract keywords that indicate new inventions or technological advancements in a specific technical field and grasp trends based on them. Furthermore, the analysis unit can use natural language processing technology to analyze patent documents and grasp technological advancements and trends in inventions. Natural language processing technology is a technology for understanding the meaning of documents and extracting information based on context, and it is possible to analyze the content of patent documents in detail. In addition, the analysis unit can use machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. Machine learning algorithms can automatically classify patent documents by learning from large amounts of data and recognizing patterns. For example, the analysis unit classifies patent documents using clustering technology and groups related technologies and inventions. Clustering technology is a method of grouping data based on similarity, and it can group related technologies and inventions based on the content of patent documents. This allows the analysis unit to efficiently analyze patent information, grasp trends in new technologies and inventions, and generate business ideas for specific industries.

[0031] The customization department customizes the generated business ideas. For example, the customization department improves the business ideas to suit the user's business. Specifically, the customization department can customize business ideas using AI. The AI ​​analyzes the user's business profile and market data to adjust the business ideas based on the user's business needs. For example, the customization department improves the business ideas to suit the user's industry and market based on the user's business profile. The customization department can also improve business ideas to match the user's market needs. To understand market needs, the customization department uses AI to analyze market data and grasp current market trends and consumer preferences. Furthermore, the customization department can optimize business ideas based on the user's resources. User resources include funds, personnel, and technology, and the customization department adjusts the business ideas considering these resources to make them feasible. For example, the customization department can propose low-cost, feasible business ideas considering the user's financial situation. It can also propose technically feasible business ideas considering the user's technical capabilities. This allows the customization department to customize business ideas based on the user's business needs, market needs, and resources, and improve them to the optimal form for the user.

[0032] The Share section allows users to share customized ideas with each other. For example, the Share section enables users to share their generated business ideas within a community. Specifically, the Share section can use AI to support idea sharing. When a user shares an idea, the AI ​​analyzes the user's profile and past sharing history to suggest the best way to share it. For example, based on the user's profile, the Share section suggests the best way to share, helping users share their ideas effectively. The Share section can also facilitate collaboration between users when they share ideas. Specifically, the Share section uses AI to analyze user profiles and interests to identify users who are likely to collaborate. This allows users to build partnerships with other users to realize their business ideas. Furthermore, the Share section can help users receive feedback when they share ideas. For example, the Share section provides a platform for users to share ideas and receive feedback from other users. Feedback is collected in the form of comments, evaluation scores, survey results, etc., and users can use this feedback to improve their ideas. This allows the sharing section to effectively share business ideas generated by users and to support them in improving those ideas through collaboration and feedback from other users.

[0033] The feedback department receives feedback on shared ideas. For example, the feedback department collects user feedback and uses it to improve ideas. Specifically, the feedback department can use AI to assist in collecting feedback. The AI ​​collects comments and evaluation scores from users and analyzes this data to identify areas for improvement in ideas. For example, the feedback department can analyze user comments using text mining techniques to extract areas for improvement in ideas. Text mining is a method for understanding the content of comments and identifying important keywords and phrases, allowing for a detailed understanding of user opinions and requests. The feedback department can also collect survey results from users and use them to improve ideas. Survey results provide information on user satisfaction and areas for improvement, and the feedback department can use this information to refine ideas. Furthermore, the feedback department can analyze user feedback to identify areas for improvement in ideas. For example, the feedback department can use machine learning algorithms to analyze feedback data and identify common areas for improvement and trends. In this way, the feedback department can efficiently collect and analyze user feedback to identify areas for improvement in ideas and improve the quality of business ideas.

[0034] The analysis unit can analyze patent documents and perform automatic classification. For example, the analysis unit can analyze patent documents using text mining techniques to extract information related to a specific technology field. The analysis unit can use AI to analyze patent documents and grasp trends in new technologies and inventions. For example, the analysis unit can analyze patent documents using natural language processing techniques to grasp technological advancements and invention trends. The analysis unit can also use machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. For example, the analysis unit can classify patent documents using clustering techniques to group related technologies and inventions. This allows for the generation of business ideas for specific industries by analyzing and automatically classifying patent documents. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, which can then analyze and automatically classify them.

[0035] The customization unit can improve the generated business idea to suit the user's business. For example, the customization unit can adjust the business idea based on the user's business needs. The customization unit can customize the business idea using AI. For example, the customization unit can improve the business idea to match the user's market needs. The customization unit can also optimize the business idea based on the user's resources. For example, the customization unit can adjust the business idea considering the user's resources to make it feasible. In this way, by improving the generated business idea to suit the user's business, it is possible to provide the user with the best possible idea. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the generated business idea into AI, and the AI ​​can improve the business idea based on the user's business needs.

[0036] The sharing section allows users to share ideas with each other and collaborate to improve and expand those ideas. For example, the sharing section enables users to share their generated business ideas within the community. The sharing section can use AI to support idea sharing. For example, the sharing section can suggest the best way to share an idea when a user shares it. The sharing section can also facilitate collaboration between users when sharing ideas. Furthermore, the sharing section can help users receive feedback when sharing ideas. For example, the sharing section provides a platform for users to share ideas and receive feedback from other users. This increases the feasibility of ideas by allowing users to share ideas and collaborate to improve and expand them. Some or all of the processes described above in the sharing section may or may not be performed using AI. For example, the sharing section can input a user-generated business idea into an AI, which can then suggest the best way to share it.

[0037] The feedback unit can collect user feedback and use it to improve ideas. For example, the feedback unit can collect user feedback and use it to improve ideas. The feedback unit can use AI to assist in collecting feedback. For example, the feedback unit can collect user comments and evaluation scores and use them to improve ideas. The feedback unit can also collect user survey results and use them to improve ideas. Furthermore, the feedback unit can analyze user feedback and identify areas for improvement in ideas. For example, the feedback unit can analyze user feedback using text mining techniques to extract areas for improvement in ideas. This allows for the collection of user feedback and its use in idea improvement, thereby providing better ideas. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input user feedback into AI, which can analyze the feedback and identify areas for improvement in ideas.

[0038] The analysis unit can optimize its analysis algorithm based on the country of issue and language of the patent when analyzing patent documents. For example, if the patent document is written in English, the analysis unit uses an analysis algorithm that takes into account English-specific terminology and expressions. The analysis unit can use AI to optimize its analysis algorithm based on the country of issue and language of the patent document. For example, if the patent document is written in Japanese, the analysis unit uses an analysis algorithm that takes into account Japanese-specific grammar and expressions. Furthermore, if the patent document is written in multiple languages, the analysis unit can combine and use analysis algorithms corresponding to each language. This improves the accuracy of the analysis by optimizing the analysis algorithm based on the country of issue and language of the patent. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the patent document into the AI, and the AI ​​can optimize the analysis algorithm based on the country of issue and language of the patent.

[0039] The analysis unit can apply different analysis methods to each technical field of the patent when analyzing patent documents. For example, the analysis unit can apply molecular biological analysis methods to patent documents in the biotechnology field. The analysis unit can also use AI to apply different analysis methods to each technical field of the patent document. For example, the analysis unit can apply algorithmic analysis methods to patent documents in the IT field. Furthermore, the analysis unit can apply analysis methods specialized in mechanical design to patent documents in the mechanical engineering field. By applying different analysis methods to each technical field of the patent, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can apply different analysis methods to each technical field of the patent.

[0040] The analysis unit can weight the analysis of patent documents based on the year of patent publication. For example, the analysis unit can assign a higher weight to the most recent patent documents and prioritize their analysis. The analysis unit can also use AI to weight the analysis based on the year of patent publication. For example, the analysis unit can assign a lower weight to older patent documents and analyze them as reference information. The analysis unit can also assign an appropriate weight to patent documents with intermediate publication dates to perform a balanced analysis. This improves the accuracy of the analysis by weighting the analysis based on the year of patent publication. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can weight the analysis based on the year of patent publication.

[0041] The analysis unit can improve the accuracy of its analysis by considering patent citation relationships when analyzing patent documents. For example, the analysis unit performs a detailed analysis to improve the accuracy of patent documents with many citation relationships. The analysis unit can improve the accuracy of its analysis by considering patent citation relationships using AI. For example, the analysis unit performs a basic analysis for patent documents with few citation relationships. The analysis unit can also perform an appropriate analysis for patent documents with an intermediate level of citation relationships to provide a balanced result. By improving the accuracy of the analysis by considering patent citation relationships, it is possible to provide more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can improve the accuracy of the analysis by considering patent citation relationships.

[0042] The customization unit can select the optimal customization method when customizing a generated business idea by referring to the user's past business history. For example, the customization unit can suggest a customization method by referring to the user's past successful business models. The customization unit can also select the optimal customization method by referring to the user's past business history using AI. For example, the customization unit can suggest a customization method that avoids business models that have failed in the user's past. The customization unit can also select the most suitable customization method from the user's past business history. In this way, by selecting the optimal customization method by referring to the user's past business history, the customization unit can provide the user with the most suitable customization method. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's past business history into AI, and the AI ​​can select the optimal customization method.

[0043] The customization unit can customize the means of customization based on the user's current market situation when customizing a generated business idea. For example, the customization unit can propose the optimal customization method based on current market trends. The customization unit can use AI to customize the means of customization based on the user's current market situation. For example, the customization unit can adjust the customization method considering the actions of competitors. The customization unit can also select a customization method to match the needs of the user's target market. In this way, by customizing the means of customization based on the user's current market situation, the customization unit can provide the user with the optimal customization method. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's current market situation into AI, and the AI ​​can select the optimal customization method.

[0044] The customization unit can select the optimal customization method when customizing a generated business idea, taking into account the user's geographical location information. For example, the customization unit can propose a customization method based on the market needs of the user's region. The customization unit can use AI to select the optimal customization method, taking into account the user's geographical location information. For example, the customization unit can propose the optimal business model based on the user's geographical location information. The customization unit can also select a customization method, taking into account regulations and laws specific to the user's region. By selecting the optimal customization method, taking into account the user's geographical location information, the customization unit can provide the user with the most suitable customization method. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's geographical location information into the AI, which can then select the optimal customization method.

[0045] The customization unit can analyze the user's social media activity and propose customization methods when customizing a generated business idea. For example, the customization unit can propose the optimal customization method based on the user's social media activity. The customization unit can use AI to analyze the user's social media activity and propose customization methods. For example, the customization unit can select a customization method considering the reactions of the user's followers and friends. The customization unit can also adjust the customization method based on the user's influence on social media. In this way, by analyzing the user's social media activity and proposing customization methods, the customization unit can provide the user with the optimal customization method. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input the user's social media activity into AI, and the AI ​​can propose the optimal customization method.

[0046] The sharing function can select the optimal sharing method when an idea is shared by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method by referring to the user's past successful sharing methods. The sharing function can also use AI to select the optimal sharing method by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method that avoids the user's past unsuccessful sharing methods. The sharing function can also select the most suitable sharing method from the user's past sharing history. In this way, by selecting the optimal sharing method by referring to the user's past sharing history, the sharing function can provide the user with the most suitable sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's past sharing history into AI, and the AI ​​can select the optimal sharing method.

[0047] The sharing function can customize the sharing method based on the user's current network status when sharing ideas. For example, if the user's network is stable, the sharing function will share high-resolution content. The sharing function can also use AI to customize the sharing method based on the user's current network status. For example, if the user's network is unstable, the sharing function will share low-resolution content. The sharing function can also select the optimal sharing method according to the user's network status. This allows the sharing function to provide the user with the most suitable sharing method by customizing the sharing method based on the user's current network status. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's network status into the AI, which can then select the optimal sharing method.

[0048] The sharing function can select the optimal sharing method when sharing an idea, taking into account the user's geographical location. For example, the sharing function can suggest a sharing method based on the market needs of the user's region. The sharing function can use AI to select the optimal sharing method, taking into account the user's geographical location. For example, the sharing function selects the optimal sharing method based on the user's geographical location. The sharing function can also select a sharing method considering regulations and laws specific to the user's region. By selecting the optimal sharing method while considering the user's geographical location, the sharing function can provide the user with the most suitable sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's geographical location into the AI, which can then select the optimal sharing method.

[0049] The sharing function can analyze a user's social media activity and suggest sharing methods when an idea is shared. For example, the sharing function can suggest the optimal sharing method based on the user's social media activity. The sharing function can use AI to analyze a user's social media activity and suggest sharing methods. For example, the sharing function can select a sharing method considering the reactions of the user's followers and friends. The sharing function can also adjust the sharing method based on the user's influence on social media. In this way, by analyzing the user's social media activity and suggesting sharing methods, the sharing function can provide the user with the optimal sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's social media activity into AI, and the AI ​​can suggest the optimal sharing method.

[0050] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can propose the optimal collection method based on the feedback the user has provided in the past. The feedback unit can also select the optimal collection method by referring to the user's past feedback history using AI. For example, the feedback unit can adjust the collection method considering the content of the feedback the user has provided in the past. The feedback unit can also select the most suitable collection method from the user's past feedback history. In this way, by selecting the optimal collection method by referring to the user's past feedback history, the feedback unit can provide the user with the most suitable feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the user's past feedback history into AI, and the AI ​​can select the optimal collection method.

[0051] The feedback unit can customize the means of feedback collection based on the user's current business situation. For example, the feedback unit can suggest the optimal feedback collection method based on the current business situation. The feedback unit can use AI to customize the means of feedback based on the user's current business situation. For example, the feedback unit can adjust the feedback collection method considering the actions of competitors. The feedback unit can also select a feedback collection method that suits the user's business situation. By customizing the means of feedback based on the user's current business situation, the feedback unit can provide the user with the optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current business situation into AI, and the AI ​​can select the optimal feedback collection method.

[0052] The feedback unit can select the optimal feedback collection method by considering the user's geographical location information when collecting feedback. For example, the feedback unit can propose a feedback collection method based on the market needs of the user's region. The feedback unit can use AI to select the optimal feedback collection method by considering the user's geographical location information. For example, the feedback unit selects the optimal feedback collection method based on the user's geographical location information. The feedback unit can also select a feedback collection method by considering regulations and laws specific to the user's region. By selecting the optimal collection method by considering the user's geographical location information, the feedback unit can provide the user with the most optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into the AI, which can then select the optimal feedback collection method.

[0053] The feedback unit can analyze a user's social media activity and suggest methods for collecting feedback when gathering feedback. For example, the feedback unit can suggest the optimal feedback collection method based on the user's social media activity. The feedback unit can use AI to analyze a user's social media activity and suggest methods for collecting feedback. For example, the feedback unit can select a feedback collection method considering the reactions of the user's followers and friends. The feedback unit can also adjust the feedback collection method based on the user's influence on social media. In this way, by analyzing a user's social media activity and suggesting methods for collecting feedback, the feedback unit can provide the user with the optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity into AI, and the AI ​​can suggest the optimal feedback collection method.

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

[0055] The analysis unit can apply different analysis methods to each technical field of the patent when analyzing patent documents. For example, the analysis unit can apply molecular biological analysis methods to patent documents in the biotechnology field. The analysis unit can also use AI to apply different analysis methods to each technical field of the patent document. For example, the analysis unit can apply algorithmic analysis methods to patent documents in the IT field. Furthermore, the analysis unit can apply analysis methods specialized in mechanical design to patent documents in the mechanical engineering field. By applying different analysis methods to each technical field of the patent, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can apply different analysis methods to each technical field of the patent.

[0056] The customization unit can select the optimal customization method when customizing a generated business idea by referring to the user's past business history. For example, the customization unit can suggest a customization method by referring to the user's past successful business models. The customization unit can also select the optimal customization method by referring to the user's past business history using AI. For example, the customization unit can suggest a customization method that avoids business models that have failed in the user's past. The customization unit can also select the most suitable customization method from the user's past business history. In this way, by selecting the optimal customization method by referring to the user's past business history, the customization unit can provide the user with the most suitable customization method. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's past business history into AI, and the AI ​​can select the optimal customization method.

[0057] The analysis unit can weight the analysis of patent documents based on the year of patent publication. For example, the analysis unit can assign a higher weight to the most recent patent documents and prioritize their analysis. The analysis unit can also use AI to weight the analysis based on the year of patent publication. For example, the analysis unit can assign a lower weight to older patent documents and analyze them as reference information. The analysis unit can also assign an appropriate weight to patent documents with intermediate publication dates to perform a balanced analysis. This improves the accuracy of the analysis by weighting the analysis based on the year of patent publication. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can weight the analysis based on the year of patent publication.

[0058] The sharing function can select the optimal sharing method when an idea is shared by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method by referring to the user's past successful sharing methods. The sharing function can also use AI to select the optimal sharing method by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method that avoids the user's past unsuccessful sharing methods. The sharing function can also select the most suitable sharing method from the user's past sharing history. In this way, by selecting the optimal sharing method by referring to the user's past sharing history, the sharing function can provide the user with the most suitable sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's past sharing history into AI, and the AI ​​can select the optimal sharing method.

[0059] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can propose the optimal collection method based on the feedback the user has provided in the past. The feedback unit can also select the optimal collection method by referring to the user's past feedback history using AI. For example, the feedback unit can adjust the collection method considering the content of the feedback the user has provided in the past. The feedback unit can also select the most suitable collection method from the user's past feedback history. In this way, by selecting the optimal collection method by referring to the user's past feedback history, the feedback unit can provide the user with the most suitable feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the user's past feedback history into AI, and the AI ​​can select the optimal collection method.

[0060] The feedback unit can customize the means of feedback collection based on the user's current business situation. For example, the feedback unit can suggest the optimal feedback collection method based on the current business situation. The feedback unit can use AI to customize the means of feedback based on the user's current business situation. For example, the feedback unit can adjust the feedback collection method considering the actions of competitors. The feedback unit can also select a feedback collection method that suits the user's business situation. By customizing the means of feedback based on the user's current business situation, the feedback unit can provide the user with the optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current business situation into AI, and the AI ​​can select the optimal feedback collection method.

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

[0062] Step 1: The analysis unit analyzes patent information. The analysis unit analyzes patent documents and performs automatic classification. It uses AI to analyze patent documents and grasp trends in new technologies and inventions. For example, it uses text mining technology and natural language processing technology to analyze patent documents and grasp technological advancements and invention trends. It also uses machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. Step 2: The customization team customizes the generated business idea. The customization team refines the business idea to suit the user's business. Using AI, they customize the business idea, adjusting and optimizing it based on the user's business needs, market needs, and resources. Step 3: The sharing section allows users to share customized ideas with each other. The sharing section enables users to share their generated business ideas within the community. It uses AI to support idea sharing, suggests the best sharing method, and promotes collaboration with other users. It also provides a platform for receiving feedback. Step 4: The Feedback Department receives feedback on the shared ideas. The Feedback Department collects user feedback to help improve the ideas. AI is used to assist in collecting feedback, gathering comments, rating scores, and survey results, and analyzing them using text mining techniques to identify areas for improvement in the ideas.

[0063] (Example of form 2) The business idea generation platform according to an embodiment of the present invention is a system that analyzes patent information specific to a particular industry or application and generates customizable business ideas. This system also includes a community function that allows users to share ideas and collaboratively improve and expand them. The business idea generation platform uses AI to analyze publicly available patents specific to a particular industry or application. The AI ​​analyzes patent documents, performs automatic classification, and generates business ideas for a specific industry. For example, the AI ​​analyzes patent documents to grasp trends in new technologies and inventions and generates new business ideas based on that. Next, the generated business ideas are provided to the user. The user can customize the provided ideas and improve them to suit their own business. For example, the user can receive an idea to develop a new product using a specific technology and adjust that idea to suit their own resources and market needs. Furthermore, the user can share the generated ideas within the community. It is possible to improve and expand ideas in cooperation with other users in the community. For example, a user can share an idea and improve it with feedback from other users. This platform enables the efficient creation of new business ideas specific to a particular industry or application, and the feasibility of the ideas is increased through collaboration among users. For example, the development of new products and services for specific industries can be accelerated, increasing the probability of success. Furthermore, this platform targets both corporations and individuals, supporting new business creation for small and medium-sized enterprises, large corporations, startups, R&D departments, and entrepreneurs in specific industries. For instance, companies, startups, and individual entrepreneurs aiming to create new businesses in specific industries can use this platform to generate new business ideas. Thus, a platform that analyzes patent information specific to particular industries and applications and generates customizable business ideas can improve the probability of success for new businesses through idea sharing and collaboration among users.This allows the business idea generation platform to efficiently create new business ideas tailored to specific industries and applications, and to enhance the feasibility of those ideas through collaboration among users.

[0064] The business idea generation platform according to this embodiment comprises an analysis unit, a customization unit, a sharing unit, and a feedback unit. The analysis unit analyzes patent information. For example, the analysis unit analyzes patent documents and performs automatic classification. The analysis unit can use AI to analyze patent documents and grasp trends in new technologies and inventions. For example, the analysis unit analyzes patent documents using text mining technology and extracts information related to a specific technology field. The analysis unit can also use natural language processing technology to analyze patent documents and grasp technological advancements and invention trends. Furthermore, the analysis unit can use machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. For example, the analysis unit classifies patent documents using clustering technology and groups related technologies and inventions. The customization unit customizes the generated business ideas. For example, the customization unit improves the business ideas to suit the user's business. The customization unit can use AI to customize business ideas. For example, the customization unit adjusts business ideas based on the user's business needs. The customization unit can also improve business ideas to match the user's market needs. Furthermore, the customization section can optimize business ideas based on the user's resources. For example, the customization section adjusts business ideas considering the user's resources to make them feasible. The sharing section allows users to share customized ideas with each other. For example, the sharing section enables users to share their generated business ideas within a community. The sharing section can use AI to support idea sharing. For example, the sharing section suggests the best way to share an idea when the user shares it. The sharing section can also facilitate collaboration between users when the user shares an idea. In addition, the sharing section can help users receive feedback when they share an idea. For example, the sharing section provides a platform for users to share ideas and receive feedback from other users. The feedback section receives feedback on the shared ideas.The feedback unit, for example, collects user feedback and uses it to improve ideas. The feedback unit can use AI to assist in collecting feedback. For example, the feedback unit collects user comments and evaluation scores and uses them to improve ideas. The feedback unit can also collect user survey results and use them to improve ideas. Furthermore, the feedback unit can analyze user feedback and identify areas for improvement in ideas. For example, the feedback unit can analyze user feedback using text mining technology and extract areas for improvement in ideas. As a result, the business idea generation platform according to this embodiment can analyze patent information, generate customizable business ideas, share them among users, and receive feedback.

[0065] The analysis unit analyzes patent information. For example, the analysis unit analyzes patent documents and performs automatic classification. The analysis unit can use AI to analyze patent documents and grasp trends in new technologies and inventions. Specifically, the analysis unit analyzes patent documents using text mining technology and extracts information related to specific technical fields. Text mining technology is a method for extracting useful information from the vast amount of text data in patent documents, and it uses natural language processing technology to understand the content of documents and identify important keywords and phrases. For example, it can extract keywords that indicate new inventions or technological advancements in a specific technical field and grasp trends based on them. Furthermore, the analysis unit can use natural language processing technology to analyze patent documents and grasp technological advancements and trends in inventions. Natural language processing technology is a technology for understanding the meaning of documents and extracting information based on context, and it is possible to analyze the content of patent documents in detail. In addition, the analysis unit can use machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. Machine learning algorithms can automatically classify patent documents by learning from large amounts of data and recognizing patterns. For example, the analysis unit classifies patent documents using clustering technology and groups related technologies and inventions. Clustering technology is a method of grouping data based on similarity, and it can group related technologies and inventions based on the content of patent documents. This allows the analysis unit to efficiently analyze patent information, grasp trends in new technologies and inventions, and generate business ideas for specific industries.

[0066] The customization department customizes the generated business ideas. For example, the customization department improves the business ideas to suit the user's business. Specifically, the customization department can customize business ideas using AI. The AI ​​analyzes the user's business profile and market data to adjust the business ideas based on the user's business needs. For example, the customization department improves the business ideas to suit the user's industry and market based on the user's business profile. The customization department can also improve business ideas to match the user's market needs. To understand market needs, the customization department uses AI to analyze market data and grasp current market trends and consumer preferences. Furthermore, the customization department can optimize business ideas based on the user's resources. User resources include funds, personnel, and technology, and the customization department adjusts the business ideas considering these resources to make them feasible. For example, the customization department can propose low-cost, feasible business ideas considering the user's financial situation. It can also propose technically feasible business ideas considering the user's technical capabilities. This allows the customization department to customize business ideas based on the user's business needs, market needs, and resources, and improve them to the optimal form for the user.

[0067] The Share section allows users to share customized ideas with each other. For example, the Share section enables users to share their generated business ideas within a community. Specifically, the Share section can use AI to support idea sharing. When a user shares an idea, the AI ​​analyzes the user's profile and past sharing history to suggest the best way to share it. For example, based on the user's profile, the Share section suggests the best way to share, helping users share their ideas effectively. The Share section can also facilitate collaboration between users when they share ideas. Specifically, the Share section uses AI to analyze user profiles and interests to identify users who are likely to collaborate. This allows users to build partnerships with other users to realize their business ideas. Furthermore, the Share section can help users receive feedback when they share ideas. For example, the Share section provides a platform for users to share ideas and receive feedback from other users. Feedback is collected in the form of comments, evaluation scores, survey results, etc., and users can use this feedback to improve their ideas. This allows the sharing section to effectively share business ideas generated by users and to support them in improving those ideas through collaboration and feedback from other users.

[0068] The feedback department receives feedback on shared ideas. For example, the feedback department collects user feedback and uses it to improve ideas. Specifically, the feedback department can use AI to assist in collecting feedback. The AI ​​collects comments and evaluation scores from users and analyzes this data to identify areas for improvement in ideas. For example, the feedback department can analyze user comments using text mining techniques to extract areas for improvement in ideas. Text mining is a method for understanding the content of comments and identifying important keywords and phrases, allowing for a detailed understanding of user opinions and requests. The feedback department can also collect survey results from users and use them to improve ideas. Survey results provide information on user satisfaction and areas for improvement, and the feedback department can use this information to refine ideas. Furthermore, the feedback department can analyze user feedback to identify areas for improvement in ideas. For example, the feedback department can use machine learning algorithms to analyze feedback data and identify common areas for improvement and trends. In this way, the feedback department can efficiently collect and analyze user feedback to identify areas for improvement in ideas and improve the quality of business ideas.

[0069] The analysis unit can analyze patent documents and perform automatic classification. For example, the analysis unit can analyze patent documents using text mining techniques to extract information related to a specific technology field. The analysis unit can use AI to analyze patent documents and grasp trends in new technologies and inventions. For example, the analysis unit can analyze patent documents using natural language processing techniques to grasp technological advancements and invention trends. The analysis unit can also use machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. For example, the analysis unit can classify patent documents using clustering techniques to group related technologies and inventions. This allows for the generation of business ideas for specific industries by analyzing and automatically classifying patent documents. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, which can then analyze and automatically classify them.

[0070] The customization unit can improve the generated business idea to suit the user's business. For example, the customization unit can adjust the business idea based on the user's business needs. The customization unit can customize the business idea using AI. For example, the customization unit can improve the business idea to match the user's market needs. The customization unit can also optimize the business idea based on the user's resources. For example, the customization unit can adjust the business idea considering the user's resources to make it feasible. In this way, by improving the generated business idea to suit the user's business, it is possible to provide the user with the best possible idea. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the generated business idea into AI, and the AI ​​can improve the business idea based on the user's business needs.

[0071] The sharing section allows users to share ideas with each other and collaborate to improve and expand those ideas. For example, the sharing section enables users to share their generated business ideas within the community. The sharing section can use AI to support idea sharing. For example, the sharing section can suggest the best way to share an idea when a user shares it. The sharing section can also facilitate collaboration between users when sharing ideas. Furthermore, the sharing section can help users receive feedback when sharing ideas. For example, the sharing section provides a platform for users to share ideas and receive feedback from other users. This increases the feasibility of ideas by allowing users to share ideas and collaborate to improve and expand them. Some or all of the processes described above in the sharing section may or may not be performed using AI. For example, the sharing section can input a user-generated business idea into an AI, which can then suggest the best way to share it.

[0072] The feedback unit can collect user feedback and use it to improve ideas. For example, the feedback unit can collect user feedback and use it to improve ideas. The feedback unit can use AI to assist in collecting feedback. For example, the feedback unit can collect user comments and evaluation scores and use them to improve ideas. The feedback unit can also collect user survey results and use them to improve ideas. Furthermore, the feedback unit can analyze user feedback and identify areas for improvement in ideas. For example, the feedback unit can analyze user feedback using text mining techniques to extract areas for improvement in ideas. This allows for the collection of user feedback and its use in idea improvement, thereby providing better ideas. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input user feedback into AI, which can analyze the feedback and identify areas for improvement in ideas.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit displays simple and visually easy-to-understand analysis results. The analysis unit can estimate the user's emotions using an emotion estimation algorithm. For example, the analysis unit analyzes the user's facial expressions and voice data to calculate an emotion score. The analysis unit can also display detailed analysis results and provide deeper insights if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results that get straight to the point. For example, the analysis unit analyzes the user's emotion data in real time and immediately grasps changes in emotion. This allows the system to adjust the display method of the analysis results according to the user's emotions, providing the optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI, which can then estimate the emotion and adjust how the analysis results are displayed.

[0074] The analysis unit can optimize its analysis algorithm based on the country of issue and language of the patent when analyzing patent documents. For example, if the patent document is written in English, the analysis unit uses an analysis algorithm that takes into account English-specific terminology and expressions. The analysis unit can use AI to optimize its analysis algorithm based on the country of issue and language of the patent document. For example, if the patent document is written in Japanese, the analysis unit uses an analysis algorithm that takes into account Japanese-specific grammar and expressions. Furthermore, if the patent document is written in multiple languages, the analysis unit can combine and use analysis algorithms corresponding to each language. This improves the accuracy of the analysis by optimizing the analysis algorithm based on the country of issue and language of the patent. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the patent document into the AI, and the AI ​​can optimize the analysis algorithm based on the country of issue and language of the patent.

[0075] The analysis unit can apply different analysis methods to each technical field of the patent when analyzing patent documents. For example, the analysis unit can apply molecular biological analysis methods to patent documents in the biotechnology field. The analysis unit can also use AI to apply different analysis methods to each technical field of the patent document. For example, the analysis unit can apply algorithmic analysis methods to patent documents in the IT field. Furthermore, the analysis unit can apply analysis methods specialized in mechanical design to patent documents in the mechanical engineering field. By applying different analysis methods to each technical field of the patent, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can apply different analysis methods to each technical field of the patent.

[0076] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize displaying analysis results related to the latest technology trends. The analysis unit can estimate the user's emotions using an emotion estimation algorithm. For example, the analysis unit can analyze the user's facial expressions and voice data to calculate an emotion score. The analysis unit can also prioritize displaying detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying concise analysis results. For example, the analysis unit can analyze the user's emotion data in real time and immediately grasp changes in emotion. This allows the analysis unit to prioritize analysis results according to the user's emotions, thereby providing the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI, which can then estimate the emotion and determine the priority of the analysis results.

[0077] The analysis unit can weight the analysis of patent documents based on the year of patent publication. For example, the analysis unit can assign a higher weight to the most recent patent documents and prioritize their analysis. The analysis unit can also use AI to weight the analysis based on the year of patent publication. For example, the analysis unit can assign a lower weight to older patent documents and analyze them as reference information. The analysis unit can also assign an appropriate weight to patent documents with intermediate publication dates to perform a balanced analysis. This improves the accuracy of the analysis by weighting the analysis based on the year of patent publication. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can weight the analysis based on the year of patent publication.

[0078] The analysis unit can improve the accuracy of its analysis by considering patent citation relationships when analyzing patent documents. For example, the analysis unit performs a detailed analysis to improve the accuracy of patent documents with many citation relationships. The analysis unit can improve the accuracy of its analysis by considering patent citation relationships using AI. For example, the analysis unit performs a basic analysis for patent documents with few citation relationships. The analysis unit can also perform an appropriate analysis for patent documents with an intermediate level of citation relationships to provide a balanced result. By improving the accuracy of the analysis by considering patent citation relationships, it is possible to provide more accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can improve the accuracy of the analysis by considering patent citation relationships.

[0079] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, if the user is stressed, the customization unit can provide simple customization options. The customization unit can estimate the user's emotions using an emotion estimation algorithm. For example, the customization unit can analyze the user's facial expressions and voice data to calculate an emotion score. The customization unit can also provide detailed customization options if the user is relaxed. Furthermore, if the user is in a hurry, the customization unit can provide options for quick customization. For example, the customization unit can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the customization method to be adjusted according to the user's emotions, providing the optimal customization method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization section can input user emotion data into the AI, which can then estimate the emotion and adjust the customization method accordingly.

[0080] The customization unit can select the optimal customization method when customizing a generated business idea by referring to the user's past business history. For example, the customization unit can suggest a customization method by referring to the user's past successful business models. The customization unit can also select the optimal customization method by referring to the user's past business history using AI. For example, the customization unit can suggest a customization method that avoids business models that have failed in the user's past. The customization unit can also select the most suitable customization method from the user's past business history. In this way, by selecting the optimal customization method by referring to the user's past business history, the customization unit can provide the user with the most suitable customization method. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's past business history into AI, and the AI ​​can select the optimal customization method.

[0081] The customization unit can customize the means of customization based on the user's current market situation when customizing a generated business idea. For example, the customization unit can propose the optimal customization method based on current market trends. The customization unit can use AI to customize the means of customization based on the user's current market situation. For example, the customization unit can adjust the customization method considering the actions of competitors. The customization unit can also select a customization method to match the needs of the user's target market. In this way, by customizing the means of customization based on the user's current market situation, the customization unit can provide the user with the optimal customization method. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's current market situation into AI, and the AI ​​can select the optimal customization method.

[0082] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is excited, the customization unit will prioritize customizations based on the latest technology trends. The customization unit can estimate the user's emotions using emotion estimation algorithms. For example, the customization unit can analyze the user's facial expressions and voice data to calculate an emotion score. The customization unit can also prioritize detailed customizations if the user is relaxed. Furthermore, if the user is in a hurry, the customization unit can prioritize options that allow for quick customization. For example, the customization unit can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the customization unit to provide the user with the optimal customization method by determining customization priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization section can input user emotion data into an AI, which can then estimate the emotion and determine the priority of customizations.

[0083] The customization unit can select the optimal customization method when customizing a generated business idea, taking into account the user's geographical location information. For example, the customization unit can propose a customization method based on the market needs of the user's region. The customization unit can use AI to select the optimal customization method, taking into account the user's geographical location information. For example, the customization unit can propose the optimal business model based on the user's geographical location information. The customization unit can also select a customization method, taking into account regulations and laws specific to the user's region. By selecting the optimal customization method, taking into account the user's geographical location information, the customization unit can provide the user with the most suitable customization method. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's geographical location information into the AI, which can then select the optimal customization method.

[0084] The customization unit can analyze the user's social media activity and propose customization methods when customizing a generated business idea. For example, the customization unit can propose the optimal customization method based on the user's social media activity. The customization unit can use AI to analyze the user's social media activity and propose customization methods. For example, the customization unit can select a customization method considering the reactions of the user's followers and friends. The customization unit can also adjust the customization method based on the user's influence on social media. In this way, by analyzing the user's social media activity and proposing customization methods, the customization unit can provide the user with the optimal customization method. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input the user's social media activity into AI, and the AI ​​can propose the optimal customization method.

[0085] The sharing function can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is stressed, the sharing function can provide a simple and intuitive sharing method. The sharing function can estimate the user's emotions using an emotion estimation algorithm. For example, the sharing function can analyze the user's facial expressions and voice data to calculate an emotion score. The sharing function can also provide detailed sharing options if the user is relaxed. Furthermore, if the sharing function is in a hurry, it can provide an option for quick sharing. For example, the sharing function can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows it to provide the optimal sharing method for the user by adjusting the sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input user emotion data into an AI, which can then estimate the emotion and adjust the sharing method accordingly.

[0086] The sharing function can select the optimal sharing method when an idea is shared by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method by referring to the user's past successful sharing methods. The sharing function can also use AI to select the optimal sharing method by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method that avoids the user's past unsuccessful sharing methods. The sharing function can also select the most suitable sharing method from the user's past sharing history. In this way, by selecting the optimal sharing method by referring to the user's past sharing history, the sharing function can provide the user with the most suitable sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's past sharing history into AI, and the AI ​​can select the optimal sharing method.

[0087] The sharing function can customize the sharing method based on the user's current network status when sharing ideas. For example, if the user's network is stable, the sharing function will share high-resolution content. The sharing function can also use AI to customize the sharing method based on the user's current network status. For example, if the user's network is unstable, the sharing function will share low-resolution content. The sharing function can also select the optimal sharing method according to the user's network status. This allows the sharing function to provide the user with the most suitable sharing method by customizing the sharing method based on the user's current network status. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's network status into the AI, which can then select the optimal sharing method.

[0088] The sharing function can estimate the user's emotions and determine sharing priorities based on those estimated emotions. For example, if the user is excited, the sharing function will prioritize sharing ideas based on the latest technology trends. The sharing function can estimate the user's emotions using emotion estimation algorithms. For example, the sharing function can analyze the user's facial expressions and voice data to calculate an emotion score. The sharing function can also prioritize sharing detailed ideas if the user is relaxed. Furthermore, if the user is in a hurry, the sharing function can prioritize ideas that can be shared quickly. For example, the sharing function can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the sharing function to provide the user with the optimal sharing method by determining sharing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input user emotion data into an AI, which can then estimate the emotion and determine the priority of sharing.

[0089] The sharing function can select the optimal sharing method when sharing an idea, taking into account the user's geographical location. For example, the sharing function can suggest a sharing method based on the market needs of the user's region. The sharing function can use AI to select the optimal sharing method, taking into account the user's geographical location. For example, the sharing function selects the optimal sharing method based on the user's geographical location. The sharing function can also select a sharing method considering regulations and laws specific to the user's region. By selecting the optimal sharing method while considering the user's geographical location, the sharing function can provide the user with the most suitable sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's geographical location into the AI, which can then select the optimal sharing method.

[0090] The sharing function can analyze a user's social media activity and suggest sharing methods when an idea is shared. For example, the sharing function can suggest the optimal sharing method based on the user's social media activity. The sharing function can use AI to analyze a user's social media activity and suggest sharing methods. For example, the sharing function can select a sharing method considering the reactions of the user's followers and friends. The sharing function can also adjust the sharing method based on the user's influence on social media. In this way, by analyzing the user's social media activity and suggesting sharing methods, the sharing function can provide the user with the optimal sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's social media activity into AI, and the AI ​​can suggest the optimal sharing method.

[0091] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide a simple feedback form. The feedback unit can estimate the user's emotions using an emotion estimation algorithm. For example, the feedback unit can analyze the user's facial expressions and voice data to calculate an emotion score. The feedback unit can also provide a detailed feedback form if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide an option for rapid feedback collection. For example, the feedback unit can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the feedback collection method to be adjusted according to the user's emotions, providing the optimal feedback collection method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into the AI, which can then estimate the emotion and adjust the method of collecting feedback.

[0092] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can propose the optimal collection method based on the feedback the user has provided in the past. The feedback unit can also select the optimal collection method by referring to the user's past feedback history using AI. For example, the feedback unit can adjust the collection method considering the content of the feedback the user has provided in the past. The feedback unit can also select the most suitable collection method from the user's past feedback history. In this way, by selecting the optimal collection method by referring to the user's past feedback history, the feedback unit can provide the user with the most suitable feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the user's past feedback history into AI, and the AI ​​can select the optimal collection method.

[0093] The feedback unit can customize the means of feedback collection based on the user's current business situation. For example, the feedback unit can suggest the optimal feedback collection method based on the current business situation. The feedback unit can use AI to customize the means of feedback based on the user's current business situation. For example, the feedback unit can adjust the feedback collection method considering the actions of competitors. The feedback unit can also select a feedback collection method that suits the user's business situation. By customizing the means of feedback based on the user's current business situation, the feedback unit can provide the user with the optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current business situation into AI, and the AI ​​can select the optimal feedback collection method.

[0094] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is excited, the feedback unit will prioritize collecting feedback based on the latest technology trends. The feedback unit can estimate the user's emotions using emotion estimation algorithms. For example, the feedback unit can analyze the user's facial expressions and voice data to calculate an emotion score. The feedback unit can also prioritize collecting detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can prioritize feedback that can be collected quickly. For example, the feedback unit can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the feedback unit to provide the user with the optimal feedback collection method by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into an AI, which can then estimate the emotion and determine the priority of the feedback.

[0095] The feedback unit can select the optimal feedback collection method by considering the user's geographical location information when collecting feedback. For example, the feedback unit can propose a feedback collection method based on the market needs of the user's region. The feedback unit can use AI to select the optimal feedback collection method by considering the user's geographical location information. For example, the feedback unit selects the optimal feedback collection method based on the user's geographical location information. The feedback unit can also select a feedback collection method by considering regulations and laws specific to the user's region. By selecting the optimal collection method by considering the user's geographical location information, the feedback unit can provide the user with the most optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into the AI, which can then select the optimal feedback collection method.

[0096] The feedback unit can analyze a user's social media activity and suggest methods for collecting feedback when gathering feedback. For example, the feedback unit can suggest the optimal feedback collection method based on the user's social media activity. The feedback unit can use AI to analyze a user's social media activity and suggest methods for collecting feedback. For example, the feedback unit can select a feedback collection method considering the reactions of the user's followers and friends. The feedback unit can also adjust the feedback collection method based on the user's influence on social media. In this way, by analyzing a user's social media activity and suggesting methods for collecting feedback, the feedback unit can provide the user with the optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity into AI, and the AI ​​can suggest the optimal feedback collection method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can apply different analysis methods to each technical field of the patent when analyzing patent documents. For example, the analysis unit can apply molecular biological analysis methods to patent documents in the biotechnology field. The analysis unit can also use AI to apply different analysis methods to each technical field of the patent document. For example, the analysis unit can apply algorithmic analysis methods to patent documents in the IT field. Furthermore, the analysis unit can apply analysis methods specialized in mechanical design to patent documents in the mechanical engineering field. By applying different analysis methods to each technical field of the patent, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can apply different analysis methods to each technical field of the patent.

[0099] The customization unit can select the optimal customization method when customizing a generated business idea by referring to the user's past business history. For example, the customization unit can suggest a customization method by referring to the user's past successful business models. The customization unit can also select the optimal customization method by referring to the user's past business history using AI. For example, the customization unit can suggest a customization method that avoids business models that have failed in the user's past. The customization unit can also select the most suitable customization method from the user's past business history. In this way, by selecting the optimal customization method by referring to the user's past business history, the customization unit can provide the user with the most suitable customization method. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's past business history into AI, and the AI ​​can select the optimal customization method.

[0100] The sharing function can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is stressed, the sharing function can provide a simple and intuitive sharing method. The sharing function can estimate the user's emotions using an emotion estimation algorithm. For example, the sharing function can analyze the user's facial expressions and voice data to calculate an emotion score. The sharing function can also provide detailed sharing options if the user is relaxed. Furthermore, if the sharing function is in a hurry, it can provide an option for quick sharing. For example, the sharing function can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows it to provide the optimal sharing method for the user by adjusting the sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input user emotion data into an AI, which can then estimate the emotion and adjust the sharing method accordingly.

[0101] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide a simple feedback form. The feedback unit can estimate the user's emotions using an emotion estimation algorithm. For example, the feedback unit can analyze the user's facial expressions and voice data to calculate an emotion score. The feedback unit can also provide a detailed feedback form if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide an option for rapid feedback collection. For example, the feedback unit can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the feedback collection method to be adjusted according to the user's emotions, providing the optimal feedback collection method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into the AI, which can then estimate the emotion and adjust the method of collecting feedback.

[0102] The analysis unit can weight the analysis of patent documents based on the year of patent publication. For example, the analysis unit can assign a higher weight to the most recent patent documents and prioritize their analysis. The analysis unit can also use AI to weight the analysis based on the year of patent publication. For example, the analysis unit can assign a lower weight to older patent documents and analyze them as reference information. The analysis unit can also assign an appropriate weight to patent documents with intermediate publication dates to perform a balanced analysis. This improves the accuracy of the analysis by weighting the analysis based on the year of patent publication. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patent documents into AI, and the AI ​​can weight the analysis based on the year of patent publication.

[0103] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, if the user is stressed, the customization unit can provide simple customization options. The customization unit can estimate the user's emotions using an emotion estimation algorithm. For example, the customization unit can analyze the user's facial expressions and voice data to calculate an emotion score. The customization unit can also provide detailed customization options if the user is relaxed. Furthermore, if the user is in a hurry, the customization unit can provide options for quick customization. For example, the customization unit can analyze the user's emotion data in real time and instantly grasp changes in emotion. This allows the customization method to be adjusted according to the user's emotions, providing the optimal customization method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization section can input user emotion data into the AI, which can then estimate the emotion and adjust the customization method accordingly.

[0104] The sharing function can select the optimal sharing method when an idea is shared by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method by referring to the user's past successful sharing methods. The sharing function can also use AI to select the optimal sharing method by referring to the user's past sharing history. For example, the sharing function can suggest a sharing method that avoids the user's past unsuccessful sharing methods. The sharing function can also select the most suitable sharing method from the user's past sharing history. In this way, by selecting the optimal sharing method by referring to the user's past sharing history, the sharing function can provide the user with the most suitable sharing method. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's past sharing history into AI, and the AI ​​can select the optimal sharing method.

[0105] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can propose the optimal collection method based on the feedback the user has provided in the past. The feedback unit can also select the optimal collection method by referring to the user's past feedback history using AI. For example, the feedback unit can adjust the collection method considering the content of the feedback the user has provided in the past. The feedback unit can also select the most suitable collection method from the user's past feedback history. In this way, by selecting the optimal collection method by referring to the user's past feedback history, the feedback unit can provide the user with the most suitable feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the user's past feedback history into AI, and the AI ​​can select the optimal collection method.

[0106] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize displaying analysis results related to the latest technology trends. The analysis unit can estimate the user's emotions using an emotion estimation algorithm. For example, the analysis unit can analyze the user's facial expressions and voice data to calculate an emotion score. The analysis unit can also prioritize displaying detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying concise analysis results. For example, the analysis unit can analyze the user's emotion data in real time and immediately grasp changes in emotion. This allows the analysis unit to prioritize analysis results according to the user's emotions, thereby providing the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI, which can then estimate the emotion and determine the priority of the analysis results.

[0107] The feedback unit can customize the means of feedback collection based on the user's current business situation. For example, the feedback unit can suggest the optimal feedback collection method based on the current business situation. The feedback unit can use AI to customize the means of feedback based on the user's current business situation. For example, the feedback unit can adjust the feedback collection method considering the actions of competitors. The feedback unit can also select a feedback collection method that suits the user's business situation. By customizing the means of feedback based on the user's current business situation, the feedback unit can provide the user with the optimal feedback collection method. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current business situation into AI, and the AI ​​can select the optimal feedback collection method.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analysis unit analyzes patent information. The analysis unit analyzes patent documents and performs automatic classification. It uses AI to analyze patent documents and grasp trends in new technologies and inventions. For example, it uses text mining technology and natural language processing technology to analyze patent documents and grasp technological advancements and invention trends. It also uses machine learning algorithms to automatically classify patent documents and generate business ideas for specific industries. Step 2: The customization team customizes the generated business idea. The customization team refines the business idea to suit the user's business. Using AI, they customize the business idea, adjusting and optimizing it based on the user's business needs, market needs, and resources. Step 3: The sharing section allows users to share customized ideas with each other. The sharing section enables users to share their generated business ideas within the community. It uses AI to support idea sharing, suggests the best sharing method, and promotes collaboration with other users. It also provides a platform for receiving feedback. Step 4: The Feedback Department receives feedback on the shared ideas. The Feedback Department collects user feedback to help improve the ideas. AI is used to assist in collecting feedback, gathering comments, rating scores, and survey results, and analyzing them using text mining techniques to identify areas for improvement in the ideas.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the analysis unit, customization unit, sharing unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes patent documents and performs automatic classification. The customization unit is implemented by the control unit 46A of the smart device 14, which adjusts business ideas based on the user's business needs. The sharing unit is implemented by the control unit 46A of the smart device 14, which enables users to share their generated business ideas within the community. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects feedback from users and uses it to improve ideas. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the analysis unit, customization unit, sharing unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes patent documents and performs automatic classification. The customization unit is implemented by the control unit 46A of the smart glasses 214, which adjusts business ideas based on the user's business needs. The sharing unit is implemented by the control unit 46A of the smart glasses 214, which enables users to share their generated business ideas within the community. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects feedback from users and uses it to improve ideas. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the analysis unit, customization unit, sharing unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes patent documents and performs automatic classification. The customization unit is implemented by the control unit 46A of the headset terminal 314, which adjusts business ideas based on the user's business needs. The sharing unit is implemented by the control unit 46A of the headset terminal 314, which enables users to share their generated business ideas within the community. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects feedback from users and uses it to improve ideas. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the analysis unit, customization unit, sharing unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes patent documents and performs automatic classification. The customization unit is implemented by the control unit 46A of the robot 414, which adjusts business ideas based on the user's business needs. The sharing unit is implemented by the control unit 46A of the robot 414, which enables users to share their generated business ideas within the community. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects feedback from users and uses it to improve ideas. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) An analysis unit that analyzes patent information, A customization unit that customizes the business ideas generated by the aforementioned analysis unit, A sharing unit for sharing ideas customized by the aforementioned customization unit among users, The system includes a feedback unit that receives feedback on the ideas shared by the sharing unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze patent documents and perform automatic classification. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned customization unit is The generated business idea is refined to suit the user's business. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned share section is Users share ideas with each other and collaborate to improve and expand those ideas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is We collect user feedback to help improve our ideas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing patent documents, optimize the analysis algorithm based on the country of issue and language of the patent. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing patent documents, different analysis methods are applied depending on the technical field of the patent. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing patent documents, weighting of the analysis is performed based on the patent's publication year. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing patent documents, consider the citation relationships of the patents to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned customization unit is When customizing the generated business idea, the system selects the optimal customization method by referring to the user's past business history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned customization unit is When customizing the generated business idea, the customization method is customized based on the user's current market conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned customization unit is When customizing the generated business idea, the system selects the optimal customization method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned customization unit is When customizing the generated business idea, we analyze the user's social media activity and suggest ways to customize it. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned share section is It estimates the user's emotions and adjusts the sharing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned share section is When sharing ideas, the system selects the optimal sharing method by referring to the user's past sharing history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned share section is When sharing ideas, the sharing method is customized based on the user's current network status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned share section is It estimates user sentiment and determines share priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned share section is When sharing ideas, the optimal sharing method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned share section is When sharing ideas, we analyze users' social media activity and suggest ways to share them. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When collecting feedback, customize the feedback method based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When collecting feedback, we analyze users' social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes patent information, A customization unit that customizes the business ideas generated by the aforementioned analysis unit, A sharing unit for sharing ideas customized by the aforementioned customization unit among users, The system includes a feedback unit that receives feedback on the ideas shared by the sharing unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze patent documents and perform automatic classification. The system according to feature 1.

3. The aforementioned customization unit is The generated business idea is refined to suit the user's business. The system according to feature 1.

4. The aforementioned share section is Users share ideas with each other and collaborate to improve and expand those ideas. The system according to feature 1.

5. The aforementioned feedback unit is We collect user feedback to help improve our ideas. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing patent documents, optimize the analysis algorithm based on the country of issue and language of the patent. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing patent documents, different analysis methods are applied depending on the technical field of the patent. The system according to feature 1.

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