system

Abstract

The system according to this embodiment aims to analyze patent information, generate new business ideas, and improve and expand them in cooperation with other users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects information from a patent database. The analysis unit analyzes the information collected by the collection unit. The generation unit generates business ideas based on the analysis results obtained by the analysis unit. The sharing unit shares the ideas generated by the generation unit and improves and expands them in cooperation with other users.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that patent information has not been fully utilized effectively to generate new business ideas and cooperate with other users for improvement and expansion.

[0005] The system according to the embodiment aims to analyze patent information, generate new business ideas, and cooperate with other users for improvement and expansion.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects information from a patent database. The analysis unit analyzes the information collected by the collection unit. The generation unit generates business ideas based on the analysis results obtained by the analysis unit. The sharing unit shares the ideas generated by the generation unit and improves and expands them in cooperation with other users. [Effects of the Invention]

[0007] The system according to this embodiment can analyze patent information, generate new business ideas, and improve and expand upon them in cooperation with other users. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A patent navigation agent system according to an embodiment of the present invention is a system that analyzes industry-specific patent information and generates customizable business ideas for the user. The patent navigation agent system collects information from a patent database, and AI analyzes that information to generate customizable ideas based on user input. The generated ideas are shared within the community and improved and expanded in collaboration with other users. For example, the patent navigation agent system collects information from a patent database. For example, the patent navigation agent system can collect information from a patent database using crawling technology. Alternatively, the patent navigation agent system can obtain information from a patent database using an API. Next, the patent navigation agent system analyzes the collected information. For example, the patent navigation agent system can analyze patent information using text mining technology. Alternatively, the patent navigation agent system can analyze patent information using data mining technology. Next, the patent navigation agent system generates business ideas based on the analysis results. For example, the patent navigation agent system sets evaluation criteria for ideas based on the analysis results and generates business ideas using a generation algorithm. Next, the patent navigation agent system shares the generated ideas within the community. For example, the patent navigation agent system can share ideas through an online forum. Alternatively, the patent navigation agent system can also share ideas using collaboration tools. Next, the Patent Navi Agent System improves and expands on ideas generated in collaboration with other users. For example, the Patent Navi Agent System can incorporate feedback and expand on ideas through the improvement process. As a result, the Patent Navi Agent System is expected to improve the quality of business ideas through the utilization of patent information, accelerate innovation through collaboration within the community, and increase the rate of new business creation. In this way, the Patent Navi Agent System can help users find new business opportunities.

[0029] The patent navigation agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects information from a patent database. The collection unit can, for example, collect information from a patent database using crawling technology. The collection unit can also obtain information from a patent database using an API. The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze patent information using text mining technology. The analysis unit can also analyze patent information using data mining technology. The generation unit generates business ideas based on the analysis results obtained by the analysis unit. The generation unit can, for example, set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. The generation unit can also generate customizable ideas based on user input. The sharing unit shares the ideas generated by the generation unit and improves and expands them in cooperation with other users. The sharing unit can, for example, share ideas through an online forum. The sharing unit can also share ideas using collaboration tools. Thus, the patent navigation agent system according to this embodiment can analyze patent information, generate customizable business ideas for users, and share them.

[0030] The data collection unit collects information from a patent database. For example, the data collection unit can collect information from a patent database using crawling technology. Specifically, by using crawling technology, it is possible to automatically collect vast amounts of information within a patent database and efficiently obtain the necessary data. Crawling technology analyzes the structure of web pages and extracts information based on specific keywords and metadata. The data collection unit can also obtain information from a patent database using an API. By using an API, it is possible to obtain the latest patent information in real time through an interface with the patent database. The API provides a protocol for sending specific queries and obtaining the necessary data. This allows the data collection unit to efficiently and accurately collect information from the patent database and provide it to the analysis unit. Furthermore, the data collection unit can centrally manage the collected data and perform data cleansing processing to prevent data duplication and loss. This allows the data collection unit to provide reliable data and improve the accuracy and efficiency of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze patent information using text mining technology. Text mining technology is a technique for extracting useful information from the text data of patent documents and discovering patterns and trends. Specifically, it analyzes the content of patent documents using natural language processing technology and extracts important keywords and phrases. The analysis unit can also analyze patent information using data mining technology. Data mining technology is a technique for discovering useful knowledge from large amounts of data, and can extract highly relevant data from patent information and find patterns and correlations. For example, it can analyze patent application trends in a specific technology field and the patent strategies of competitors. Furthermore, the analysis unit can classify patent information using machine learning algorithms and perform detailed analysis based on data classified into specific categories. In this way, the analysis unit can analyze the collected patent information from multiple perspectives and provide useful information to users.

[0032] The generation unit generates business ideas based on the analysis results obtained by the analysis unit. For example, the generation unit can set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. Specifically, it evaluates the technical advantages and market needs based on patent information obtained from the analysis results and generates new business ideas based on that. The generation algorithm generates ideas based on specific evaluation criteria and proposes them to the user. The generation unit can also generate customizable ideas based on user input. Users input specific conditions and parameters according to their business needs and goals, and the generation algorithm generates customized business ideas based on that. This enables the generation unit to achieve flexible idea generation that meets user needs and provides the user with the optimal business ideas. Furthermore, the generation unit can collect evaluations and feedback on the generated ideas and continuously improve the accuracy and effectiveness of the algorithm. This allows the generation unit to always provide high-quality business ideas based on the latest information and technology, supporting the user's business success.

[0033] The sharing unit shares ideas generated by the generation unit and collaborates with other users to improve and expand them. For example, the sharing unit can share ideas through online forums. These forums are platforms for users to exchange opinions and provide feedback on generated ideas, allowing users to collaborate to improve and expand them. The sharing unit can also share ideas using collaboration tools. These tools enable real-time collaboration, allowing users to simultaneously edit and comment on ideas. This allows the sharing unit to promote collaboration among users and improve the quality of ideas. Furthermore, the sharing unit can track the progress and results of generated ideas and provide feedback to users. This allows the sharing unit to support users in realizing their generated ideas and increase their feasibility. Based on user feedback, the sharing unit can improve the generation algorithms and analysis methods, enhancing the overall system performance. This allows the sharing unit to provide valuable information and support to users, maximizing the effectiveness of the patent navigation agent system.

[0034] The collection unit can collect information from a patent database. For example, the collection unit can collect information from a patent database using crawling technology. The collection unit can also obtain information from a patent database using an API. This allows for the utilization of patent information by collecting information from a patent database. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, when collecting information from a patent database, the collection unit may use AI to select the patent information to be collected.

[0035] The analysis unit can analyze the collected information and generate customizable ideas based on user input. For example, the analysis unit can analyze patent information using text mining techniques. Alternatively, the analysis unit can analyze patent information using data mining techniques. This allows for the generation of customizable ideas based on user input, thereby providing ideas tailored to user needs. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected information into an AI, which can then output the analysis results.

[0036] The generation unit can generate business ideas based on the analysis results. For example, the generation unit can set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. The generation unit can also generate customizable ideas based on user input. This makes it possible to provide new business ideas that utilize patent information by generating business ideas based on 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 results into AI, and the AI ​​can generate business ideas.

[0037] The sharing section allows users to share generated ideas within the community and collaborate with other users to improve and expand them. For example, ideas can be shared through online forums. Alternatively, ideas can be shared using collaboration tools. This allows for the acceleration of innovation by sharing generated ideas within the community and collaborating with other users to improve and expand them. Some or all of the processes described above in the sharing section may be performed using AI, or not. For example, the sharing section could input generated ideas into an AI, which could then suggest ways to share the ideas.

[0038] The data collection unit can analyze the user's past search history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant information based on patent information the user has previously searched for. The data collection unit can also focus on collecting information related to specific fields from the user's search history. Furthermore, the data collection unit can analyze the user's search patterns and determine the optimal data collection timing. This allows the optimal data collection method to be selected by analyzing the user's past search history. 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 AI, which can then select the optimal data collection method.

[0039] The collection unit can filter patent information based on the user's current projects and areas of interest when collecting it. For example, the collection unit can prioritize collecting patent information related to the user's current projects. The collection unit can also filter highly relevant patent information based on the user's areas of interest. Furthermore, the collection unit can provide necessary patent information in a timely manner according to the user's project progress. This allows the collection unit to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's project information into AI, which can then perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting patent information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of patent information related to that region. The data collection unit can also provide region-specific patent information based on the user's location. Furthermore, if the user is on the move, the data collection unit can provide patent information corresponding to their current location in real time. This allows for the provision of highly relevant information by considering the user's geographical location. 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 location information into the AI, which can then select highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information when collecting patent information. For example, the data collection unit can prioritize collecting patent information that the user has shown interest in on social media. The data collection unit can also extract relevant patent information from the user's social media activity. Furthermore, the data collection unit can collect patent information that the user's followers and friends are interested in. This allows the system to provide relevant information by analyzing the user's social media activity. 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 social media data into AI, which can then extract relevant information.

[0042] 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 can perform a detailed analysis of highly important patent information. Conversely, it can perform a simplified analysis of less important patent information. The analysis unit can also determine the priority of the analysis according to the importance of the patent information. This allows for the provision of more important information by adjusting 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 AI. For example, the analysis unit can input patent information importance data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] 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 specialized analysis algorithm to patent information in the medical field. It can also apply a technical analysis algorithm to patent information in the IT field. Furthermore, it can apply an analysis algorithm that considers environmental impact to patent information in the environmental field. By applying different analysis algorithms depending on the category of the patent information, more appropriate analysis results can be provided. 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 patent information category data into the AI, which can then apply an appropriate analysis algorithm.

[0044] 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 will prioritize the analysis of the most recent patent information. The analysis unit can also analyze older patent information as needed. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the filing date. This allows the system to provide the latest information by determining 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 patent information filing date data into the AI, and the AI ​​can determine the priority of analysis.

[0045] 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 prioritizes the analysis of highly relevant patent information. It can also postpone the analysis of less relevant patent information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the patent information. This allows for the provision of more relevant information by adjusting 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 patent information relevance data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the generated ideas based on the importance of the analysis results during generation. For example, the generation unit can generate detailed ideas based on high-importance analysis results. It can also generate concise ideas based on low-importance analysis results. Furthermore, the generation unit can dynamically adjust the level of detail of the ideas according to the importance of the analysis results. This allows for the provision of more important ideas by adjusting the level of detail of the generated ideas 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 importance data of the analysis results into the AI, which can then adjust the level of detail of the ideas.

[0047] 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 specialized generation algorithm to analysis results in the medical field. It can also apply a technical generation algorithm to analysis results in the IT field. Furthermore, it can apply a generation algorithm that considers environmental impact to analysis results in the environmental field. By applying different generation algorithms depending on the category of the analysis results, more appropriate ideas can be provided. 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 category data of the analysis results into the AI, which can then apply an appropriate generation algorithm.

[0048] The generation unit can determine the priority of ideas to generate based on the submission timing of the analysis results. For example, the generation unit prioritizes generating ideas based on the latest analysis results. The generation unit can also generate ideas for older analysis results as needed. Furthermore, the generation unit can dynamically adjust the idea generation priority based on the submission timing. This allows the generation unit to provide the latest ideas by determining the priority of ideas to generate 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 analysis result submission timing data into the AI, which can then determine the idea priority.

[0049] The generation unit can adjust the order in which ideas are generated based on the relevance of the analysis results during generation. For example, the generation unit prioritizes generating ideas based on highly relevant analysis results. The generation unit can also postpone generating ideas based on less relevant analysis results. Furthermore, the generation unit can dynamically adjust the order in which ideas are generated according to the relevance of the analysis results. This allows for the provision of more relevant ideas by adjusting the order in which ideas are 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 data of the analysis results into the AI, which can then adjust the order of the ideas.

[0050] The sharing unit can select the optimal sharing method by referring to the user's past feedback history when sharing. For example, the sharing unit can prioritize providing sharing methods that the user has previously given high ratings to. The sharing unit can also suggest the optimal sharing method based on the user's feedback history. Furthermore, the sharing unit can analyze the user's past feedback and dynamically adjust the sharing method. This allows it to provide the optimal sharing method by referring to the user's past feedback history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input user feedback history data into AI, which can then select the optimal sharing method.

[0051] The sharing function can filter ideas based on the user's current projects and areas of interest during the sharing process. For example, the sharing function prioritizes sharing ideas related to the user's current projects. It can also filter highly relevant ideas based on the user's areas of interest. Furthermore, the sharing function can provide necessary ideas in a timely manner according to the user's project progress. This allows for the provision of highly relevant ideas by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input the user's project information into an AI, which can then perform the filtering.

[0052] The sharing function can prioritize sharing highly relevant ideas by considering the user's geographical location during the sharing process. For example, if a user is in a specific region, the sharing function will prioritize sharing ideas related to that region. Furthermore, the sharing function can provide region-specific ideas based on the user's location. Additionally, if a user is on the move, the sharing function can provide ideas relevant to their current location in real time. This allows for the provision of highly relevant ideas by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI, or without AI. For example, the sharing function can input the user's location information into an AI, which can then select highly relevant ideas.

[0053] The sharing function can analyze the user's social media activity and share relevant ideas during the sharing process. For example, the sharing function prioritizes sharing ideas that the user has shown interest in on social media. It can also extract relevant ideas from the user's social media activity. Furthermore, the sharing function can share ideas that the user's followers and friends are interested in. In this way, it can provide relevant ideas by analyzing the user's social media activity. Some or all of the above processing in the sharing function may be performed using AI, for example, or not. For example, the sharing function can input the user's social media data into an AI, which can then extract relevant ideas.

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

[0055] The data collection unit can analyze the user's past search history and select the optimal collection method when collecting information from the patent database. For example, it can prioritize collecting relevant information based on patent information the user has previously searched for. Furthermore, the data collection unit can focus on collecting information in specific fields based on the user's search history. In addition, the data collection unit can analyze the user's search patterns and determine the optimal collection timing. This allows the system to select the most suitable collection method by analyzing the user's past search history.

[0056] The analysis unit can apply different analysis algorithms depending on the category of patent information when analyzing the collected information. For example, a specialized analysis algorithm can be applied to patent information in the medical field. A technical analysis algorithm can be applied to patent information in the IT field. Furthermore, an analysis algorithm that takes environmental impacts into account can be applied to patent information in the environmental field. By applying different analysis algorithms according to the category of patent information, more appropriate analysis results can be provided.

[0057] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting patent information. For example, if the user is in a specific region, it will prioritize the collection of patent information related to that region. Furthermore, the data collection unit can provide region-specific patent information based on the user's location. In addition, if the user is on the move, it can provide patent information relevant to their current location in real time. This allows for the provision of highly relevant information by considering the user's geographical location.

[0058] The analysis unit can determine the priority of analysis based on the filing date of the patent information during the analysis process. For example, it can prioritize the analysis of the most recent patent information. Older patent information can be analyzed as needed. Furthermore, the analysis priority can be dynamically adjusted based on the filing date. This allows for the provision of the latest information by prioritizing analysis based on the filing date of the patent information.

[0059] The generation unit can adjust the level of detail of the generated ideas based on the importance of the analysis results during generation. For example, it can generate detailed ideas based on high-importance analysis results, and concise ideas based on low-importance analysis results. Furthermore, it can dynamically adjust the level of detail of the ideas according to the importance of the analysis results. This allows for the provision of more important ideas by adjusting the level of detail of the generated ideas based on the importance of the analysis results.

[0060] The sharing function can analyze a user's social media activity and share relevant ideas during the sharing process. For example, it can prioritize sharing ideas that the user has shown interest in on social media. The sharing function can also extract relevant ideas from the user's social media activity. Furthermore, the sharing function can share ideas that the user's followers and friends are interested in. This allows the system to provide relevant ideas by analyzing the user's social media activity.

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

[0062] Step 1: The collection unit collects information from the patent database. The collection unit can, for example, collect information from the patent database using crawling technology. Alternatively, the collection unit can retrieve information from the patent database using an API. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze patent information using, for example, text mining techniques. Alternatively, the analysis unit can analyze patent information using data mining techniques. Step 3: The generation unit generates business ideas based on the analysis results obtained by the analysis unit. For example, the generation unit can set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. The generation unit can also generate customizable ideas based on user input. Step 4: The sharing team shares the ideas generated by the generation team and collaborates with other users to improve and expand them. The sharing team can share ideas, for example, through online forums. They can also share ideas using collaboration tools.

[0063] (Example of form 2) A patent navigation agent system according to an embodiment of the present invention is a system that analyzes industry-specific patent information and generates customizable business ideas for the user. The patent navigation agent system collects information from a patent database, and AI analyzes that information to generate customizable ideas based on user input. The generated ideas are shared within the community and improved and expanded in collaboration with other users. For example, the patent navigation agent system collects information from a patent database. For example, the patent navigation agent system can collect information from a patent database using crawling technology. Alternatively, the patent navigation agent system can obtain information from a patent database using an API. Next, the patent navigation agent system analyzes the collected information. For example, the patent navigation agent system can analyze patent information using text mining technology. Alternatively, the patent navigation agent system can analyze patent information using data mining technology. Next, the patent navigation agent system generates business ideas based on the analysis results. For example, the patent navigation agent system sets evaluation criteria for ideas based on the analysis results and generates business ideas using a generation algorithm. Next, the patent navigation agent system shares the generated ideas within the community. For example, the patent navigation agent system can share ideas through an online forum. Alternatively, the patent navigation agent system can also share ideas using collaboration tools. Next, the Patent Navi Agent System improves and expands on ideas generated in collaboration with other users. For example, the Patent Navi Agent System can incorporate feedback and expand on ideas through the improvement process. As a result, the Patent Navi Agent System is expected to improve the quality of business ideas through the utilization of patent information, accelerate innovation through collaboration within the community, and increase the rate of new business creation. In this way, the Patent Navi Agent System can help users find new business opportunities.

[0064] The patent navigation agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a sharing unit. The collection unit collects information from a patent database. The collection unit can, for example, collect information from a patent database using crawling technology. The collection unit can also obtain information from a patent database using an API. The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze patent information using text mining technology. The analysis unit can also analyze patent information using data mining technology. The generation unit generates business ideas based on the analysis results obtained by the analysis unit. The generation unit can, for example, set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. The generation unit can also generate customizable ideas based on user input. The sharing unit shares the ideas generated by the generation unit and improves and expands them in cooperation with other users. The sharing unit can, for example, share ideas through an online forum. The sharing unit can also share ideas using collaboration tools. Thus, the patent navigation agent system according to this embodiment can analyze patent information, generate customizable business ideas for users, and share them.

[0065] The data collection unit collects information from a patent database. For example, the data collection unit can collect information from a patent database using crawling technology. Specifically, by using crawling technology, it is possible to automatically collect vast amounts of information within a patent database and efficiently obtain the necessary data. Crawling technology analyzes the structure of web pages and extracts information based on specific keywords and metadata. The data collection unit can also obtain information from a patent database using an API. By using an API, it is possible to obtain the latest patent information in real time through an interface with the patent database. The API provides a protocol for sending specific queries and obtaining the necessary data. This allows the data collection unit to efficiently and accurately collect information from the patent database and provide it to the analysis unit. Furthermore, the data collection unit can centrally manage the collected data and perform data cleansing processing to prevent data duplication and loss. This allows the data collection unit to provide reliable data and improve the accuracy and efficiency of the entire system.

[0066] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze patent information using text mining technology. Text mining technology is a technique for extracting useful information from the text data of patent documents and discovering patterns and trends. Specifically, it analyzes the content of patent documents using natural language processing technology and extracts important keywords and phrases. The analysis unit can also analyze patent information using data mining technology. Data mining technology is a technique for discovering useful knowledge from large amounts of data, and can extract highly relevant data from patent information and find patterns and correlations. For example, it can analyze patent application trends in a specific technology field and the patent strategies of competitors. Furthermore, the analysis unit can classify patent information using machine learning algorithms and perform detailed analysis based on data classified into specific categories. In this way, the analysis unit can analyze the collected patent information from multiple perspectives and provide useful information to users.

[0067] The generation unit generates business ideas based on the analysis results obtained by the analysis unit. For example, the generation unit can set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. Specifically, it evaluates the technical advantages and market needs based on patent information obtained from the analysis results and generates new business ideas based on that. The generation algorithm generates ideas based on specific evaluation criteria and proposes them to the user. The generation unit can also generate customizable ideas based on user input. Users input specific conditions and parameters according to their business needs and goals, and the generation algorithm generates customized business ideas based on that. This enables the generation unit to achieve flexible idea generation that meets user needs and provides the user with the optimal business ideas. Furthermore, the generation unit can collect evaluations and feedback on the generated ideas and continuously improve the accuracy and effectiveness of the algorithm. This allows the generation unit to always provide high-quality business ideas based on the latest information and technology, supporting the user's business success.

[0068] The sharing unit shares ideas generated by the generation unit and collaborates with other users to improve and expand them. For example, the sharing unit can share ideas through online forums. These forums are platforms for users to exchange opinions and provide feedback on generated ideas, allowing users to collaborate to improve and expand them. The sharing unit can also share ideas using collaboration tools. These tools enable real-time collaboration, allowing users to simultaneously edit and comment on ideas. This allows the sharing unit to promote collaboration among users and improve the quality of ideas. Furthermore, the sharing unit can track the progress and results of generated ideas and provide feedback to users. This allows the sharing unit to support users in realizing their generated ideas and increase their feasibility. Based on user feedback, the sharing unit can improve the generation algorithms and analysis methods, enhancing the overall system performance. This allows the sharing unit to provide valuable information and support to users, maximizing the effectiveness of the patent navigation agent system.

[0069] The collection unit can collect information from a patent database. For example, the collection unit can collect information from a patent database using crawling technology. The collection unit can also obtain information from a patent database using an API. This allows for the utilization of patent information by collecting information from a patent database. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, when collecting information from a patent database, the collection unit may use AI to select the patent information to be collected.

[0070] The analysis unit can analyze the collected information and generate customizable ideas based on user input. For example, the analysis unit can analyze patent information using text mining techniques. Alternatively, the analysis unit can analyze patent information using data mining techniques. This allows for the generation of customizable ideas based on user input, thereby providing ideas tailored to user needs. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected information into an AI, which can then output the analysis results.

[0071] The generation unit can generate business ideas based on the analysis results. For example, the generation unit can set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. The generation unit can also generate customizable ideas based on user input. This makes it possible to provide new business ideas that utilize patent information by generating business ideas based on 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 results into AI, and the AI ​​can generate business ideas.

[0072] The sharing section allows users to share generated ideas within the community and collaborate with other users to improve and expand them. For example, ideas can be shared through online forums. Alternatively, ideas can be shared using collaboration tools. This allows for the acceleration of innovation by sharing generated ideas within the community and collaborating with other users to improve and expand them. Some or all of the processes described above in the sharing section may be performed using AI, or not. For example, the sharing section could input generated ideas into an AI, which could then suggest ways to share the ideas.

[0073] 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 provide information in a relaxed state. If the user is excited, the data collection unit can collect and provide information quickly. If the user is focused, the data collection unit can optimize the collection timing to provide information efficiently. By adjusting the timing of patent information collection according to the user's emotions, information can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 an AI, which can then adjust the collection timing.

[0074] The data collection unit can analyze the user's past search history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant information based on patent information the user has previously searched for. The data collection unit can also focus on collecting information related to specific fields from the user's search history. Furthermore, the data collection unit can analyze the user's search patterns and determine the optimal data collection timing. This allows the optimal data collection method to be selected by analyzing the user's past search history. 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 AI, which can then select the optimal data collection method.

[0075] The collection unit can filter patent information based on the user's current projects and areas of interest when collecting it. For example, the collection unit can prioritize collecting patent information related to the user's current projects. The collection unit can also filter highly relevant patent information based on the user's areas of interest. Furthermore, the collection unit can provide necessary patent information in a timely manner according to the user's project progress. This allows the collection unit to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's project information into AI, which can then perform the filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of patent information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone collecting less important information. If the user is relaxed, the data collection unit can prioritize providing detailed information. If the user is in a hurry, the data collection unit can quickly provide the most important information. This allows for the provision of more appropriate information by prioritizing the patent information to be collected according to the user's emotions. 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 AI, which can then determine the priority of patent information.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting patent information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of patent information related to that region. The data collection unit can also provide region-specific patent information based on the user's location. Furthermore, if the user is on the move, the data collection unit can provide patent information corresponding to their current location in real time. This allows for the provision of highly relevant information by considering the user's geographical location. 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 location information into the AI, which can then select highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information when collecting patent information. For example, the data collection unit can prioritize collecting patent information that the user has shown interest in on social media. The data collection unit can also extract relevant patent information from the user's social media activity. Furthermore, the data collection unit can collect patent information that the user's followers and friends are interested in. This allows the system to provide relevant information by analyzing the user's social media activity. 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 social media data into AI, which can then extract relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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, or not using AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0080] 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 can perform a detailed analysis of highly important patent information. Conversely, it can perform a simplified analysis of less important patent information. The analysis unit can also determine the priority of the analysis according to the importance of the patent information. This allows for the provision of more important information by adjusting 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 AI. For example, the analysis unit can input patent information importance data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0081] 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 specialized analysis algorithm to patent information in the medical field. It can also apply a technical analysis algorithm to patent information in the IT field. Furthermore, it can apply an analysis algorithm that considers environmental impact to patent information in the environmental field. By applying different analysis algorithms depending on the category of the patent information, more appropriate analysis results can be provided. 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 patent information category data into the AI, which can then apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 the AI, and the AI ​​can adjust the length of the analysis.

[0083] 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 will prioritize the analysis of the most recent patent information. The analysis unit can also analyze older patent information as needed. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the filing date. This allows the system to provide the latest information by determining 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 patent information filing date data into the AI, and the AI ​​can determine the priority of analysis.

[0084] 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 prioritizes the analysis of highly relevant patent information. It can also postpone the analysis of less relevant patent information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the patent information. This allows for the provision of more relevant information by adjusting 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 patent information relevance data into the AI, and the AI ​​can adjust the order of analysis.

[0085] The generation unit can estimate the user's emotions and adjust the way the generated ideas are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate ideas that proceed at a leisurely pace. If the user is in a hurry, the generation unit can generate ideas that emphasize the shortest route. If the user is excited, the generation unit can generate ideas with visually stimulating effects. By adjusting the way the generated ideas are presented according to the user's emotions, more appropriate ideas can be provided. 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, or not using AI. For example, the generation unit can input user emotion data into an AI, which can then adjust the way the ideas are presented.

[0086] The generation unit can adjust the level of detail of the generated ideas based on the importance of the analysis results during generation. For example, the generation unit can generate detailed ideas based on high-importance analysis results. It can also generate concise ideas based on low-importance analysis results. Furthermore, the generation unit can dynamically adjust the level of detail of the ideas according to the importance of the analysis results. This allows for the provision of more important ideas by adjusting the level of detail of the generated ideas 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 importance data of the analysis results into the AI, which can then adjust the level of detail of the ideas.

[0087] 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 specialized generation algorithm to analysis results in the medical field. It can also apply a technical generation algorithm to analysis results in the IT field. Furthermore, it can apply a generation algorithm that considers environmental impact to analysis results in the environmental field. By applying different generation algorithms depending on the category of the analysis results, more appropriate ideas can be provided. 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 category data of the analysis results into the AI, which can then apply an appropriate generation algorithm.

[0088] The generation unit can estimate the user's emotions and adjust the length of the ideas it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise ideas. If the user is relaxed, the generation unit can generate longer ideas with more detailed explanations. If the user is excited, the generation unit can generate ideas with visually stimulating effects. By adjusting the length of the ideas generated according to the user's emotions, more appropriate ideas can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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, or not using AI. For example, the generation unit can input user emotion data into an AI, which can then adjust the length of the ideas.

[0089] The generation unit can determine the priority of ideas to generate based on the submission timing of the analysis results. For example, the generation unit prioritizes generating ideas based on the latest analysis results. The generation unit can also generate ideas for older analysis results as needed. Furthermore, the generation unit can dynamically adjust the idea generation priority based on the submission timing. This allows the generation unit to provide the latest ideas by determining the priority of ideas to generate 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 analysis result submission timing data into the AI, which can then determine the idea priority.

[0090] The generation unit can adjust the order in which ideas are generated based on the relevance of the analysis results during generation. For example, the generation unit prioritizes generating ideas based on highly relevant analysis results. The generation unit can also postpone generating ideas based on less relevant analysis results. Furthermore, the generation unit can dynamically adjust the order in which ideas are generated according to the relevance of the analysis results. This allows for the provision of more relevant ideas by adjusting the order in which ideas are 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 data of the analysis results into the AI, which can then adjust the order of the ideas.

[0091] The sharing section can estimate the user's emotions and adjust how shared ideas are displayed based on the estimated emotions. For example, if the user is nervous, the sharing section can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. If the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how shared ideas are displayed according to the user's emotions, a more appropriate display method can be provided. 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 sharing section may be performed using AI, for example, or not using AI. For example, the sharing section can input user emotion data into an AI, which can then adjust the display method.

[0092] The sharing unit can select the optimal sharing method by referring to the user's past feedback history when sharing. For example, the sharing unit can prioritize providing sharing methods that the user has previously given high ratings to. The sharing unit can also suggest the optimal sharing method based on the user's feedback history. Furthermore, the sharing unit can analyze the user's past feedback and dynamically adjust the sharing method. This allows it to provide the optimal sharing method by referring to the user's past feedback history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input user feedback history data into AI, which can then select the optimal sharing method.

[0093] The sharing function can filter ideas based on the user's current projects and areas of interest during the sharing process. For example, the sharing function prioritizes sharing ideas related to the user's current projects. It can also filter highly relevant ideas based on the user's areas of interest. Furthermore, the sharing function can provide necessary ideas in a timely manner according to the user's project progress. This allows for the provision of highly relevant ideas by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input the user's project information into an AI, which can then perform the filtering.

[0094] The sharing unit can estimate the user's emotions and prioritize the ideas to share based on those emotions. For example, if the user is stressed, the sharing unit will postpone less important ideas. If the user is relaxed, the sharing unit can prioritize providing detailed ideas. If the user is in a hurry, the sharing unit can quickly provide the most important ideas. This allows for the provision of more appropriate ideas by prioritizing the ideas to share according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input user emotion data into an AI, which can then determine the priority of ideas.

[0095] The sharing function can prioritize sharing highly relevant ideas by considering the user's geographical location during the sharing process. For example, if a user is in a specific region, the sharing function will prioritize sharing ideas related to that region. Furthermore, the sharing function can provide region-specific ideas based on the user's location. Additionally, if a user is on the move, the sharing function can provide ideas relevant to their current location in real time. This allows for the provision of highly relevant ideas by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI, or without AI. For example, the sharing function can input the user's location information into an AI, which can then select highly relevant ideas.

[0096] The sharing function can analyze the user's social media activity and share relevant ideas during the sharing process. For example, the sharing function prioritizes sharing ideas that the user has shown interest in on social media. It can also extract relevant ideas from the user's social media activity. Furthermore, the sharing function can share ideas that the user's followers and friends are interested in. In this way, it can provide relevant ideas by analyzing the user's social media activity. Some or all of the above processing in the sharing function may be performed using AI, for example, or not. For example, the sharing function can input the user's social media data into an AI, which can then extract relevant ideas.

[0097] The sharing section can estimate the user's emotions and adjust how shared ideas are displayed based on the estimated emotions. For example, if the user is nervous, the sharing section can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. If the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how shared ideas are displayed according to the user's emotions, a more appropriate display method can be provided. 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 sharing section may be performed using AI, for example, or not using AI. For example, the sharing section can input user emotion data into an AI, which can then adjust the display method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can analyze the user's past search history and select the optimal collection method when collecting information from the patent database. For example, it can prioritize collecting relevant information based on patent information the user has previously searched for. Furthermore, the data collection unit can focus on collecting information in specific fields based on the user's search history. In addition, the data collection unit can analyze the user's search patterns and determine the optimal collection timing. This allows the system to select the most suitable collection method by analyzing the user's past search history.

[0100] The analysis unit can apply different analysis algorithms depending on the category of patent information when analyzing the collected information. For example, a specialized analysis algorithm can be applied to patent information in the medical field. A technical analysis algorithm can be applied to patent information in the IT field. Furthermore, an analysis algorithm that takes environmental impacts into account can be applied to patent information in the environmental field. By applying different analysis algorithms according to the category of patent information, more appropriate analysis results can be provided.

[0101] The generation unit, when generating business ideas based on analysis results, can estimate the user's emotions and adjust the way the generated ideas are presented based on those estimated emotions. For example, if the user is relaxed, it can generate ideas that proceed at a leisurely pace. If the user is in a hurry, it can generate ideas that emphasize the shortest route. Furthermore, if the user is excited, it can generate ideas with visually stimulating effects. By adjusting the way the generated ideas are presented according to the user's emotions, it is possible to provide more appropriate ideas.

[0102] The sharing section can estimate the user's emotions when sharing generated ideas within the community, and adjust how the shared ideas are displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting how shared ideas are displayed according to the user's emotions, a more appropriate display method can be provided.

[0103] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting patent information. For example, if the user is in a specific region, it will prioritize the collection of patent information related to that region. Furthermore, the data collection unit can provide region-specific patent information based on the user's location. In addition, if the user is on the move, it can provide patent information relevant to their current location in real time. This allows for the provision of highly relevant information by considering the user's geographical location.

[0104] The analysis unit can determine the priority of analysis based on the filing date of the patent information during the analysis process. For example, it can prioritize the analysis of the most recent patent information. Older patent information can be analyzed as needed. Furthermore, the analysis priority can be dynamically adjusted based on the filing date. This allows for the provision of the latest information by prioritizing analysis based on the filing date of the patent information.

[0105] The generation unit can adjust the level of detail of the generated ideas based on the importance of the analysis results during generation. For example, it can generate detailed ideas based on high-importance analysis results, and concise ideas based on low-importance analysis results. Furthermore, it can dynamically adjust the level of detail of the ideas according to the importance of the analysis results. This allows for the provision of more important ideas by adjusting the level of detail of the generated ideas based on the importance of the analysis results.

[0106] The sharing function can analyze a user's social media activity and share relevant ideas during the sharing process. For example, it can prioritize sharing ideas that the user has shown interest in on social media. The sharing function can also extract relevant ideas from the user's social media activity. Furthermore, the sharing function can share ideas that the user's followers and friends are interested in. This allows the system to provide relevant ideas by analyzing the user's social media activity.

[0107] The data collection unit can estimate the user's emotions and determine the priority of patent information to collect based on those emotions. For example, if the user is stressed, less important information will be prioritized. If the user is relaxed, the data collection unit can prioritize providing detailed information. Furthermore, if the user is in a hurry, the most important information can be provided quickly. In this way, by prioritizing the patent information to be collected according to the user's emotions, more relevant information can be provided.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects information from the patent database. The collection unit can, for example, collect information from the patent database using crawling technology. Alternatively, the collection unit can retrieve information from the patent database using an API. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze patent information using, for example, text mining techniques. Alternatively, the analysis unit can analyze patent information using data mining techniques. Step 3: The generation unit generates business ideas based on the analysis results obtained by the analysis unit. For example, the generation unit can set evaluation criteria for ideas based on the analysis results and generate business ideas using a generation algorithm. The generation unit can also generate customizable ideas based on user input. Step 4: The sharing team shares the ideas generated by the generation team and collaborates with other users to improve and expand them. The sharing team can share ideas, for example, through online forums. They can also share ideas using collaboration tools.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect information from a patent database using the communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates business ideas based on the analysis results. The sharing unit allows the generated ideas to be shared within the community using the control unit 46A of the smart device 14. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect information from a patent database using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates business ideas based on the analysis results. The sharing unit allows the generated ideas to be shared within the community using the control unit 46A of the smart glasses 214. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect information from a patent database using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates business ideas based on the analysis results. The sharing unit allows the generated ideas to be shared within the community using the control unit 46A of the headset terminal 314. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and sharing unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect information from a patent database using the communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates business ideas based on the analysis results. The sharing unit allows the generated ideas to be shared within the community using the control unit 46A of the robot 414. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that collects information from a patent database, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates business ideas based on the analysis results obtained by the aforementioned analysis unit, The system includes a sharing unit that shares ideas generated by the generation unit and improves and expands them in cooperation with other users. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from patent databases The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to generate customizable ideas based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate business ideas based on analysis results The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Share generated ideas within the community and collaborate with other users to improve and expand them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, It includes a feature that allows users to evaluate generated ideas and provide feedback. 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 Analyze the user's past search history and select the optimal data 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 the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned 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 information by considering 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 and collect relevant 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 It estimates the user's emotions and adjusts how 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, adjust the level of detail of the generated ideas 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 It estimates the user's emotions and adjusts the length of the 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 ideas to be generated is determined based on the timing of 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 generated ideas 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 aforementioned shared portion is, It estimates the user's emotions and adjusts how shared ideas are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing, the system selects the optimal sharing method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, When sharing, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, It estimates user emotions and prioritizes ideas to share based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing, the system prioritizes sharing highly relevant ideas by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, When sharing, we analyze the user's social media activity and share relevant ideas. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 information from a patent database, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates business ideas based on the analysis results obtained by the aforementioned analysis unit, The system includes a sharing unit that shares ideas generated by the generation unit and improves and expands them in cooperation with other users. A system characterized by the following features.

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

3. The aforementioned analysis unit, The collected information is analyzed to generate customizable ideas based on user input. The system according to feature 1.

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

5. The aforementioned shared portion is, Share the generated ideas within the community and collaborate with other users to improve and expand them. The system according to feature 1.

6. The aforementioned shared portion is, It includes a feature that allows users to evaluate generated ideas and provide feedback. 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 Analyze the user's past search history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting patent information, filtering is performed based on the user's current projects and areas of interest. 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.

Images