system

Abstract

The system according to this embodiment aims to efficiently generate new business ideas by utilizing publicly available patent information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects patent information. The analysis unit analyzes the patent information collected by the collection unit. The generation unit generates new business ideas based on the results of the analysis by the analysis unit. The evaluation unit evaluates the business ideas generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 difficult to efficiently generate new business ideas by utilizing the disclosed patent information.

[0005] The system according to the embodiment aims to efficiently generate new business ideas by utilizing the disclosed patent information.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects patent information. The analysis unit analyzes the patent information collected by the collection unit. The generation unit generates new business ideas based on the results of the analysis performed by the analysis unit. The evaluation unit evaluates the business ideas generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate new business ideas by utilizing publicly available patent information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business idea generation system according to an embodiment of the present invention is a system that uses AI to analyze publicly available patent information and generates new business ideas that utilize that technology. This business idea generation system enables companies and startups to quickly and efficiently find innovative business ideas. First, the business idea generation system collects publicly available patent information. This patent information includes information obtained from patent documents and patent databases. Next, the business idea generation system uses AI to analyze the collected patent information. The AI ​​automatically classifies the patent information and identifies the characteristics of the technology and the fields in which it can be applied. For example, the business idea generation system extracts information on a specific technology from patent documents and analyzes in what fields that technology can be used. Based on the analysis results, the business idea generation system generates new business ideas. The generated business ideas are ideas for new businesses or services that utilize patented technology. For example, the business idea generation system proposes a new product or service concept based on the patented technology analyzed by the AI. This allows companies and startups to quickly find promising business opportunities. Furthermore, the business idea generation system also automates market research related to the generated business ideas. The AI ​​collects data on the relevant market and evaluates the feasibility and market size of the business idea. This allows companies and startups to quickly obtain the information they need when developing business plans. This business idea generation system is particularly useful for companies and startups aiming to create new businesses. It solves the problem that generating new business ideas is difficult and time-consuming, by using AI to quickly and efficiently analyze patent information and generate business ideas that leverage technology. As a result, companies and startups can more easily find innovative business ideas and develop business plans more smoothly. In short, this business idea generation system enables companies and startups to quickly and efficiently find innovative business ideas.

[0029] The business idea generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects patent information. Patent information includes, but is not limited to, patent documents, patent databases, and patent applications. For example, the collection unit obtains patent documents from a patent database. The collection unit can also obtain patent applications from the Japan Patent Office database. Furthermore, the collection unit can use crawling technology to automatically collect patent information. For example, the collection unit periodically crawls patent databases to collect new patent information. The analysis unit analyzes the patent information collected by the collection unit. For example, the analysis unit analyzes the patent information using text mining technology. Furthermore, the analysis unit can also analyze the patent information using data mining technology. Furthermore, the analysis unit can also analyze the patent information using machine learning algorithms. For example, the analysis unit extracts information on a specific technology from patent documents and analyzes in what fields that technology can be utilized. The generation unit generates new business ideas based on the results analyzed by the analysis unit. The generation unit generates, for example, technical ideas. Furthermore, the generation unit can generate business models. In addition, the generation unit can generate marketing strategies. For example, the generation unit proposes new product or service concepts based on the analysis results. The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit evaluates, for example, the feasibility of the business ideas. The evaluation unit can also evaluate the market size. Furthermore, the evaluation unit can conduct competitive analysis. For example, the evaluation unit collects data on relevant markets and evaluates the feasibility and market size of the business ideas. Thus, the business idea generation system according to the embodiment can analyze patent information, generate new business ideas, and evaluate them.

[0030] The collection unit collects patent information. This patent information includes, but is not limited to, patent documents, patent databases, and patent applications. For example, the collection unit obtains patent documents from patent databases. It can also obtain patent applications from the Japan Patent Office's database. Furthermore, the collection unit can use crawling technology to automatically collect patent information. For example, the collection unit periodically crawls patent databases to collect new patent information. Specifically, the collection unit accesses patent databases and searches for patent documents based on specific keywords or technical fields. It extracts relevant patent documents from the search results and stores these documents in the database. Similarly, for patent applications, it accesses the Japan Patent Office's database and periodically obtains newly published patent applications. When using crawling technology, the collection unit automatically navigates the patent database websites to detect and collect new patent information. The frequency and scope of crawling can be adjusted according to the system settings, enabling rapid collection of the latest patent information. Furthermore, the collection unit simultaneously collects metadata of patent information (e.g., inventor name, filing date, patent number, etc.) and integrates this information into a database. This allows the collection unit to efficiently collect a wide range of patent information, making it available for use by the analysis and generation units.

[0031] The analysis unit analyzes the patent information collected by the collection unit. The analysis unit can analyze patent information using, for example, text mining techniques. It can also analyze patent information using data mining techniques. Furthermore, it can analyze patent information using machine learning algorithms. Specifically, it analyzes the content of patent documents using text mining techniques to extract specific technologies and keywords. For example, it analyzes the text of patent documents using natural language processing techniques to identify technical features and related technical fields. When using data mining techniques, it extracts patterns and trends from patent information to understand technological evolution and market trends. When using machine learning algorithms, it uses patent information as training data to predict the potential of new technologies and business models. For example, it extracts information on specific technologies from patent documents and analyzes in what fields that technology can be utilized. Furthermore, the analysis unit can classify and cluster patent information, grouping related patent documents. This allows the analysis unit to analyze the collected patent information from multiple perspectives and provide the information necessary for generating new business ideas.

[0032] The generation unit generates new business ideas based on the results analyzed by the analysis unit. For example, the generation unit can generate technical ideas, business models, and marketing strategies. Specifically, it proposes new product and service concepts based on the analysis results. For instance, it analyzes how a particular technology can meet market needs and generates new product ideas utilizing that technology. In business model generation, it proposes revenue models and partnership strategies for the commercialization of the technology. In marketing strategy generation, it identifies target markets and develops promotional strategies. The generation unit compiles these ideas into concrete proposals and presentation materials and provides them to the evaluation unit. Furthermore, the generation unit can automate the idea generation process using AI, efficiently generating a diverse range of ideas. For example, it can use generation AI to input prompts based on analysis results and generate new business ideas. The generation AI can learn from past success stories and market data to propose optimal ideas. This allows the generation unit to quickly and effectively generate new business ideas, improving the overall creativity and innovativeness of the system.

[0033] The evaluation unit evaluates the business ideas generated by the generation unit. For example, the evaluation unit assesses the feasibility of the business ideas. The evaluation unit can also assess the market size. Furthermore, the evaluation unit can conduct competitive analysis. Specifically, it evaluates the technical feasibility of the business ideas and estimates the necessary resources and costs. In market size evaluation, it analyzes the size and growth potential of the target market and assesses the commercial potential of the business ideas. In competitive analysis, it collects data on the relevant market and analyzes the trends and market share of competitors. This allows the evaluation unit to comprehensively evaluate the feasibility and market size of business ideas and formulate the optimal business strategy. Furthermore, the evaluation unit can use AI to support the evaluation process. For example, it can use machine learning algorithms to analyze market data and predict the probability of success of business ideas. In addition, the evaluation unit can establish a feedback loop and work with the generation and analysis units to provide feedback on evaluation results and improve or regenerate ideas. This allows the evaluation unit to streamline the business idea evaluation process and improve the accuracy and reliability of the entire system.

[0034] The collection unit can collect patent information from patent documents and patent databases. For example, the collection unit can obtain patent documents from patent databases. The collection unit can also obtain patent applications from the Japan Patent Office database. Furthermore, the collection unit can use crawling technology to automatically collect patent information. For example, the collection unit can periodically crawl patent databases to collect new patent information. In this way, the collection unit can collect patent information from patent documents and patent databases. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can collect patent information using an AI model that crawls patent databases.

[0035] The analysis unit can automatically classify collected patent information and identify the characteristics of the technology and its applicable fields. For example, the analysis unit can automatically classify patent information using machine learning algorithms. It can also classify patent information using clustering methods. Furthermore, the analysis unit can extract technical keywords and identify the characteristics of the technology. For example, the analysis unit can extract information about a specific technology from patent documents and analyze the fields in which that technology can be applied. This allows the analysis unit to automatically classify patent information and identify the characteristics of the technology and its applicable fields. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to classify patent information to identify the characteristics of the technology and its applicable fields.

[0036] The generation unit can generate new business ideas based on the analysis results. For example, the generation unit can generate technical ideas. It can also generate business models. Furthermore, it can generate marketing strategies. For example, the generation unit can propose new product or service concepts based on the analysis results. In this way, the generation unit can generate new business ideas based on the analysis results. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate business ideas using an AI model that takes the analysis results as input and outputs new business ideas.

[0037] The evaluation unit can assess the feasibility and market size of the generated business ideas. For example, the evaluation unit can assess the feasibility of the business ideas. It can also assess the market size. Furthermore, the evaluation unit can conduct competitive analysis. For example, the evaluation unit can collect data on relevant markets and assess the feasibility and market size of the business ideas. This allows the evaluation unit to assess the feasibility and market size of the generated business ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can perform evaluations using an AI model that assesses the feasibility of business ideas.

[0038] The generation unit can provide the generated business ideas in report format. For example, the generation unit can generate text reports. It can also generate graph reports. Furthermore, the generation unit can generate reports in presentation format. For example, the generation unit can provide new product or service concepts in report format based on analysis results. In this way, the generation unit can provide the generated business ideas in report format. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate reports using an AI model that provides business ideas in report format.

[0039] The data collection unit can analyze the user's past search history and select the optimal data collection method when collecting patent information. For example, the data collection unit can analyze trends in the patent information the user has searched for in the past and prioritize the collection of highly relevant information. The data collection unit can also adjust the scope of patent information collection based on the search keywords frequently used by the user. Furthermore, the data collection unit can select a data collection method specialized for a specific technical field based on the user's past search history. For example, the data collection unit can analyze the user's search history and prioritize the collection of highly relevant patent information. This allows the data collection unit to analyze the user's past search history and select the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's search history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The collection unit can filter patent information based on specific technical fields or keywords when collecting it. For example, the collection unit can set keywords related to a specific technical field and filter patent information based on those keywords. The collection unit can also prioritize the collection of highly relevant patent information based on keywords specified by the user. Furthermore, the collection unit can improve the accuracy of the collected patent information by applying filtering algorithms specialized for specific technical fields. For example, the collection unit can set keywords related to a specific technical field and filter patent information based on those keywords. This allows the collection unit to filter patent information based on specific technical fields or keywords. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input keywords related to a specific technical field into a generating AI and have the generating AI perform the filtering.

[0041] The collection unit can prioritize the collection of highly relevant patent information based on the user's geographical location information when collecting patent information. For example, the collection unit can prioritize the collection of patent information related to the user's current location. The collection unit can also collect patent information related to places the user has visited in the past. Furthermore, the collection unit can prioritize the collection of region-specific patent information based on the user's geographical location information. For example, the collection unit can prioritize the collection of highly relevant patent information based on the user's geographical location information. This allows the collection unit to prioritize the collection of highly relevant patent information based on the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant patent information.

[0042] The collection unit can analyze the user's social media activity and collect relevant patent information when collecting patent information. For example, the collection unit can analyze the content of the user's social media posts and collect relevant patent information. The collection unit can also collect highly relevant patent information based on the activity of accounts that the user follows. Furthermore, the collection unit can analyze the user's interests on social media and collect patent information related to specific technological fields. For example, the collection unit can analyze the user's social media activity and collect relevant patent information. In this way, the collection unit can analyze the user's social media activity and collect relevant patent information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant patent information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the patent information during the analysis. For example, the analysis unit performs a detailed analysis for patent information of high importance. The analysis unit can also perform a concise analysis for patent information of low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the patent information. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the patent information. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the patent information. 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 of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the patent information during analysis. For example, the analysis unit can apply a chemical-specific analysis algorithm to patent information in the chemical field. It can also apply an IT-specific analysis algorithm to patent information in the IT field. Furthermore, it can apply a medical-specific analysis algorithm to patent information in the medical field. For example, the analysis unit can apply different analysis algorithms depending on the category of the patent information. This allows the analysis unit to apply different analysis algorithms depending on the category of the patent information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the patent information into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the filing date of the patent information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent patent information. The analysis unit may also postpone the analysis of older patent information. Furthermore, the analysis unit can adjust the order of analysis based on the filing date. For example, the analysis unit can determine the priority of analysis based on the filing date of the patent information. This allows the analysis unit to determine the priority of analysis based on the filing date of the patent information. 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 filing date of the patent information into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the patent information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant patent information. The analysis unit may also postpone the analysis of less relevant patent information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the patent information. For example, the analysis unit adjusts the order of analysis based on the relevance of the patent information. In this way, the analysis unit can adjust the order of analysis based on the relevance of the patent information. 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 of the patent information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail of the business ideas it generates based on the importance of the analysis results during generation. For example, the generation unit can generate detailed business ideas based on analysis results of high importance. It can also generate concise business ideas based on analysis results of low importance. Furthermore, the generation unit can adjust the level of detail of the business ideas it generates according to the importance of the analysis results. For example, the generation unit adjusts the level of detail of the business ideas it generates based on the importance of the analysis results. In this way, the generation unit can adjust the level of detail of the business ideas it generates based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the business ideas.

[0048] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. For example, the generation unit can apply a chemistry-specific generation algorithm based on analysis results in the chemistry field. It can also apply an IT-specific generation algorithm based on analysis results in the IT field. Furthermore, it can apply a medical-specific generation algorithm based on analysis results in the medical field. For example, the generation unit applies different generation algorithms depending on the category of the analysis results. This allows the generation unit to apply different generation algorithms depending on the category of the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis result category into a generation AI and have the generation AI execute the application of the generation algorithm.

[0049] The generation unit can determine the priority of business ideas to generate based on the submission timing of the analysis results. For example, the generation unit can prioritize generating business ideas based on the latest analysis results. The generation unit can also postpone generating business ideas based on older analysis results. Furthermore, the generation unit can adjust the order of the business ideas to be generated based on the submission timing. For example, the generation unit can determine the priority of business ideas to be generated based on the submission timing of the analysis results. This allows the generation unit to determine the priority of business ideas to be generated based on the submission timing of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing of the analysis results into the generation AI and have the generation AI perform the determination of the priority of business ideas.

[0050] The generation unit can adjust the order of business ideas generated based on the relevance of the analysis results during generation. For example, the generation unit can prioritize generating business ideas based on highly relevant analysis results. The generation unit can also postpone generating business ideas based on less relevant analysis results. Furthermore, the generation unit can adjust the order of the business ideas generated based on the relevance of the analysis results. For example, the generation unit adjusts the order of the business ideas generated based on the relevance of the analysis results. This allows the generation unit to adjust the order of business ideas generated based on the relevance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI perform the adjustment of the order of business ideas.

[0051] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the generated business ideas during the evaluation process. For example, the evaluation unit will perform a detailed evaluation for business ideas with high importance. The evaluation unit can also perform a concise evaluation for business ideas with low importance. Furthermore, the evaluation unit can adjust the depth of the evaluation according to the importance of the business ideas. For example, the evaluation unit can adjust the level of detail of the evaluation based on the importance of the generated business ideas. This allows the evaluation unit to adjust the level of detail of the evaluation based on the importance of the generated business ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the importance of the business ideas into the generating AI and have the generating AI perform the adjustment of the level of detail of the evaluation.

[0052] The evaluation unit can apply different evaluation algorithms depending on the category of the generated business idea during the evaluation process. For example, the evaluation unit can apply a chemistry-specific evaluation algorithm to a business idea in the chemical field. It can also apply an IT-specific evaluation algorithm to a business idea in the IT field. Furthermore, it can apply a medical-specific evaluation algorithm to a business idea in the medical field. For example, the evaluation unit can apply different evaluation algorithms depending on the category of the generated business idea. This allows the evaluation unit to apply different evaluation algorithms depending on the category of the generated business idea. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the business idea category into a generating AI and have the generating AI execute the application of the evaluation algorithm.

[0053] The evaluation unit can determine the priority of evaluation based on the submission timing of the generated business ideas during the evaluation process. For example, the evaluation unit may prioritize the evaluation of the most recent business ideas. Alternatively, the evaluation unit may postpone the evaluation of older business ideas. Furthermore, the evaluation unit may adjust the order of evaluation based on the submission timing. For example, the evaluation unit can determine the priority of evaluation based on the submission timing of the generated business ideas. This allows the evaluation unit to determine the priority of evaluation based on the submission timing of the generated business ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the submission timing of the business ideas into a generating AI and have the generating AI perform the determination of the evaluation priority.

[0054] The evaluation unit can adjust the order of evaluation based on the relevance of the generated business ideas during the evaluation process. For example, the evaluation unit may prioritize the evaluation of highly relevant business ideas. It can also postpone the evaluation of less relevant business ideas. Furthermore, the evaluation unit can adjust the order of evaluation based on the relevance of the business ideas. For example, the evaluation unit can adjust the order of evaluation based on the relevance of the generated business ideas. This allows the evaluation unit to adjust the order of evaluation based on the relevance of the generated business ideas. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the relevance of the business ideas into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The business idea generation system can further analyze the user's past search history and select the optimal analysis method. For example, the analysis unit can analyze trends in patent information previously searched by the user and prioritize the analysis of highly relevant information. It can also adjust the scope of analysis based on frequently used search keywords. Furthermore, it can select an analysis method specialized for a specific technical field from the user's past search history. In this way, the analysis unit can analyze the user's past search history and select the optimal analysis method. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's search history data into a generation AI and have the generation AI select the optimal analysis method.

[0057] The business idea generation system can also prioritize the collection of highly relevant patent information based on the user's geographical location. For example, the collection unit can prioritize the collection of patent information related to the user's current location. It can also collect patent information related to places the user has visited in the past. Furthermore, it can prioritize the collection of region-specific patent information based on the user's geographical location. This allows the collection unit to prioritize the collection of highly relevant patent information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generation AI and have the generation AI perform the collection of highly relevant patent information.

[0058] The business idea generation system can further analyze the user's social media activity and collect relevant patent information. For example, the collection unit can analyze the user's social media posts and collect relevant patent information. It can also collect highly relevant patent information based on the activity of accounts the user follows. Furthermore, it can analyze the user's interests on social media and collect patent information related to specific technological fields. In this way, the collection unit can analyze the user's social media activity and collect relevant patent information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI perform the collection of relevant patent information.

[0059] The business idea generation system can further adjust the level of detail of its analysis based on the importance of the patent information. For example, the analysis unit can perform a detailed analysis on highly important patent information and a concise analysis on less important patent information. Furthermore, it can adjust the depth of the analysis according to the importance of the patent information. In this way, the analysis unit can adjust the level of detail of its analysis based on the importance of the patent information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the patent information into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0060] The business idea generation system can further adjust the level of detail in the evaluation of the generated business ideas based on their importance. For example, the evaluation unit can perform a detailed evaluation for business ideas with high importance, and a concise evaluation for business ideas with low importance. Furthermore, it can adjust the depth of the evaluation according to the importance of the business idea. In this way, the evaluation unit can adjust the level of detail in the evaluation based on the importance of the generated business ideas. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input the importance of the business ideas into the generation AI and have the generation AI perform the adjustment of the level of detail in the evaluation.

[0061] The business idea generation system can further apply different evaluation algorithms to the generated business ideas depending on their category. For example, the evaluation unit can apply a chemistry-specific evaluation algorithm to business ideas in the chemical field. It can also apply an IT-specific evaluation algorithm to business ideas in the IT field. Furthermore, it can apply a medical-specific evaluation algorithm to business ideas in the medical field. This allows the evaluation unit to apply different evaluation algorithms depending on the category of the generated business idea. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the category of the business idea into the generation AI and have the generation AI execute the application of the evaluation algorithm.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects patent information. This patent information includes patent documents, patent databases, and patent applications. The collection unit can obtain patent documents from patent databases and patent applications from the Japan Patent Office database. The collection unit can also automatically collect patent information using crawling technology. For example, the collection unit can periodically crawl patent databases to collect new patent information. Step 2: The analysis unit analyzes the patent information collected by the collection unit. The analysis unit analyzes the patent information using text mining techniques, data mining techniques, and machine learning algorithms. For example, the analysis unit extracts information about a specific technology from the patent documents and analyzes in what fields that technology can be used. Step 3: The generation unit generates new business ideas based on the results analyzed by the analysis unit. The generation unit can generate technical ideas, business models, and marketing strategies. For example, the generation unit proposes new product or service concepts based on the analysis results. Step 4: The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit can assess the feasibility, market size, and competitive analysis of the business ideas. For example, the evaluation unit can collect relevant market data to evaluate the feasibility and market size of the business ideas.

[0064] (Example of form 2) The business idea generation system according to an embodiment of the present invention is a system that uses AI to analyze publicly available patent information and generates new business ideas that utilize that technology. This business idea generation system enables companies and startups to quickly and efficiently find innovative business ideas. First, the business idea generation system collects publicly available patent information. This patent information includes information obtained from patent documents and patent databases. Next, the business idea generation system uses AI to analyze the collected patent information. The AI ​​automatically classifies the patent information and identifies the characteristics of the technology and the fields in which it can be applied. For example, the business idea generation system extracts information on a specific technology from patent documents and analyzes in what fields that technology can be used. Based on the analysis results, the business idea generation system generates new business ideas. The generated business ideas are ideas for new businesses or services that utilize patented technology. For example, the business idea generation system proposes a new product or service concept based on the patented technology analyzed by the AI. This allows companies and startups to quickly find promising business opportunities. Furthermore, the business idea generation system also automates market research related to the generated business ideas. The AI ​​collects data on the relevant market and evaluates the feasibility and market size of the business idea. This allows companies and startups to quickly obtain the information they need when developing business plans. This business idea generation system is particularly useful for companies and startups aiming to create new businesses. It solves the problem that generating new business ideas is difficult and time-consuming, by using AI to quickly and efficiently analyze patent information and generate business ideas that leverage technology. As a result, companies and startups can more easily find innovative business ideas and develop business plans more smoothly. In short, this business idea generation system enables companies and startups to quickly and efficiently find innovative business ideas.

[0065] The business idea generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects patent information. Patent information includes, but is not limited to, patent documents, patent databases, and patent applications. For example, the collection unit obtains patent documents from a patent database. The collection unit can also obtain patent applications from the Japan Patent Office database. Furthermore, the collection unit can use crawling technology to automatically collect patent information. For example, the collection unit periodically crawls patent databases to collect new patent information. The analysis unit analyzes the patent information collected by the collection unit. For example, the analysis unit analyzes the patent information using text mining technology. Furthermore, the analysis unit can also analyze the patent information using data mining technology. Furthermore, the analysis unit can also analyze the patent information using machine learning algorithms. For example, the analysis unit extracts information on a specific technology from patent documents and analyzes in what fields that technology can be utilized. The generation unit generates new business ideas based on the results analyzed by the analysis unit. The generation unit generates, for example, technical ideas. Furthermore, the generation unit can generate business models. In addition, the generation unit can generate marketing strategies. For example, the generation unit proposes new product or service concepts based on the analysis results. The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit evaluates, for example, the feasibility of the business ideas. The evaluation unit can also evaluate the market size. Furthermore, the evaluation unit can conduct competitive analysis. For example, the evaluation unit collects data on relevant markets and evaluates the feasibility and market size of the business ideas. Thus, the business idea generation system according to the embodiment can analyze patent information, generate new business ideas, and evaluate them.

[0066] The collection unit collects patent information. This patent information includes, but is not limited to, patent documents, patent databases, and patent applications. For example, the collection unit obtains patent documents from patent databases. It can also obtain patent applications from the Japan Patent Office's database. Furthermore, the collection unit can use crawling technology to automatically collect patent information. For example, the collection unit periodically crawls patent databases to collect new patent information. Specifically, the collection unit accesses patent databases and searches for patent documents based on specific keywords or technical fields. It extracts relevant patent documents from the search results and stores these documents in the database. Similarly, for patent applications, it accesses the Japan Patent Office's database and periodically obtains newly published patent applications. When using crawling technology, the collection unit automatically navigates the patent database websites to detect and collect new patent information. The frequency and scope of crawling can be adjusted according to the system settings, enabling rapid collection of the latest patent information. Furthermore, the collection unit simultaneously collects metadata of patent information (e.g., inventor name, filing date, patent number, etc.) and integrates this information into a database. This allows the collection unit to efficiently collect a wide range of patent information, making it available for use by the analysis and generation units.

[0067] The analysis unit analyzes the patent information collected by the collection unit. The analysis unit can analyze patent information using, for example, text mining techniques. It can also analyze patent information using data mining techniques. Furthermore, it can analyze patent information using machine learning algorithms. Specifically, it analyzes the content of patent documents using text mining techniques to extract specific technologies and keywords. For example, it analyzes the text of patent documents using natural language processing techniques to identify technical features and related technical fields. When using data mining techniques, it extracts patterns and trends from patent information to understand technological evolution and market trends. When using machine learning algorithms, it uses patent information as training data to predict the potential of new technologies and business models. For example, it extracts information on specific technologies from patent documents and analyzes in what fields that technology can be utilized. Furthermore, the analysis unit can classify and cluster patent information, grouping related patent documents. This allows the analysis unit to analyze the collected patent information from multiple perspectives and provide the information necessary for generating new business ideas.

[0068] The generation unit generates new business ideas based on the results analyzed by the analysis unit. For example, the generation unit can generate technical ideas, business models, and marketing strategies. Specifically, it proposes new product and service concepts based on the analysis results. For instance, it analyzes how a particular technology can meet market needs and generates new product ideas utilizing that technology. In business model generation, it proposes revenue models and partnership strategies for the commercialization of the technology. In marketing strategy generation, it identifies target markets and develops promotional strategies. The generation unit compiles these ideas into concrete proposals and presentation materials and provides them to the evaluation unit. Furthermore, the generation unit can automate the idea generation process using AI, efficiently generating a diverse range of ideas. For example, it can use generation AI to input prompts based on analysis results and generate new business ideas. The generation AI can learn from past success stories and market data to propose optimal ideas. This allows the generation unit to quickly and effectively generate new business ideas, improving the overall creativity and innovativeness of the system.

[0069] The evaluation unit evaluates the business ideas generated by the generation unit. For example, the evaluation unit assesses the feasibility of the business ideas. The evaluation unit can also assess the market size. Furthermore, the evaluation unit can conduct competitive analysis. Specifically, it evaluates the technical feasibility of the business ideas and estimates the necessary resources and costs. In market size evaluation, it analyzes the size and growth potential of the target market and assesses the commercial potential of the business ideas. In competitive analysis, it collects data on the relevant market and analyzes the trends and market share of competitors. This allows the evaluation unit to comprehensively evaluate the feasibility and market size of business ideas and formulate the optimal business strategy. Furthermore, the evaluation unit can use AI to support the evaluation process. For example, it can use machine learning algorithms to analyze market data and predict the probability of success of business ideas. In addition, the evaluation unit can establish a feedback loop and work with the generation and analysis units to provide feedback on evaluation results and improve or regenerate ideas. This allows the evaluation unit to streamline the business idea evaluation process and improve the accuracy and reliability of the entire system.

[0070] The collection unit can collect patent information from patent documents and patent databases. For example, the collection unit can obtain patent documents from patent databases. The collection unit can also obtain patent applications from the Japan Patent Office database. Furthermore, the collection unit can use crawling technology to automatically collect patent information. For example, the collection unit can periodically crawl patent databases to collect new patent information. In this way, the collection unit can collect patent information from patent documents and patent databases. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can collect patent information using an AI model that crawls patent databases.

[0071] The analysis unit can automatically classify collected patent information and identify the characteristics of the technology and its applicable fields. For example, the analysis unit can automatically classify patent information using machine learning algorithms. It can also classify patent information using clustering methods. Furthermore, the analysis unit can extract technical keywords and identify the characteristics of the technology. For example, the analysis unit can extract information about a specific technology from patent documents and analyze the fields in which that technology can be applied. This allows the analysis unit to automatically classify patent information and identify the characteristics of the technology and its applicable fields. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to classify patent information to identify the characteristics of the technology and its applicable fields.

[0072] The generation unit can generate new business ideas based on the analysis results. For example, the generation unit can generate technical ideas. It can also generate business models. Furthermore, it can generate marketing strategies. For example, the generation unit can propose new product or service concepts based on the analysis results. In this way, the generation unit can generate new business ideas based on the analysis results. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate business ideas using an AI model that takes the analysis results as input and outputs new business ideas.

[0073] The evaluation unit can assess the feasibility and market size of the generated business ideas. For example, the evaluation unit can assess the feasibility of the business ideas. It can also assess the market size. Furthermore, the evaluation unit can conduct competitive analysis. For example, the evaluation unit can collect data on relevant markets and assess the feasibility and market size of the business ideas. This allows the evaluation unit to assess the feasibility and market size of the generated business ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can perform evaluations using an AI model that assesses the feasibility of business ideas.

[0074] The generation unit can provide the generated business ideas in report format. For example, the generation unit can generate text reports. It can also generate graph reports. Furthermore, the generation unit can generate reports in presentation format. For example, the generation unit can provide new product or service concepts in report format based on analysis results. In this way, the generation unit can provide the generated business ideas in report format. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate reports using an AI model that provides business ideas in report format.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of patent information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can accelerate the collection timing to provide information efficiently. Furthermore, if the user is in a hurry, the data collection unit can immediately collect and quickly provide patent information. For example, the data collection unit can monitor the user's emotions in real time and adjust the collection timing according to changes in emotions. This allows the data collection unit to adjust the timing of patent information collection based on 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can analyze the user's past search history and select the optimal data collection method when collecting patent information. For example, the data collection unit can analyze trends in the patent information the user has searched for in the past and prioritize the collection of highly relevant information. The data collection unit can also adjust the scope of patent information collection based on the search keywords frequently used by the user. Furthermore, the data collection unit can select a data collection method specialized for a specific technical field based on the user's past search history. For example, the data collection unit can analyze the user's search history and prioritize the collection of highly relevant patent information. This allows the data collection unit to analyze the user's past search history and select the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's search history data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The collection unit can filter patent information based on specific technical fields or keywords when collecting it. For example, the collection unit can set keywords related to a specific technical field and filter patent information based on those keywords. The collection unit can also prioritize the collection of highly relevant patent information based on keywords specified by the user. Furthermore, the collection unit can improve the accuracy of the collected patent information by applying filtering algorithms specialized for specific technical fields. For example, the collection unit can set keywords related to a specific technical field and filter patent information based on those keywords. This allows the collection unit to filter patent information based on specific technical fields or keywords. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input keywords related to a specific technical field into a generating AI and have the generating AI perform the filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of patent information to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit will prioritize collecting the latest patent information. If the user is relaxed, the data collection unit can also collect a wide range of patent information, including past patent information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting high-priority patent information. For example, the data collection unit can monitor the user's emotions in real time and determine the priority of patent information to collect according to changes in emotions. This allows the data collection unit to determine the priority of patent information to collect based on 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The collection unit can prioritize the collection of highly relevant patent information based on the user's geographical location information when collecting patent information. For example, the collection unit can prioritize the collection of patent information related to the user's current location. The collection unit can also collect patent information related to places the user has visited in the past. Furthermore, the collection unit can prioritize the collection of region-specific patent information based on the user's geographical location information. For example, the collection unit can prioritize the collection of highly relevant patent information based on the user's geographical location information. This allows the collection unit to prioritize the collection of highly relevant patent information based on the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant patent information.

[0080] The collection unit can analyze the user's social media activity and collect relevant patent information when collecting patent information. For example, the collection unit can analyze the content of the user's social media posts and collect relevant patent information. The collection unit can also collect highly relevant patent information based on the activity of accounts that the user follows. Furthermore, the collection unit can analyze the user's interests on social media and collect patent information related to specific technological fields. For example, the collection unit can analyze the user's social media activity and collect relevant patent information. In this way, the collection unit can analyze the user's social media activity and collect relevant patent information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant patent information.

[0081] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can monitor the user's emotions in real time and adjust the presentation of the analysis according to changes in emotions. This allows the analysis unit to adjust the presentation of the analysis based on 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 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.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the patent information during the analysis. For example, the analysis unit performs a detailed analysis for patent information of high importance. The analysis unit can also perform a concise analysis for patent information of low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the patent information. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the patent information. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the patent information. 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 of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the patent information during analysis. For example, the analysis unit can apply a chemical-specific analysis algorithm to patent information in the chemical field. It can also apply an IT-specific analysis algorithm to patent information in the IT field. Furthermore, it can apply a medical-specific analysis algorithm to patent information in the medical field. For example, the analysis unit can apply different analysis algorithms depending on the category of the patent information. This allows the analysis unit to apply different analysis algorithms depending on the category of the patent information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the patent information into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0084] 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. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. For example, the analysis unit can monitor the user's emotions in real time and adjust the length of the analysis according to changes in emotions. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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.

[0085] The analysis unit can determine the priority of analysis based on the filing date of the patent information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent patent information. The analysis unit may also postpone the analysis of older patent information. Furthermore, the analysis unit can adjust the order of analysis based on the filing date. For example, the analysis unit can determine the priority of analysis based on the filing date of the patent information. This allows the analysis unit to determine the priority of analysis based on the filing date of the patent information. 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 filing date of the patent information into a generating AI and have the generating AI perform the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the patent information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant patent information. The analysis unit may also postpone the analysis of less relevant patent information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the patent information. For example, the analysis unit adjusts the order of analysis based on the relevance of the patent information. In this way, the analysis unit can adjust the order of analysis based on the relevance of the patent information. 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 of the patent information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0087] The generation unit can estimate the user's emotions and adjust the way it presents the generated business ideas based on those emotions. For example, if the user is relaxed, the generation unit can provide a detailed business idea. If the user is in a hurry, it can provide a concise business idea that gets straight to the point. Furthermore, if the user is excited, it can provide a visually appealing business idea. For example, the generation unit can monitor the user's emotions in real time and adjust the way it presents the business ideas in response to changes in those emotions. This allows the generation unit to adjust the way it presents the business ideas it generates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can adjust the level of detail of the business ideas it generates based on the importance of the analysis results during generation. For example, the generation unit can generate detailed business ideas based on analysis results of high importance. It can also generate concise business ideas based on analysis results of low importance. Furthermore, the generation unit can adjust the level of detail of the business ideas it generates according to the importance of the analysis results. For example, the generation unit adjusts the level of detail of the business ideas it generates based on the importance of the analysis results. In this way, the generation unit can adjust the level of detail of the business ideas it generates based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI perform the adjustment of the level of detail of the business ideas.

[0089] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. For example, the generation unit can apply a chemistry-specific generation algorithm based on analysis results in the chemistry field. It can also apply an IT-specific generation algorithm based on analysis results in the IT field. Furthermore, it can apply a medical-specific generation algorithm based on analysis results in the medical field. For example, the generation unit applies different generation algorithms depending on the category of the analysis results. This allows the generation unit to apply different generation algorithms depending on the category of the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis result category into a generation AI and have the generation AI execute the application of the generation algorithm.

[0090] The generation unit can estimate the user's emotions and determine the priority of business ideas to generate based on the estimated user emotions. For example, if the user is excited, the generation unit will prioritize providing the latest business ideas. If the user is relaxed, the generation unit can also provide a wide range of business ideas, including past ideas. Furthermore, if the user is in a hurry, the generation unit can prioritize providing business ideas of high importance. For example, the generation unit can monitor the user's emotions in real time and determine the priority of business ideas to generate in response to changes in emotions. This allows the generation unit to determine the priority of business ideas to generate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0091] The generation unit can determine the priority of business ideas to generate based on the submission timing of the analysis results. For example, the generation unit can prioritize generating business ideas based on the latest analysis results. The generation unit can also postpone generating business ideas based on older analysis results. Furthermore, the generation unit can adjust the order of the business ideas to be generated based on the submission timing. For example, the generation unit can determine the priority of business ideas to be generated based on the submission timing of the analysis results. This allows the generation unit to determine the priority of business ideas to be generated based on the submission timing of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing of the analysis results into the generation AI and have the generation AI perform the determination of the priority of business ideas.

[0092] The generation unit can adjust the order of business ideas generated based on the relevance of the analysis results during generation. For example, the generation unit can prioritize generating business ideas based on highly relevant analysis results. The generation unit can also postpone generating business ideas based on less relevant analysis results. Furthermore, the generation unit can adjust the order of the business ideas generated based on the relevance of the analysis results. For example, the generation unit adjusts the order of the business ideas generated based on the relevance of the analysis results. This allows the generation unit to adjust the order of business ideas generated based on the relevance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI and have the generation AI perform the adjustment of the order of business ideas.

[0093] The evaluation unit can estimate the user's emotions and adjust the way the evaluation is presented based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation result. If the user is in a hurry, the evaluation unit can also provide a concise evaluation result that gets straight to the point. Furthermore, if the user is excited, the evaluation unit can provide a visually appealing evaluation result. For example, the evaluation unit can monitor the user's emotions in real time and adjust the way the evaluation is presented in response to changes in emotions. This allows the evaluation unit to adjust the way the evaluation is presented based on 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 evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the generated business ideas during the evaluation process. For example, the evaluation unit will perform a detailed evaluation for business ideas with high importance. The evaluation unit can also perform a concise evaluation for business ideas with low importance. Furthermore, the evaluation unit can adjust the depth of the evaluation according to the importance of the business ideas. For example, the evaluation unit can adjust the level of detail of the evaluation based on the importance of the generated business ideas. This allows the evaluation unit to adjust the level of detail of the evaluation based on the importance of the generated business ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the importance of the business ideas into the generating AI and have the generating AI perform the adjustment of the level of detail of the evaluation.

[0095] The evaluation unit can apply different evaluation algorithms depending on the category of the generated business idea during the evaluation process. For example, the evaluation unit can apply a chemistry-specific evaluation algorithm to a business idea in the chemical field. It can also apply an IT-specific evaluation algorithm to a business idea in the IT field. Furthermore, it can apply a medical-specific evaluation algorithm to a business idea in the medical field. For example, the evaluation unit can apply different evaluation algorithms depending on the category of the generated business idea. This allows the evaluation unit to apply different evaluation algorithms depending on the category of the generated business idea. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the business idea category into a generating AI and have the generating AI execute the application of the evaluation algorithm.

[0096] The evaluation unit can estimate the user's emotions and determine evaluation priorities based on the estimated user emotions. For example, if the user is excited, the evaluation unit may prioritize evaluating the latest business ideas. If the user is relaxed, the evaluation unit may also evaluate a wide range of ideas, including past business ideas. Furthermore, if the user is in a hurry, the evaluation unit may prioritize evaluating business ideas of high importance. For example, the evaluation unit may monitor the user's emotions in real time and determine evaluation priorities according to changes in emotions. This allows the evaluation unit to determine evaluation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The evaluation unit can determine the priority of evaluation based on the submission timing of the generated business ideas during the evaluation process. For example, the evaluation unit may prioritize the evaluation of the most recent business ideas. Alternatively, the evaluation unit may postpone the evaluation of older business ideas. Furthermore, the evaluation unit may adjust the order of evaluation based on the submission timing. For example, the evaluation unit can determine the priority of evaluation based on the submission timing of the generated business ideas. This allows the evaluation unit to determine the priority of evaluation based on the submission timing of the generated business ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the submission timing of the business ideas into a generating AI and have the generating AI perform the determination of the evaluation priority.

[0098] The evaluation unit can adjust the order of evaluation based on the relevance of the generated business ideas during the evaluation process. For example, the evaluation unit may prioritize the evaluation of highly relevant business ideas. It can also postpone the evaluation of less relevant business ideas. Furthermore, the evaluation unit can adjust the order of evaluation based on the relevance of the business ideas. For example, the evaluation unit can adjust the order of evaluation based on the relevance of the generated business ideas. This allows the evaluation unit to adjust the order of evaluation based on the relevance of the generated business ideas. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the relevance of the business ideas into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The business idea generation system can further estimate the user's emotions and customize the patent information analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise summary of the analysis results. If the user is relaxed, it can also provide a detailed analysis result. Furthermore, if the user is excited, it can provide the analysis results in a visually appealing format. In this way, the analysis unit can customize the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0101] The business idea generation system can further analyze the user's past search history and select the optimal analysis method. For example, the analysis unit can analyze trends in patent information previously searched by the user and prioritize the analysis of highly relevant information. It can also adjust the scope of analysis based on frequently used search keywords. Furthermore, it can select an analysis method specialized for a specific technical field from the user's past search history. In this way, the analysis unit can analyze the user's past search history and select the optimal analysis method. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's search history data into a generation AI and have the generation AI select the optimal analysis method.

[0102] The business idea generation system can also prioritize the collection of highly relevant patent information based on the user's geographical location. For example, the collection unit can prioritize the collection of patent information related to the user's current location. It can also collect patent information related to places the user has visited in the past. Furthermore, it can prioritize the collection of region-specific patent information based on the user's geographical location. This allows the collection unit to prioritize the collection of highly relevant patent information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generation AI and have the generation AI perform the collection of highly relevant patent information.

[0103] The business idea generation system can further analyze the user's social media activity and collect relevant patent information. For example, the collection unit can analyze the user's social media posts and collect relevant patent information. It can also collect highly relevant patent information based on the activity of accounts the user follows. Furthermore, it can analyze the user's interests on social media and collect patent information related to specific technological fields. In this way, the collection unit can analyze the user's social media activity and collect relevant patent information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI perform the collection of relevant patent information.

[0104] The business idea generation system can further adjust the level of detail of its analysis based on the importance of the patent information. For example, the analysis unit can perform a detailed analysis on highly important patent information and a concise analysis on less important patent information. Furthermore, it can adjust the depth of the analysis according to the importance of the patent information. In this way, the analysis unit can adjust the level of detail of its analysis based on the importance of the patent information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the patent information into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0105] The business idea generation system can further estimate the user's emotions and adjust the way the generated business ideas are presented based on those estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed business ideas. If the user is in a hurry, it can provide concise business ideas that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing business ideas. This allows the generation unit to adjust the way the generated business ideas are presented based on the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0106] The business idea generation system can further estimate the user's emotions when evaluating the generated business ideas and adjust the evaluation method based on the estimated emotions. For example, the evaluation unit can provide detailed evaluation results if the user is relaxed. If the user is in a hurry, it can provide concise evaluation results that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing evaluation results. In this way, the evaluation unit can adjust the evaluation method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0107] The business idea generation system can further adjust the level of detail in the evaluation of the generated business ideas based on their importance. For example, the evaluation unit can perform a detailed evaluation for business ideas with high importance, and a concise evaluation for business ideas with low importance. Furthermore, it can adjust the depth of the evaluation according to the importance of the business idea. In this way, the evaluation unit can adjust the level of detail in the evaluation based on the importance of the generated business ideas. Some or all of the above processing in the evaluation unit may be performed using AI, or it may be performed without AI. For example, the evaluation unit can input the importance of the business ideas into the generation AI and have the generation AI perform the adjustment of the level of detail in the evaluation.

[0108] The business idea generation system can further apply different evaluation algorithms to the generated business ideas depending on their category. For example, the evaluation unit can apply a chemistry-specific evaluation algorithm to business ideas in the chemical field. It can also apply an IT-specific evaluation algorithm to business ideas in the IT field. Furthermore, it can apply a medical-specific evaluation algorithm to business ideas in the medical field. This allows the evaluation unit to apply different evaluation algorithms depending on the category of the generated business idea. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the category of the business idea into the generation AI and have the generation AI execute the application of the evaluation algorithm.

[0109] The business idea generation system can also estimate the user's emotions when evaluating the generated business ideas and determine the evaluation priority based on the estimated emotions. For example, if the user is excited, the evaluation unit will prioritize evaluating the latest business ideas. If the user is relaxed, it can also evaluate a wide range of ideas, including past business ideas. Furthermore, if the user is in a hurry, it can also prioritize evaluating business ideas of high importance. In this way, the evaluation unit can determine the evaluation priority based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects patent information. This patent information includes patent documents, patent databases, and patent applications. The collection unit can obtain patent documents from patent databases and patent applications from the Japan Patent Office database. The collection unit can also automatically collect patent information using crawling technology. For example, the collection unit can periodically crawl patent databases to collect new patent information. Step 2: The analysis unit analyzes the patent information collected by the collection unit. The analysis unit analyzes the patent information using text mining techniques, data mining techniques, and machine learning algorithms. For example, the analysis unit extracts information about a specific technology from the patent documents and analyzes in what fields that technology can be used. Step 3: The generation unit generates new business ideas based on the results analyzed by the analysis unit. The generation unit can generate technical ideas, business models, and marketing strategies. For example, the generation unit proposes new product or service concepts based on the analysis results. Step 4: The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit can assess the feasibility, market size, and competitive analysis of the business ideas. For example, the evaluation unit can collect relevant market data to evaluate the feasibility and market size of the business ideas.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects patent information by the control unit 46A of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the patent information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates new business ideas based on the analysis results. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the generated business ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects patent information by the control unit 46A of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the patent information. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates new business ideas based on the analysis results. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and evaluates the generated business ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects patent information using the control unit 46A of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the patent information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates new business ideas based on the analysis results. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the generated business ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[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 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.

[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 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.

[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 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and evaluation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects patent information by the control unit 46A of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the patent information. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates new business ideas based on the analysis results. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and evaluates the generated business ideas. 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] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A collection unit that collects patent information, An analysis unit analyzes the patent information collected by the aforementioned collection unit, A generation unit that generates new business ideas based on the results of the analysis performed by the aforementioned analysis unit, The system includes an evaluation unit that evaluates the business ideas generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect patent information from patent documents and patent databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected patent information is automatically categorized to identify the characteristics of the technology and its applicable fields. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate new business ideas based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Evaluate the feasibility and market size of the generated business ideas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The generated business ideas are provided in report format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of patent information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting patent information, the system analyzes the user's past search history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting patent information, filtering is performed based on specific technical fields or keywords. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of patent information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting patent information, the system prioritizes collecting highly relevant patent information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting patent information, we analyze users' social media activity to gather relevant patent information. 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 of the analysis is adjusted based on the importance of the patent information. 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 patent information. 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 is determined based on the filing date of the patent information. 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 patent information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user emotions and adjust the way business ideas are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail of the generated business ideas is adjusted based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is We estimate user emotions and prioritize business ideas generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of business ideas to be generated is determined based on the timing of the submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of the business ideas generated is adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, It estimates the user's emotions and adjusts the way evaluations are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, During the evaluation, the level of detail of the evaluation is adjusted based on the importance of the generated business ideas. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of the generated business idea. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, During the evaluation process, the priority of the evaluation will be determined based on when the generated business ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit, During the evaluation process, the order of evaluation will be adjusted based on the relevance of the generated business ideas. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects patent information, An analysis unit analyzes the patent information collected by the aforementioned collection unit, A generation unit that generates new business ideas based on the results of the analysis performed by the aforementioned analysis unit, The system includes an evaluation unit that evaluates the business ideas generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect patent information from patent documents and patent databases. The system according to feature 1.

3. The aforementioned analysis unit, The collected patent information is automatically categorized to identify the characteristics of the technology and its applicable fields. The system according to feature 1.

4. The generating unit is Generate new business ideas based on analysis results. The system according to feature 1.

5. The evaluation unit, Evaluate the feasibility and market size of the generated business ideas. The system according to feature 1.

6. The generating unit is The generated business ideas are provided in report format. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of patent information collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is When collecting patent information, the system analyzes the user's past search history to select the most suitable collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting patent information, filtering is performed based on specific technical fields or keywords. The system according to feature 1.

10. The aforementioned collection unit is It estimates user sentiment and determines the priority of patent information to collect based on the estimated user sentiment. The system according to feature 1.

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