system

Abstract

The system according to this embodiment aims to efficiently generate new ideas and verify their novelty. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, a refinement unit, and a proposal unit. The reception unit receives the user's inquiry. The analysis unit analyzes the inquiry received by the reception unit. The search unit searches for existing patents using a patent search API based on the content analyzed by the analysis unit. The refinement unit refines the idea based on the search results obtained by the search unit. The proposal unit proposes the idea refined by the refinement unit to the user.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including: 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 prior art, there is a problem that it is laborious to create a new idea and confirm its novelty, and it is difficult to efficiently generate patent ideas.

[0005] The system according to the embodiment aims to efficiently create a new idea and confirm its novelty.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a refinement unit, and a proposal unit. The reception unit receives the user's inquiry. The analysis unit analyzes the inquiry received by the reception unit. The search unit searches for existing patents using a patent search API based on the content analyzed by the analysis unit. The refinement unit refines the idea based on the search results obtained by the search unit. The proposal unit proposes the idea refined by the refinement unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate new ideas and verify their novelty. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 patent idea generation support system according to an embodiment of the present invention is a system that uses a generative AI to help users efficiently generate new patent ideas. This system provides a mechanism where, when a user consults with the generative AI about a desired patent or a problem they want to solve, the generative AI refines the idea while referring to existing patents, creates a new idea not found in existing patents, and proposes it to the user. For example, a user inputs specific consultation details into the generative AI's chat tool, such as "I want patent ideas about a new smartphone function." This information is input into the generative AI. Next, the generative AI analyzes the input consultation details and searches for existing patents using a patent search API. Based on the search results, the generative AI refers to existing patents related to the user's consultation and refines the idea. For example, it creates a new patent idea by combining technologies and ideas described in existing patents. The generative AI then proposes the refined idea to the user. For example, it proposes a specific idea such as, "As a new smartphone function, we propose a new unlocking method that combines facial recognition technology and voice recognition technology." The user can then proceed with preparing a patent application based on the proposed idea. This mechanism allows users to generate patent ideas very efficiently and create a large number of patent proposals. For example, a company's research and development department can use generative AI to generate many patent ideas in a short period of time, thereby enhancing its competitiveness. Similarly, individual inventors can use generative AI to simplify the patent application process, enabling them to quickly patent new ideas. In this way, patent idea generation support systems can help users efficiently generate new patent ideas.

[0029] The patent idea generation support system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a refinement unit, and a proposal unit. The reception unit receives inquiries from users. For example, a user can input specific inquiries such as "I would like patent ideas for new smartphone features." The reception unit inputs the user's inquiries into the generation AI. The analysis unit analyzes the inquiries received by the reception unit. For example, the analysis unit uses text analysis technology to analyze the user's inquiries and sets search conditions for using a patent search API. The analysis unit can also use data mining technology to analyze the user's inquiries. The search unit searches for existing patents using a patent search API based on the analysis performed by the analysis unit. For example, the search unit performs a patent search using an endpoint of a specific API provider. The search unit can efficiently search for existing patents using a patent search API. The refinement unit refines the ideas based on the search results obtained by the search unit. For example, the refinement unit creates new patent ideas by combining technologies and ideas described in existing patents. The refinement unit can perform the process of modifying and improving ideas. The proposal unit proposes the refined ideas to the user. For example, the proposal unit presents specific examples of the refined ideas to the user. The proposal unit can propose ideas based on the format and evaluation criteria of the proposed content. As a result, the patent idea creation support system according to the embodiment can efficiently analyze the user's consultation content and enable patent searching, idea refinement, and proposal.

[0030] The reception desk receives user inquiries. For example, a user can input a specific inquiry such as, "I would like patent ideas for a new smartphone feature." The reception desk analyzes the user's input using natural language processing technology and converts it into an appropriate format. Specifically, it tokenizes the text entered by the user and extracts important keywords and phrases. This allows for an accurate understanding of the user's intent and requests. Furthermore, the reception desk can refer to the user's past inquiry history and profile information to provide more personalized responses. For example, for users who have made similar inquiries in the past, it can provide suggestions based on their previous inquiries. The reception desk inputs the user's inquiry into a generative AI. The generative AI generates relevant information and ideas based on the user's inquiry. For example, the generative AI researches technologies and market trends related to the patent idea the user is seeking and provides information that is useful to the user. This allows the reception desk to efficiently process user inquiries and provide appropriate information using the generative AI.

[0031] The analysis unit analyzes the content of inquiries received by the reception unit. For example, the analysis unit uses text analysis technology to analyze the user's inquiry and sets search conditions for using the patent search API. Specifically, it uses natural language processing technology to analyze the user's inquiry and extract important keywords and phrases. This allows for an accurate understanding of the user's intentions and requests. The analysis unit can also analyze the user's inquiry using data mining technology. For example, it analyzes past patent databases to identify patents and technologies related to the user's inquiry. Furthermore, the analysis unit can use machine learning algorithms to support the generation of patent ideas based on the user's inquiry. For example, it can use the user's inquiry as input data to train a patent idea generation model and generate new patent ideas. In this way, the analysis unit can analyze the user's inquiry from multiple angles and provide useful information for patent searching and idea generation.

[0032] The search unit searches for existing patents using a patent search API based on the analysis performed by the analysis unit. For example, the search unit performs a patent search using the endpoint of a specific API provider. Specifically, it sends queries to the patent database based on the search conditions set by the analysis unit and searches for relevant patents. The search unit can efficiently search for existing patents using a patent search API. For example, it searches based on information such as the patent title, abstract, claims, and inventor name to identify relevant patents. Furthermore, the search unit can filter the search results and extract the patents most relevant to the user's inquiry. For example, it narrows down the search results by considering the patent publication date, technical field, and patent citation relationships. In this way, the search unit can efficiently perform patent searches based on the user's inquiry and provide relevant patent information.

[0033] The revision unit refines ideas based on search results obtained by the search unit. For example, the revision unit creates new patent ideas by combining technologies and ideas described in existing patents. Specifically, it analyzes patent information obtained from search results and extracts technical features and key points of the invention. This makes it possible to create new ideas based on existing patents. The revision unit can also perform the process of modifying and improving ideas. For example, it can use generative AI to suggest improvements and new perspectives on existing ideas. The generative AI makes suggestions for concretizing and improving ideas based on the user's consultation content and search results. This allows the revision unit to efficiently create new patent ideas based on the user's consultation content and to perform modifications and improvements.

[0034] The proposal unit proposes ideas refined by the revision unit to the user. For example, the proposal unit presents specific examples of the refined ideas to the user. Specifically, it uses a generation AI to generate detailed explanations and examples of the refined ideas and provides them to the user. The proposal unit can propose ideas based on the format and evaluation criteria of the proposal content. For example, it can generate a proposal document in the format of a patent application and provide it to the user. The proposal unit can also collect feedback from the user and improve the proposal content. For example, if the user makes comments or requests revisions to a proposed idea, the proposal unit will reflect this and make a revised proposal. In this way, the proposal unit can propose concrete and practical patent ideas to the user and respond to the user's needs.

[0035] The search unit can search for existing patents using a patent search API. The search unit can perform a patent search using, for example, an endpoint of a specific API provider. The search unit can efficiently search for existing patents using a patent search API. The search unit can extract relevant patents from a patent database using a patent search API. For example, the search unit can search for patents based on specific keywords or filtering conditions using a patent search API. This allows for efficient searching of existing patents using a patent search API. Some or all of the above-described processes in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can input patent data obtained using a patent search API into a generating AI and have the generating AI perform analysis of the patent data.

[0036] The revision unit can create new patent ideas by combining technologies and ideas described in existing patents. For example, the revision unit creates new patent ideas by combining technologies and ideas described in existing patents. The revision unit can refine ideas considering technical compatibility and complementarity of ideas. The revision unit can perform the process of modifying and improving ideas. For example, the revision unit creates new patent ideas by combining technologies and ideas described in existing patents. This makes it possible to efficiently create new patent ideas based on existing patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without using AI. For example, the revision unit can input existing patent data into a generation AI and have the generation AI perform the creation of new patent ideas.

[0037] The proposal unit can propose refined ideas to the user. For example, the proposal unit can show concrete examples of refined ideas and propose them to the user. The proposal unit can propose ideas based on the format and evaluation criteria of the proposal content. The proposal unit can efficiently propose refined ideas to the user. For example, the proposal unit can show concrete examples of refined ideas and propose them to the user. This allows the proposal unit to efficiently propose refined ideas to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input refined ideas into a generation AI and have the generation AI generate the proposal content.

[0038] The analysis unit can analyze the user's consultation content and set search conditions for using the patent search API. For example, the analysis unit can use text analysis technology to analyze the user's consultation content and set search conditions for using the patent search API. The analysis unit can also use data mining technology to analyze the user's consultation content. The analysis unit can set appropriate search conditions based on the user's consultation content. For example, the analysis unit can analyze the user's consultation content and set specific keywords and filtering conditions. This allows for the setting of appropriate search conditions based on the user's consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's consultation content into a generating AI and have the generating AI perform the setting of search conditions.

[0039] The proposal section can provide concrete examples of the proposed idea. For example, the proposal section can show specific implementations of the proposed idea. The proposal section can also show application examples of the proposed idea. The proposal section can also demonstrate the feasibility of the proposed idea. For example, the proposal section can show specific implementations of the proposed idea and propose them to the user. This makes it easier for the user to understand the proposed idea by showing concrete examples. Some or all of the above processing in the proposal section may be performed using AI, for example, or without using AI. For example, the proposal section can input concrete examples of the proposed idea into a generating AI and have the generating AI perform the generation of concrete examples.

[0040] The reception desk can analyze a user's past consultation history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the types of consultations the user has frequently had in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest the types of consultations a user might use at a specific time of day based on their past consultation history. This allows the reception desk to provide the most suitable reception method based on past consultation history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0041] The reception desk can filter inquiries based on the user's current projects and areas of interest at the time of reception. For example, the reception desk can prioritize inquiries related to the user's current projects. The reception desk can also automatically filter relevant inquiries based on the user's areas of interest. The reception desk can also suggest appropriate inquiries according to the progress of the user's projects. This ensures that appropriate inquiries are received based on the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. The reception desk can also suggest inquiries related to region-specific issues based on the user's current location. If the user is on the move, the reception desk can update and prioritize inquiries related to the user's current location in real time. This allows for the priority of receiving appropriate inquiries based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0043] The reception desk can analyze the user's social media activity and accept relevant inquiries at the time of reception. For example, the reception desk can analyze the user's social media posts and suggest relevant inquiries. The reception desk can also prioritize accepting relevant inquiries by referring to the activities of the user's followers and friends. The reception desk can also analyze the user's social media trends and suggest relevant inquiries. This allows for the acceptance of appropriate inquiries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant inquiries.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during the analysis. For example, the analysis unit can perform a detailed analysis for consultation content of high importance. The analysis unit can also perform a concise analysis for consultation content of low importance. The analysis unit can adjust the depth and scope of the analysis according to the importance. This allows for the provision of appropriate analysis results according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit can apply a technology-specific analysis algorithm to technical consultation content. The analysis unit can also apply a legal-specific analysis algorithm to legal consultation content. The analysis unit can also apply a business-specific analysis algorithm to business-related consultation content. This allows the appropriate analysis algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input consultation content category data into a generating AI and have the generating AI select an analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the consultation content was submitted. For example, the analysis unit may prioritize the analysis of the most recently submitted consultation content. The analysis unit may also postpone the analysis of older consultation content. The analysis unit can adjust the order of analysis according to the submission date. This allows for the provision of an appropriate analysis order according to the submission date of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the consultation content submission date data into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the consultation content during the analysis. For example, the analysis unit may analyze consultation content in order of its relevance. The analysis unit may also postpone analyzing consultation content that is less relevant. The analysis unit can determine the priority of analysis according to the relevance of the consultation content. This allows for the provision of an appropriate analysis order according to the relevance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The search unit can improve the accuracy of its search by considering the interrelationships of patents during the search process. For example, the search unit can prioritize searching for highly relevant patents by considering citation relationships. The search unit can also filter search results by considering the relevance of the patents' technical fields. The search unit can also adjust search results by considering the relevance of the patent inventors. This improves the accuracy of the search by considering the interrelationships of patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0049] The search unit can perform searches while considering the attribute information of the patent submitter. For example, the search unit can search for highly relevant patents by considering the patent submitter's area of ​​expertise. The search unit can also adjust the search results by considering the patent submitter's past patent application history. The search unit can also filter the search results by considering the institution to which the patent submitter belongs. This improves the accuracy of the search by considering the attribute information of the patent submitter. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the patent submitter's attribute information data into a generating AI and have the generating AI perform the search accuracy improvement.

[0050] The search unit can perform searches while considering the geographical distribution of patents. For example, the search unit can search for highly relevant patents by considering the country in which the patent was filed. The search unit can also filter search results by considering the geographical distribution of the patent's technical field. The search unit can also adjust search results by considering the geographical distribution of the patent's inventor. This improves the accuracy of the search by considering the geographical distribution of patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the geographical distribution data of patents into a generating AI and have the generating AI perform the task of improving the search accuracy.

[0051] The search unit can improve the accuracy of its search by referring to relevant patent documents during the search process. For example, the search unit can search for highly relevant patents by considering cited patent documents. The search unit can also filter search results by referring to relevant documents in the patent's technical field. The search unit can also refine search results by referring to relevant documents related to the patent's inventor. This improves the accuracy of the search by referring to relevant patent documents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent relevant document data into a generating AI and have the generating AI perform the search accuracy improvement.

[0052] The revision unit can improve the accuracy of revision by considering the interrelationships of technologies and ideas in existing patents during the revision process. For example, the revision unit adjusts the revision results by considering the technical relevance of existing patents. The revision unit can also generate new ideas by considering combinations of ideas in existing patents. The revision unit can also filter the revision results by considering the citation relationships of existing patents. This improves the accuracy of revision by considering the interrelationships of technologies and ideas in existing patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input existing patent data into a generating AI and have the generating AI perform the revision accuracy improvement.

[0053] The revision unit can perform revisions while considering the attribute information of the patent applicant. For example, the revision unit can combine highly relevant technologies and ideas by considering the patent applicant's field of expertise. The revision unit can also adjust the revision results by considering the patent applicant's past patent application history. The revision unit can also filter the revision results by considering the institution to which the patent applicant belongs. This improves the accuracy of revision by considering the attribute information of the patent applicant. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the patent applicant's attribute information data into a generating AI and have the generating AI perform the revision accuracy improvement.

[0054] The revision unit can perform revisions while considering the geographical distribution of patents. For example, the revision unit can combine highly relevant technologies and ideas by considering the countries in which the patents are filed. The revision unit can also filter the revision results by considering the geographical distribution of the patent's technical field. The revision unit can also adjust the revision results by considering the geographical distribution of the patent's inventors. This improves the accuracy of revision by considering the geographical distribution of patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the geographical distribution data of patents into a generating AI and have the generating AI perform the revision accuracy improvement.

[0055] The revision unit can improve the accuracy of the revision by referring to relevant patent documents during the revision process. For example, the revision unit considers cited patent documents and combines highly relevant technologies and ideas. The revision unit can also filter the revision results by referring to relevant documents in the patent's technical field. The revision unit can also adjust the revision results by referring to relevant documents of the patent's inventors. This improves the accuracy of the revision by referring to relevant patent documents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input patent relevant document data into a generating AI and have the generating AI perform the revision accuracy improvement.

[0056] The proposal unit can adjust the level of detail of a proposal based on the importance of the idea. For example, the proposal unit will provide a detailed proposal for a highly important idea. For a less important idea, the proposal unit can provide a concise proposal. The proposal unit can adjust the depth and scope of the proposal according to its importance. This allows it to provide appropriate proposals according to the importance of the idea. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input idea importance data into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0057] The proposal unit can apply different proposal algorithms depending on the category of the idea during the proposal process. For example, the proposal unit can apply a technology-specific proposal algorithm to technical ideas. It can also apply a legal-specific proposal algorithm to legal ideas. It can also apply a business-specific proposal algorithm to business-related ideas. This allows for the application of an appropriate proposal algorithm according to the category of the idea. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input idea category data into a generating AI and have the generating AI select a proposal algorithm.

[0058] The proposal department can determine the priority of proposals based on when the ideas were submitted. For example, the proposal department may prioritize ideas that were submitted most recently. The proposal department may also postpone the submission of older ideas. The proposal department can adjust the order of proposals according to the submission date. This allows for an appropriate order of proposals based on when the ideas were submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input idea submission date data into a generating AI and have the generating AI determine the priority of proposals.

[0059] The proposal unit can adjust the order of proposals based on the relevance of the ideas during the proposal process. For example, the proposal unit may propose ideas in order of their relevance, starting with the most relevant. It can also postpone less relevant ideas. The proposal unit can determine the priority of proposals according to the relevance of the ideas. This allows for the provision of an appropriate order of proposals according to the relevance of the ideas. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input idea relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0060] The proposal unit can provide concrete examples of the idea at the time of proposal. For example, the proposal unit can show specific implementations of the proposed idea. The proposal unit can also show application examples of the proposed idea. The proposal unit can also demonstrate the feasibility of the proposed idea. By providing concrete examples of the idea, it becomes easier for users to understand. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input concrete example data of the proposed idea into a generating AI and have the generating AI perform the generation of concrete examples.

[0061] The proposal unit can evaluate the feasibility of an idea at the time of proposal. For example, the proposal unit can evaluate the technical feasibility of the proposed idea. The proposal unit can also evaluate the marketability of the proposed idea. The proposal unit can also evaluate the legal feasibility of the proposed idea. This allows users to select feasible ideas by evaluating their feasibility. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input feasibility data of the proposed idea into a generating AI and have the generating AI perform the feasibility evaluation.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The analysis unit can analyze the user's past consultation history and select the optimal analysis method. For example, it can automatically display as candidates the topics the user has frequently consulted about in the past. It can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, it can predict and suggest consultation topics that the user will use at specific times based on their past consultation history. This allows the system to provide the optimal analysis method based on past consultation history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past consultation history data into a generating AI and have the generating AI select the optimal analysis method.

[0064] The search unit can filter results based on the user's current projects and areas of interest. For example, it can prioritize searching for consultations related to the user's current projects. It can also automatically filter relevant consultations based on the user's areas of interest. Furthermore, it can suggest appropriate consultations according to the progress of the user's projects. This allows users to find appropriate consultations based on their projects and areas of interest. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0065] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving inquiries related to that region. It can also suggest inquiries related to region-specific issues based on the user's current location. Furthermore, if the user is on the move, it can update and prioritize inquiries related to their current location in real time. This allows for the priority of receiving appropriate inquiries based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during the analysis. For example, it can perform a detailed analysis for consultation content of high importance, and a concise analysis for consultation content of low importance. Furthermore, it can adjust the depth and scope of the analysis according to the importance. This allows for the provision of appropriate analysis results according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0067] The search unit can improve the accuracy of its search by considering the interrelationships of patents. For example, it can prioritize searching for highly relevant patents by considering citation relationships. It can also filter search results by considering the relevance of the patents' technical fields. Furthermore, it can adjust search results by considering the relevance of the patent inventors. This improves the accuracy of the search by considering the interrelationships of patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0068] The revision unit can perform revisions while considering the geographical distribution of patents. For example, it can combine highly relevant technologies and ideas by considering the countries in which patents are filed. It can also filter the revision results by considering the geographical distribution of the patent's technical field. Furthermore, it can adjust the revision results by considering the geographical distribution of the patent's inventors. This improves the accuracy of revision by considering the geographical distribution of patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the geographical distribution data of patents into a generating AI and have the generating AI perform the revision accuracy improvement.

[0069] The proposal unit can evaluate the feasibility of an idea at the time of proposal. For example, it can evaluate the technical feasibility of the proposed idea. It can also evaluate the marketability of the proposed idea. Furthermore, it can evaluate the legal feasibility of the proposed idea. This allows users to select feasible ideas by evaluating their feasibility. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input feasibility data of the proposed idea into a generating AI and have the generating AI perform the feasibility evaluation.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk receives the user's inquiry. For example, the user can input a specific inquiry such as, "I would like patent ideas for a new smartphone feature." The reception desk then inputs the user's inquiry into the generating AI. Step 2: The analysis unit analyzes the consultation content received by the reception unit. For example, the analysis unit uses text analysis technology to analyze the user's consultation content and sets search conditions for using the patent search API. The analysis unit can also use data mining technology to analyze the user's consultation content. Step 3: The search unit searches for existing patents using a patent search API based on the analysis performed by the analysis unit. For example, the search unit performs a patent search using an endpoint of a specific API provider. The search unit can efficiently search for existing patents using a patent search API. Step 4: The refinement unit refines the idea based on the search results obtained by the search unit. For example, the refinement unit creates a new patent idea by combining technologies and ideas described in existing patents. The refinement unit can also perform the process of modifying and improving the idea. Step 5: The proposal team presents the refined ideas to the user. For example, the proposal team provides specific examples of the refined ideas and proposes them to the user. The proposal team can propose ideas based on the format and evaluation criteria of the proposal.

[0072] (Example of form 2) The patent idea generation support system according to an embodiment of the present invention is a system that uses a generative AI to help users efficiently generate new patent ideas. This system provides a mechanism where, when a user consults with the generative AI about a desired patent or a problem they want to solve, the generative AI refines the idea while referring to existing patents, creates a new idea not found in existing patents, and proposes it to the user. For example, a user inputs specific consultation details into the generative AI's chat tool, such as "I want patent ideas about a new smartphone function." This information is input into the generative AI. Next, the generative AI analyzes the input consultation details and searches for existing patents using a patent search API. Based on the search results, the generative AI refers to existing patents related to the user's consultation and refines the idea. For example, it creates a new patent idea by combining technologies and ideas described in existing patents. The generative AI then proposes the refined idea to the user. For example, it proposes a specific idea such as, "As a new smartphone function, we propose a new unlocking method that combines facial recognition technology and voice recognition technology." The user can then proceed with preparing a patent application based on the proposed idea. This mechanism allows users to generate patent ideas very efficiently and create a large number of patent proposals. For example, a company's research and development department can use generative AI to generate many patent ideas in a short period of time, thereby enhancing its competitiveness. Similarly, individual inventors can use generative AI to simplify the patent application process, enabling them to quickly patent new ideas. In this way, patent idea generation support systems can help users efficiently generate new patent ideas.

[0073] The patent idea generation support system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a refinement unit, and a proposal unit. The reception unit receives inquiries from users. For example, a user can input specific inquiries such as "I would like patent ideas for new smartphone features." The reception unit inputs the user's inquiries into the generation AI. The analysis unit analyzes the inquiries received by the reception unit. For example, the analysis unit uses text analysis technology to analyze the user's inquiries and sets search conditions for using a patent search API. The analysis unit can also use data mining technology to analyze the user's inquiries. The search unit searches for existing patents using a patent search API based on the analysis performed by the analysis unit. For example, the search unit performs a patent search using an endpoint of a specific API provider. The search unit can efficiently search for existing patents using a patent search API. The refinement unit refines the ideas based on the search results obtained by the search unit. For example, the refinement unit creates new patent ideas by combining technologies and ideas described in existing patents. The refinement unit can perform the process of modifying and improving ideas. The proposal unit proposes the refined ideas to the user. For example, the proposal unit presents specific examples of the refined ideas to the user. The proposal unit can propose ideas based on the format and evaluation criteria of the proposed content. As a result, the patent idea creation support system according to the embodiment can efficiently analyze the user's consultation content and enable patent searching, idea refinement, and proposal.

[0074] The reception desk receives user inquiries. For example, a user can input a specific inquiry such as, "I would like patent ideas for a new smartphone feature." The reception desk analyzes the user's input using natural language processing technology and converts it into an appropriate format. Specifically, it tokenizes the text entered by the user and extracts important keywords and phrases. This allows for an accurate understanding of the user's intent and requests. Furthermore, the reception desk can refer to the user's past inquiry history and profile information to provide more personalized responses. For example, for users who have made similar inquiries in the past, it can provide suggestions based on their previous inquiries. The reception desk inputs the user's inquiry into a generative AI. The generative AI generates relevant information and ideas based on the user's inquiry. For example, the generative AI researches technologies and market trends related to the patent idea the user is seeking and provides information that is useful to the user. This allows the reception desk to efficiently process user inquiries and provide appropriate information using the generative AI.

[0075] The analysis unit analyzes the content of inquiries received by the reception unit. For example, the analysis unit uses text analysis technology to analyze the user's inquiry and sets search conditions for using the patent search API. Specifically, it uses natural language processing technology to analyze the user's inquiry and extract important keywords and phrases. This allows for an accurate understanding of the user's intentions and requests. The analysis unit can also analyze the user's inquiry using data mining technology. For example, it analyzes past patent databases to identify patents and technologies related to the user's inquiry. Furthermore, the analysis unit can use machine learning algorithms to support the generation of patent ideas based on the user's inquiry. For example, it can use the user's inquiry as input data to train a patent idea generation model and generate new patent ideas. In this way, the analysis unit can analyze the user's inquiry from multiple angles and provide useful information for patent searching and idea generation.

[0076] The search unit searches for existing patents using a patent search API based on the analysis performed by the analysis unit. For example, the search unit performs a patent search using the endpoint of a specific API provider. Specifically, it sends queries to the patent database based on the search conditions set by the analysis unit and searches for relevant patents. The search unit can efficiently search for existing patents using a patent search API. For example, it searches based on information such as the patent title, abstract, claims, and inventor name to identify relevant patents. Furthermore, the search unit can filter the search results and extract the patents most relevant to the user's inquiry. For example, it narrows down the search results by considering the patent publication date, technical field, and patent citation relationships. In this way, the search unit can efficiently perform patent searches based on the user's inquiry and provide relevant patent information.

[0077] The revision unit refines ideas based on search results obtained by the search unit. For example, the revision unit creates new patent ideas by combining technologies and ideas described in existing patents. Specifically, it analyzes patent information obtained from search results and extracts technical features and key points of the invention. This makes it possible to create new ideas based on existing patents. The revision unit can also perform the process of modifying and improving ideas. For example, it can use generative AI to suggest improvements and new perspectives on existing ideas. The generative AI makes suggestions for concretizing and improving ideas based on the user's consultation content and search results. This allows the revision unit to efficiently create new patent ideas based on the user's consultation content and to perform modifications and improvements.

[0078] The proposal unit proposes ideas refined by the revision unit to the user. For example, the proposal unit presents specific examples of the refined ideas to the user. Specifically, it uses a generation AI to generate detailed explanations and examples of the refined ideas and provides them to the user. The proposal unit can propose ideas based on the format and evaluation criteria of the proposal content. For example, it can generate a proposal document in the format of a patent application and provide it to the user. The proposal unit can also collect feedback from the user and improve the proposal content. For example, if the user makes comments or requests revisions to a proposed idea, the proposal unit will reflect this and make a revised proposal. In this way, the proposal unit can propose concrete and practical patent ideas to the user and respond to the user's needs.

[0079] The search unit can search for existing patents using a patent search API. The search unit can perform a patent search using, for example, an endpoint of a specific API provider. The search unit can efficiently search for existing patents using a patent search API. The search unit can extract relevant patents from a patent database using a patent search API. For example, the search unit can search for patents based on specific keywords or filtering conditions using a patent search API. This allows for efficient searching of existing patents using a patent search API. Some or all of the above-described processes in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can input patent data obtained using a patent search API into a generating AI and have the generating AI perform analysis of the patent data.

[0080] The revision unit can create new patent ideas by combining technologies and ideas described in existing patents. For example, the revision unit creates new patent ideas by combining technologies and ideas described in existing patents. The revision unit can refine ideas considering technical compatibility and complementarity of ideas. The revision unit can perform the process of modifying and improving ideas. For example, the revision unit creates new patent ideas by combining technologies and ideas described in existing patents. This makes it possible to efficiently create new patent ideas based on existing patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without using AI. For example, the revision unit can input existing patent data into a generation AI and have the generation AI perform the creation of new patent ideas.

[0081] The proposal unit can propose refined ideas to the user. For example, the proposal unit can show concrete examples of refined ideas and propose them to the user. The proposal unit can propose ideas based on the format and evaluation criteria of the proposal content. The proposal unit can efficiently propose refined ideas to the user. For example, the proposal unit can show concrete examples of refined ideas and propose them to the user. This allows the proposal unit to efficiently propose refined ideas to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input refined ideas into a generation AI and have the generation AI generate the proposal content.

[0082] The analysis unit can analyze the user's consultation content and set search conditions for using the patent search API. For example, the analysis unit can use text analysis technology to analyze the user's consultation content and set search conditions for using the patent search API. The analysis unit can also use data mining technology to analyze the user's consultation content. The analysis unit can set appropriate search conditions based on the user's consultation content. For example, the analysis unit can analyze the user's consultation content and set specific keywords and filtering conditions. This allows for the setting of appropriate search conditions based on the user's consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's consultation content into a generating AI and have the generating AI perform the setting of search conditions.

[0083] The proposal section can provide concrete examples of the proposed idea. For example, the proposal section can show specific implementations of the proposed idea. The proposal section can also show application examples of the proposed idea. The proposal section can also demonstrate the feasibility of the proposed idea. For example, the proposal section can show specific implementations of the proposed idea and propose them to the user. This makes it easier for the user to understand the proposed idea by showing concrete examples. Some or all of the above processing in the proposal section may be performed using AI, for example, or without using AI. For example, the proposal section can input concrete examples of the proposed idea into a generating AI and have the generating AI perform the generation of concrete examples.

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

[0085] The reception desk can analyze a user's past consultation history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the types of consultations the user has frequently had in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest the types of consultations a user might use at a specific time of day based on their past consultation history. This allows the reception desk to provide the most suitable reception method based on past consultation history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0086] The reception desk can filter inquiries based on the user's current projects and areas of interest at the time of reception. For example, the reception desk can prioritize inquiries related to the user's current projects. The reception desk can also automatically filter relevant inquiries based on the user's areas of interest. The reception desk can also suggest appropriate inquiries according to the progress of the user's projects. This ensures that appropriate inquiries are received based on the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0087] The reception desk can estimate the user's emotions and determine the priority of the consultation content to be received based on the estimated emotions. For example, if the user feels it is urgent, the reception desk will prioritize the consultation content. If the user is relaxed, the reception desk can also accept the consultation with the normal priority. If the user is feeling anxious, the reception desk can also raise the priority to respond quickly. This allows for the appropriate determination of the priority of consultation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. The reception desk can also suggest inquiries related to region-specific issues based on the user's current location. If the user is on the move, the reception desk can update and prioritize inquiries related to the user's current location in real time. This allows for the priority of receiving appropriate inquiries based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0089] The reception desk can analyze the user's social media activity and accept relevant inquiries at the time of reception. For example, the reception desk can analyze the user's social media posts and suggest relevant inquiries. The reception desk can also prioritize accepting relevant inquiries by referring to the activities of the user's followers and friends. The reception desk can also analyze the user's social media trends and suggest relevant inquiries. This allows for the acceptance of appropriate inquiries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant inquiries.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. This allows for the provision of appropriate analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during the analysis. For example, the analysis unit can perform a detailed analysis for consultation content of high importance. The analysis unit can also perform a concise analysis for consultation content of low importance. The analysis unit can adjust the depth and scope of the analysis according to the importance. This allows for the provision of appropriate analysis results according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit can apply a technology-specific analysis algorithm to technical consultation content. The analysis unit can also apply a legal-specific analysis algorithm to legal consultation content. The analysis unit can also apply a business-specific analysis algorithm to business-related consultation content. This allows the appropriate analysis algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input consultation content category data into a generating AI and have the generating AI select an analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. This allows for the provision of an analysis result of appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can determine the priority of analysis based on when the consultation content was submitted. For example, the analysis unit may prioritize the analysis of the most recently submitted consultation content. The analysis unit may also postpone the analysis of older consultation content. The analysis unit can adjust the order of analysis according to the submission date. This allows for the provision of an appropriate analysis order according to the submission date of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the consultation content submission date data into a generating AI and have the generating AI determine the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the consultation content during the analysis. For example, the analysis unit may analyze consultation content in order of its relevance. The analysis unit may also postpone analyzing consultation content that is less relevant. The analysis unit can determine the priority of analysis according to the relevance of the consultation content. This allows for the provision of an appropriate analysis order according to the relevance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0096] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is stressed, the search unit can provide simple and highly visible search results. If the user is relaxed, the search unit can also provide detailed search results. If the user is in a hurry, the search unit can also provide concise and to-the-point search results. This allows the search unit to provide appropriate search criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The search unit can improve the accuracy of its search by considering the interrelationships of patents during the search process. For example, the search unit can prioritize searching for highly relevant patents by considering citation relationships. The search unit can also filter search results by considering the relevance of the patents' technical fields. The search unit can also adjust search results by considering the relevance of the patent inventors. This improves the accuracy of the search by considering the interrelationships of patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0098] The search unit can perform searches while considering the attribute information of the patent submitter. For example, the search unit can search for highly relevant patents by considering the patent submitter's area of ​​expertise. The search unit can also adjust the search results by considering the patent submitter's past patent application history. The search unit can also filter the search results by considering the institution to which the patent submitter belongs. This improves the accuracy of the search by considering the attribute information of the patent submitter. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the patent submitter's attribute information data into a generating AI and have the generating AI perform the search accuracy improvement.

[0099] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can provide simple and highly visible search results. If the user is relaxed, the search unit can also provide detailed search results. If the user is in a hurry, the search unit can also provide concise search results that get straight to the point. This allows for the display order of search results to be appropriate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The search unit can perform searches while considering the geographical distribution of patents. For example, the search unit can search for highly relevant patents by considering the country in which the patent was filed. The search unit can also filter search results by considering the geographical distribution of the patent's technical field. The search unit can also adjust search results by considering the geographical distribution of the patent's inventor. This improves the accuracy of the search by considering the geographical distribution of patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the geographical distribution data of patents into a generating AI and have the generating AI perform the task of improving the search accuracy.

[0101] The search unit can improve the accuracy of its search by referring to relevant patent documents during the search process. For example, the search unit can search for highly relevant patents by considering cited patent documents. The search unit can also filter search results by referring to relevant documents in the patent's technical field. The search unit can also refine search results by referring to relevant documents related to the patent's inventor. This improves the accuracy of the search by referring to relevant patent documents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent relevant document data into a generating AI and have the generating AI perform the search accuracy improvement.

[0102] The editing unit can estimate the user's emotions and adjust the editing method based on the estimated emotions. For example, if the user is nervous, the editing unit can provide a simple and easy-to-read editing result. If the user is relaxed, the editing unit can also provide a detailed editing result. If the user is in a hurry, the editing unit can also provide a concise editing result that gets straight to the point. This allows the editing unit to provide an appropriate editing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The revision unit can improve the accuracy of revision by considering the interrelationships of technologies and ideas in existing patents during the revision process. For example, the revision unit adjusts the revision results by considering the technical relevance of existing patents. The revision unit can also generate new ideas by considering combinations of ideas in existing patents. The revision unit can also filter the revision results by considering the citation relationships of existing patents. This improves the accuracy of revision by considering the interrelationships of technologies and ideas in existing patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input existing patent data into a generating AI and have the generating AI perform the revision accuracy improvement.

[0104] The revision unit can perform revisions while considering the attribute information of the patent applicant. For example, the revision unit can combine highly relevant technologies and ideas by considering the patent applicant's field of expertise. The revision unit can also adjust the revision results by considering the patent applicant's past patent application history. The revision unit can also filter the revision results by considering the institution to which the patent applicant belongs. This improves the accuracy of revision by considering the attribute information of the patent applicant. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the patent applicant's attribute information data into a generating AI and have the generating AI perform the revision accuracy improvement.

[0105] The editing unit can estimate the user's emotions and adjust the display method of the editing results based on the estimated user emotions. For example, if the user is nervous, the editing unit can provide a simple and highly visible display method. If the user is relaxed, the editing unit can also provide a display method that includes detailed information. If the user is in a hurry, the editing unit can also provide a display method that gets straight to the point. This allows the editing unit to provide an appropriate display method of the editing results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The revision unit can perform revisions while considering the geographical distribution of patents. For example, the revision unit can combine highly relevant technologies and ideas by considering the countries in which the patents are filed. The revision unit can also filter the revision results by considering the geographical distribution of the patent's technical field. The revision unit can also adjust the revision results by considering the geographical distribution of the patent's inventors. This improves the accuracy of revision by considering the geographical distribution of patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the geographical distribution data of patents into a generating AI and have the generating AI perform the revision accuracy improvement.

[0107] The revision unit can improve the accuracy of the revision by referring to relevant patent documents during the revision process. For example, the revision unit considers cited patent documents and combines highly relevant technologies and ideas. The revision unit can also filter the revision results by referring to relevant documents in the patent's technical field. The revision unit can also adjust the revision results by referring to relevant documents of the patent's inventors. This improves the accuracy of the revision by referring to relevant patent documents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input patent relevant document data into a generating AI and have the generating AI perform the revision accuracy improvement.

[0108] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. This allows the suggestion unit to provide appropriate suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The proposal unit can adjust the level of detail of a proposal based on the importance of the idea. For example, the proposal unit will provide a detailed proposal for a highly important idea. For a less important idea, the proposal unit can provide a concise proposal. The proposal unit can adjust the depth and scope of the proposal according to its importance. This allows it to provide appropriate proposals according to the importance of the idea. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input idea importance data into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0110] The proposal unit can apply different proposal algorithms depending on the category of the idea during the proposal process. For example, the proposal unit can apply a technology-specific proposal algorithm to technical ideas. It can also apply a legal-specific proposal algorithm to legal ideas. It can also apply a business-specific proposal algorithm to business-related ideas. This allows for the application of an appropriate proposal algorithm according to the category of the idea. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input idea category data into a generating AI and have the generating AI select a proposal algorithm.

[0111] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide suggestions with visually stimulating effects. This allows the suggestion unit to provide suggestions of appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The proposal department can determine the priority of proposals based on when the ideas were submitted. For example, the proposal department may prioritize ideas that were submitted most recently. The proposal department may also postpone the submission of older ideas. The proposal department can adjust the order of proposals according to the submission date. This allows for an appropriate order of proposals based on when the ideas were submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input idea submission date data into a generating AI and have the generating AI determine the priority of proposals.

[0113] The proposal unit can adjust the order of proposals based on the relevance of the ideas during the proposal process. For example, the proposal unit may propose ideas in order of their relevance, starting with the most relevant. It can also postpone less relevant ideas. The proposal unit can determine the priority of proposals according to the relevance of the ideas. This allows for the provision of an appropriate order of proposals according to the relevance of the ideas. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input idea relevance data into a generating AI and have the generating AI adjust the order of proposals.

[0114] The proposal unit can provide concrete examples of the idea at the time of proposal. For example, the proposal unit can show specific implementations of the proposed idea. The proposal unit can also show application examples of the proposed idea. The proposal unit can also demonstrate the feasibility of the proposed idea. By providing concrete examples of the idea, it becomes easier for users to understand. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input concrete example data of the proposed idea into a generating AI and have the generating AI perform the generation of concrete examples.

[0115] The proposal unit can evaluate the feasibility of an idea at the time of proposal. For example, the proposal unit can evaluate the technical feasibility of the proposed idea. The proposal unit can also evaluate the marketability of the proposed idea. The proposal unit can also evaluate the legal feasibility of the proposed idea. This allows users to select feasible ideas by evaluating their feasibility. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input feasibility data of the proposed idea into a generating AI and have the generating AI perform the feasibility evaluation.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The reception desk can estimate the user's emotions and adjust the method of receiving the consultation based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided and the input steps can be minimized. If the user is relaxed, detailed input options can be provided and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick input of the consultation details. This allows for the provision of an appropriate reception method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The analysis unit can analyze the user's past consultation history and select the optimal analysis method. For example, it can automatically display as candidates the topics the user has frequently consulted about in the past. It can also prioritize suggesting input methods (voice, text, etc.) the user has used in the past. Furthermore, it can predict and suggest consultation topics that the user will use at specific times based on their past consultation history. This allows the system to provide the optimal analysis method based on past consultation history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past consultation history data into a generating AI and have the generating AI select the optimal analysis method.

[0119] The search unit can filter results based on the user's current projects and areas of interest. For example, it can prioritize searching for consultations related to the user's current projects. It can also automatically filter relevant consultations based on the user's areas of interest. Furthermore, it can suggest appropriate consultations according to the progress of the user's projects. This allows users to find appropriate consultations based on their projects and areas of interest. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0120] The proofreading unit can estimate the user's emotions and adjust the proofreading method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-read proofreading result. If the user is relaxed, it can provide a detailed proofreading result. Furthermore, if the user is in a hurry, it can provide a concise proofreading result that gets straight to the point. This allows the system to provide an appropriate proofreading method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proofreading unit may be performed using AI, for example, or not using AI. For example, the proofreading unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0121] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions that get straight to the point. This allows for the provision of appropriate suggestions according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving inquiries related to that region. It can also suggest inquiries related to region-specific issues based on the user's current location. Furthermore, if the user is on the move, it can update and prioritize inquiries related to their current location in real time. This allows for the priority of receiving appropriate inquiries based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0123] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during the analysis. For example, it can perform a detailed analysis for consultation content of high importance, and a concise analysis for consultation content of low importance. Furthermore, it can adjust the depth and scope of the analysis according to the importance. This allows for the provision of appropriate analysis results according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0124] The search unit can improve the accuracy of its search by considering the interrelationships of patents. For example, it can prioritize searching for highly relevant patents by considering citation relationships. It can also filter search results by considering the relevance of the patents' technical fields. Furthermore, it can adjust search results by considering the relevance of the patent inventors. This improves the accuracy of the search by considering the interrelationships of patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0125] The revision unit can perform revisions while considering the geographical distribution of patents. For example, it can combine highly relevant technologies and ideas by considering the countries in which patents are filed. It can also filter the revision results by considering the geographical distribution of the patent's technical field. Furthermore, it can adjust the revision results by considering the geographical distribution of the patent's inventors. This improves the accuracy of revision by considering the geographical distribution of patents. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the geographical distribution data of patents into a generating AI and have the generating AI perform the revision accuracy improvement.

[0126] The proposal unit can evaluate the feasibility of an idea at the time of proposal. For example, it can evaluate the technical feasibility of the proposed idea. It can also evaluate the marketability of the proposed idea. Furthermore, it can evaluate the legal feasibility of the proposed idea. This allows users to select feasible ideas by evaluating their feasibility. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input feasibility data of the proposed idea into a generating AI and have the generating AI perform the feasibility evaluation.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk receives the user's inquiry. For example, the user can input a specific inquiry such as, "I would like patent ideas for a new smartphone feature." The reception desk then inputs the user's inquiry into the generating AI. Step 2: The analysis unit analyzes the consultation content received by the reception unit. For example, the analysis unit uses text analysis technology to analyze the user's consultation content and sets search conditions for using the patent search API. The analysis unit can also use data mining technology to analyze the user's consultation content. Step 3: The search unit searches for existing patents using a patent search API based on the analysis performed by the analysis unit. For example, the search unit performs a patent search using an endpoint of a specific API provider. The search unit can efficiently search for existing patents using a patent search API. Step 4: The refinement unit refines the idea based on the search results obtained by the search unit. For example, the refinement unit creates a new patent idea by combining technologies and ideas described in existing patents. The refinement unit can also perform the process of modifying and improving the idea. Step 5: The proposal team presents the refined ideas to the user. For example, the proposal team provides specific examples of the refined ideas and proposes them to the user. The proposal team can propose ideas based on the format and evaluation criteria of the proposal.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, refinement unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's inquiry. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiry. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for existing patents using a patent search API. The refinement unit is implemented by the specific processing unit 290 of the data processing unit 12 and refines the idea based on the search results. The proposal unit is implemented by the output device 40 of the smart device 14 and proposes the refined idea to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, refinement unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's inquiry. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiry. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and searches for existing patents using a patent search API. The refinement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and refines the idea based on the search results. The proposal unit is implemented, for example, by the speaker 240 of the smart glasses 214 and proposes the refined idea to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, refinement unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's inquiry. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiry. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for existing patents using a patent search API. The refinement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and refines the idea based on the search results. The proposal unit is implemented by, for example, the display 343 of the headset terminal 314 and proposes the refined idea to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, refinement unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's inquiry. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiry. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for existing patents using a patent search API. The refinement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and refines the idea based on the search results. The proposal unit is implemented by, for example, the speaker 240 of the robot 414 and proposes the refined idea to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A reception desk that receives inquiries from users, An analysis unit analyzes the content of consultations received by the reception unit, A search unit searches for existing patents using a patent search API based on the content analyzed by the aforementioned analysis unit, A refinement unit refines the idea based on the search results obtained by the search unit, The system comprises a proposal unit that proposes the ideas refined by the revision unit to the user. A system characterized by the following features. (Note 2) The aforementioned search unit, Search for existing patents using a patent search API. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proofreading unit, New patent ideas are created by combining technologies and ideas described in existing patents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Propose refined ideas to users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the user's inquiry and set search criteria for using the patent search API. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Here are some specific examples of the proposed ideas. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request, the system prioritizes accepting inquiries that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and receives related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the consultation content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, consider the interrelationships between patents to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When performing a search, the search will take into account the attribute information of the patent filer. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When performing a search, consider the geographical distribution of patents. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, refer to related patent documents to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proofreading unit, It estimates the user's emotions and adjusts the revision method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proofreading unit, During the revision process, consider the interrelationships of existing patented technologies and ideas to improve the accuracy of the revision. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proofreading unit, During the revision process, the attribute information of the patent applicant is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proofreading unit, It estimates the user's emotions and adjusts how the revision results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proofreading unit, When revising, the geographical distribution of the patents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proofreading unit, During the revision process, refer to relevant patent documents to improve the accuracy of the revision. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making proposals, adjust the order of the proposals based on the relevance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, provide specific examples of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When proposing an idea, evaluate its feasibility. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives inquiries from users, An analysis unit analyzes the content of consultations received by the reception unit, A search unit searches for existing patents using a patent search API based on the content analyzed by the aforementioned analysis unit, A refinement unit refines the idea based on the search results obtained by the search unit, The system comprises a proposal unit that proposes the ideas refined by the revision unit to the user. A system characterized by the following features.

2. The aforementioned search unit, Search for existing patents using a patent search API. The system according to feature 1.

3. The aforementioned proofreading unit, New patent ideas are created by combining technologies and ideas described in existing patents. The system according to feature 1.

4. The aforementioned proposal section is, Propose refined ideas to users. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the user's inquiry and set search criteria for using the patent search API. The system according to feature 1.

6. The aforementioned proposal section is, Here are some specific examples of the proposed ideas. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving inquiries based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system according to feature 1.

9. The aforementioned reception unit is During registration, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system according to feature 1.

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