system

Abstract

The system according to this embodiment aims to efficiently uncover and appropriately utilize intellectual property hidden within a company. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a judgment unit, a proposal unit, and a utilization proposal unit. The collection unit collects internal company data. The analysis unit analyzes the data collected by the collection unit. The judgment unit determines the potential of the intellectual property based on the results of the analysis by the analysis unit. The proposal unit proposes an application to the Japan Patent Office based on the results determined by the judgment unit. The utilization proposal unit proposes a method for utilizing the intellectual property based on the results proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently discover and appropriately utilize the intellectual property buried within an enterprise.

[0005] The system according to the embodiment aims to efficiently discover and appropriately utilize the intellectual property buried within an enterprise.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a judgment unit, a proposal unit, and a utilization proposal unit. The collection unit collects internal company data. The analysis unit analyzes the data collected by the collection unit. The judgment unit determines the potential of the data as intellectual property based on the results of the analysis by the analysis unit. The proposal unit proposes an application to the Japan Patent Office based on the results determined by the judgment unit. The utilization proposal unit proposes methods for utilizing the intellectual property based on the results proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently uncover and appropriately utilize intellectual property hidden within a company. [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 manages 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) An intellectual property mining system according to an embodiment of the present invention is a system that unearths intellectual property hidden within a company and determines its feasibility as a patent or utility model. The intellectual property mining system uses multimodal generative AI to cross-sectionally unearth and analyze groups of information that can be established as intellectual property from all kinds of materials scattered within the company. Next, it determines the feasibility of obtaining patents, utility models, etc., for the unearthed groups of information and proposes an application to the Japan Patent Office. This system makes it possible to asset out intellectual property hidden within a company, maximize corporate value, and enhance technological superiority over competitors. For example, the intellectual property mining system collects all kinds of materials scattered within the company. For example, it collects data such as text, images, and audio from shared folders, emails, chats, etc. At this stage, it also performs preprocessing such as data normalization and deduplication to improve the accuracy of subsequent analysis. Next, the intellectual property mining system analyzes the collected data in detail. It uses advanced natural language processing to analyze text-based information in detail. The system utilizes advanced language comprehension capabilities, including contextual understanding, interpretation of technical terms, and inference of potential value, to explore the potential of intellectual property. Furthermore, the intellectual property mining system builds a RAG (Retrieval-Augmented Generation) system that integrates specialized knowledge to identify and evaluate intellectual property. This system constantly learns the latest patent information and market trends, providing highly accurate evaluations. Finally, the intellectual property mining system uses AI to understand a company's management strategy and propose ways to utilize intellectual property based on that understanding. Machine learning models learn from past success stories and market trends to generate strategies optimized for each company. This allows intellectual property hidden within a company to be turned into assets, maximizing corporate value and enhancing technological superiority over competitors. For example, in the case of manufacturing company A, after system implementation, the AI ​​analyzed experimental data from 10 years ago and discovered the potential of a new material that aligns with current sustainability trends. This material, which was not noticed at the time, possessed properties that comply with current environmental regulations. The AI ​​evaluated the market value of the new material, generated detailed proposals for patenting and product development, and the management decided to launch a new business. With the support of AI, we were able to quickly file a patent application and successfully launch a new product into the market one year later.This allows the intellectual property mining system to uncover hidden intellectual property within a company, determine its potential for patent or utility model registration, and maximize corporate value.

[0029] The intellectual property mining system according to this embodiment comprises a collection unit, an analysis unit, a judgment unit, a proposal unit, and a utilization proposal unit. The collection unit collects internal company materials. The collection unit can collect data such as text, images, and audio from sources such as shared folders, emails, and chats. For example, the collection unit collects technical documents from shared folders. The collection unit can also collect research reports from email attachments. Furthermore, the collection unit can collect meeting records from chat logs. For example, the collection unit collects PDF files from shared folders and saves them to a database. It collects JPEG images from email attachments and saves them to an image database. It collects MP3 audio files from chat logs and saves them to an audio database. The analysis unit analyzes the materials collected by the collection unit. For example, the analysis unit can analyze the collected data in detail and perform contextual understanding and interpretation of technical terms. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also interpret technical terms using machine learning models. Furthermore, the analysis unit can infer potential value using data mining techniques. For example, the analysis unit can analyze the content of technical documents using natural language processing techniques and extract important keywords. It can interpret specialized terminology in research reports and understand their meaning using machine learning models. It can infer potential value from meeting records using data mining techniques. The judgment unit determines the potential of intellectual property based on the results analyzed by the analysis unit. For example, the judgment unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. The judgment unit can determine the potential of intellectual property based on criteria such as novelty, inventiveness, and industrial applicability. The judgment unit can also evaluate the value of intellectual property by considering patent information and market trends. Furthermore, the judgment unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. For example, the judgment unit can evaluate the content of technical documents based on the novelty criterion and determine the likelihood of obtaining a patent. It can evaluate the content of research reports based on the inventiveness criterion and determine the likelihood of obtaining a utility model. It can evaluate the content of meeting records based on the industrial applicability criterion and determine the value of intellectual property.The Proposal Department proposes applications to the Japan Patent Office based on the results determined by the Judgment Department. The Proposal Department can, for example, propose the preparation of patent application documents. The Proposal Department can, for example, propose methods for preparing patent application documents. The Proposal Department can also propose the preparation of utility model application documents. Furthermore, the Proposal Department can also propose application procedures to the Japan Patent Office. For example, the Proposal Department proposes methods for preparing patent application documents and explains how to submit them to the Japan Patent Office. It proposes methods for preparing utility model application documents and explains how to submit them to the Japan Patent Office. It proposes application procedures to the Japan Patent Office and explains how to prepare the necessary documents. The Utilization Proposal Department proposes ways to utilize intellectual property based on the results proposed by the Proposal Department. The Utilization Proposal Department can, for example, propose ways to utilize intellectual property based on a company's business strategy. The Utilization Proposal Department makes proposals for license agreements. Furthermore, the Utilization Proposal Department can also propose joint research. Furthermore, the Utilization Proposal Department can also propose commercialization. For example, the Utilization Proposal Department makes proposals for license agreements and explains the contract terms. It makes proposals for joint research and explains the research content and cooperation system. We propose product development and explain the product development plan and market launch method. This allows the intellectual property mining system, according to the embodiment, to uncover hidden intellectual property within a company, determine its potential for patent or utility model registration, and maximize corporate value.

[0030] The data collection department collects internal company documents. For example, it can collect data such as text, images, and audio from shared folders, emails, and chats. Specifically, when collecting technical documents from shared folders, it automatically scans PDF and Word documents within the folder and extracts relevant technical information. When collecting research reports from email attachments, it accesses the email server, filters based on specific keywords and senders, and extracts the necessary files. Furthermore, when collecting meeting records from chat logs, it can utilize the chat application's API to retrieve specific chat rooms and conversation content, and use speech recognition technology to convert audio data into text. For example, the data collection department can collect PDF files from shared folders and save them to a database. It can collect JPEG images from email attachments and save them to an image database. It can collect MP3 audio files from chat logs and save them to an audio database. This allows the data collection department to efficiently gather information from diverse data sources and comprehensively understand the company's intellectual property information. Furthermore, the data collection department can centrally manage the collected data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and decision-making units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the materials collected by the collection department. For example, the analysis department can analyze the collected data in detail, understanding the context and interpreting technical terms. Specifically, it can analyze text data using natural language processing techniques to understand the content of documents. For instance, it can analyze the content of technical documents and extract important keywords. This can utilize topic modeling and text classification algorithms. It can also interpret technical terms using machine learning models. For example, it can use pre-trained models to interpret technical terms in research reports and understand their meaning. Furthermore, it can infer potential value using data mining techniques. For example, it can use association rule mining and clustering techniques to infer potential value from meeting records. This allows the analysis department to analyze collected data quickly and accurately, gaining a deep understanding of intellectual property information within the company. Additionally, the analysis department can leverage historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict trends in specific technological fields based on past technical documents and research reports, suggesting future research and development directions. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Judgment Unit determines the potential of intellectual property based on the results of the analysis conducted by the Analysis Unit. For example, the Judgment Unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. Specifically, it determines the potential of intellectual property based on criteria such as novelty, inventive step, and industrial applicability. For example, it evaluates the content of a technical document based on the novelty criterion to determine the likelihood of obtaining a patent. It evaluates the content of a research report based on the inventive step criterion to determine the likelihood of obtaining a utility model. It evaluates the content of a meeting record based on the industrial applicability criterion to determine the value of the intellectual property. The Judgment Unit can also evaluate the value of intellectual property by considering patent information and market trends. For example, it can refer to patent databases, investigate patent information for similar technologies, and understand the trends of competitors. Furthermore, the Judgment Unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. The RAG system automatically evaluates the value of intellectual property and determines the priority of patent applications based on past patent information and market data. This allows the Judgment Unit to evaluate the potential of intellectual property with high accuracy and optimize the company's intellectual property strategy. Furthermore, based on the evaluation results, the decision-making unit can formulate a patent application strategy and make specific suggestions to strengthen the company's intellectual property portfolio. For example, it can determine the priority of patent applications in specific technological fields and optimize resource allocation. The decision-making unit can also conduct a risk assessment of patent applications and take measures to minimize those risks. In this way, the decision-making unit can effectively support the company's intellectual property strategy and maximize the value of its intellectual property.

[0033] The Proposal Department proposes applications to the Japan Patent Office (JPO) based on the decisions made by the Judgment Department. For example, the Proposal Department can propose the preparation of patent application documents. Specifically, it can propose methods for preparing patent application documents and explain how to submit them to the JPO. For example, it can propose methods for preparing patent application documents and explain how to submit them to the JPO. It can also propose methods for preparing utility model application documents and explain how to submit them to the JPO. Furthermore, it can propose application procedures to the JPO and explain how to prepare the necessary documents. The Proposal Department collects the information necessary for preparing patent application documents and prepares the documents in the appropriate format. The Proposal Department also supports the submission procedures to the JPO, providing deadlines and checklists of necessary documents. In addition, the Proposal Department can formulate patent application strategies and make specific suggestions to strengthen a company's intellectual property portfolio. For example, it can determine the priority of patent applications in specific technological fields and optimize resource allocation. The Proposal Department can also conduct risk assessments of patent applications and take measures to minimize risks. This allows the Proposal Department to effectively support a company's intellectual property strategy and maximize the value of its intellectual property. Furthermore, the proposal department can monitor the progress of patent applications and make suggestions for revisions or additions as needed. For example, it can suggest revisions to application documents or provide additional information based on the examination results from the patent office. In addition, the proposal department can analyze past application data and develop optimal application strategies to improve the success rate of patent applications. This allows the proposal department to continuously improve the company's intellectual property strategy and maximize the value of intellectual property.

[0034] The Intellectual Property Utilization Proposal Department proposes methods for utilizing intellectual property based on the results proposed by the Proposal Department. For example, the Department can propose methods for utilizing intellectual property based on a company's management strategy. Specifically, it can propose license agreements and explain the contract terms. For example, it can propose a license agreement for a specific technology and explain the contract terms and how royalties are set. The Department can also propose joint research and explain the research content and cooperation system. For example, it can propose joint research in a specific technological field and explain the research content, cooperation system, and how research results will be shared. Furthermore, the Department can propose product commercialization and explain the product development plan and how to bring the product to market. For example, it can propose a development plan for a new product based on a specific technology and explain the timing of market entry and marketing strategy. In this way, the Department can make concrete proposals to maximize the utilization of a company's intellectual property and improve its corporate value. Furthermore, the Department can collect feedback on the utilization of intellectual property and continuously improve the accuracy and effectiveness of its proposals. For example, it can monitor the implementation status of license agreements and the progress of joint research and revise the proposals as needed. Furthermore, the Intellectual Property Utilization Proposal Department can analyze market trends and competitor activities related to intellectual property utilization and formulate optimal utilization strategies. This allows the Department to make concrete proposals for effectively utilizing a company's intellectual property and maximizing corporate value. In addition, the Department can provide education and training on intellectual property utilization, improving intellectual property awareness within the company. For example, it can hold seminars and workshops on the importance of intellectual property and methods of utilization to raise employees' awareness. The Department can also share successful case studies related to intellectual property utilization, promoting its use throughout the company. This enables the Department to make concrete proposals for maximizing the utilization of a company's intellectual property and improving corporate value.

[0035] The data collection unit can collect data such as text, images, and audio from shared folders, emails, chats, etc. For example, the data collection unit can collect technical documents from shared folders. For example, the data collection unit can collect research reports from email attachments. For example, the data collection unit can collect meeting records from chat logs. For example, the data collection unit can collect PDF files from shared folders and save them to a database. For example, the data collection unit can collect JPEG images from email attachments and save them to an image database. For example, the data collection unit can collect MP3 audio files from chat logs and save them to an audio database. This allows for the collection of all kinds of internal materials, which can be used to uncover intellectual property. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input technical documents from shared folders into a generating AI and have the generating AI perform the collection of technical documents.

[0036] The analysis department can analyze the collected data in detail, understand the context, and interpret technical terms. For example, the analysis department can analyze text data using natural language processing technology. For example, the analysis department can interpret technical terms using machine learning models. For example, the analysis department can infer potential value using data mining technology. For example, the analysis department can analyze the content of technical documents using natural language processing technology and extract important keywords. For example, the analysis department can interpret technical terms in research reports and understand their meaning using machine learning models. For example, the analysis department can infer potential value from meeting records using data mining technology. This allows for a detailed analysis of the collected data and the exploration of intellectual property potential. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected text data into a generating AI and have the generating AI perform the analysis of the text data.

[0037] The judgment unit can determine the potential of intellectual property based on the analysis results. For example, the judgment unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. For example, the judgment unit can determine the potential of intellectual property based on criteria such as novelty, inventiveness, and industrial applicability. For example, the judgment unit can evaluate the value of intellectual property by considering patent information and market trends. For example, the judgment unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. For example, the judgment unit can evaluate the content of a technical document based on the novelty criterion and determine the likelihood of obtaining a patent. For example, the judgment unit can evaluate the content of a research report based on the inventiveness criterion and determine the likelihood of obtaining a utility model. For example, the judgment unit can evaluate the content of a meeting record based on the industrial applicability criterion and determine the value of intellectual property. This allows the judgment unit to determine the potential of intellectual property based on the analysis results and evaluate the likelihood of obtaining a patent or utility model. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the analysis results into a generating AI and have the generating AI perform the determination of the potential of intellectual property.

[0038] The proposal department can propose an application to the Japan Patent Office. The proposal department can, for example, propose the preparation of patent application documents. The proposal department can, for example, propose a method for preparing patent application documents. The proposal department can, for example, propose the preparation of utility model application documents. The proposal department can, for example, propose the application procedure to the Japan Patent Office. For example, the proposal department can propose a method for preparing patent application documents and explain how to submit them to the Japan Patent Office. For example, the proposal department can propose a method for preparing utility model application documents and explain how to submit them to the Japan Patent Office. The proposal department can, for example, propose an application procedure to the Japan Patent Office and explain how to prepare the necessary documents. This allows the proposal department to propose an application to the Japan Patent Office and support the acquisition of intellectual property rights. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the preparation of patent application documents into a generating AI and have the generating AI perform the preparation of patent application documents.

[0039] The Intellectual Property Utilization Proposal Department can propose ways to utilize intellectual property based on a company's business strategy. For example, the Department can propose license agreements. For example, the Department can propose joint research. For example, the Department can propose product development. For example, the Department can propose license agreements and explain the contract terms. For example, the Department can propose joint research and explain the research content and cooperation system. For example, the Department can propose product development and explain the product development plan and market launch method. In this way, it is possible to propose ways to utilize intellectual property based on a company's business strategy and maximize corporate value. Some or all of the above processes in the Intellectual Property Utilization Proposal Department may be performed using AI, for example, or not using AI. For example, the Department can input a company's business strategy into a generating AI and have the generating AI execute proposals for ways to utilize intellectual property.

[0040] The data collection unit can determine the priority of data collection based on the importance of the data at the time of collection. For example, the data collection unit can prioritize the collection of highly important data and provide it quickly for subsequent analysis. For example, the data collection unit can postpone the collection of less important data and prioritize the collection of highly important data. For example, the data collection unit can evaluate the importance of the data in real time and dynamically adjust the collection priority. This allows the collection priority to be determined based on the importance of the data, and important data to be collected preferentially. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the importance of the data into a generating AI and have the generating AI perform the determination of the collection priority.

[0041] The data collection unit can apply different collection algorithms depending on the format of the data during collection. For example, the data collection unit can apply a collection algorithm using natural language processing to text data. For example, the data collection unit can apply a collection algorithm using image recognition technology to image data. For example, the data collection unit can apply a collection algorithm using speech recognition technology to audio data. For example, the data collection unit can perform data collection using natural language processing technology for text data. For example, the data collection unit can perform data collection using image recognition technology for image data. For example, the data collection unit can perform data collection using speech recognition technology for audio data. This makes it possible to apply an appropriate collection algorithm according to the format of the data and achieve efficient data collection. 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 format of the data into a generating AI and have the generating AI execute the application of the collection algorithm.

[0042] The collection unit can perform data collection while considering the attribute information of the data creator. For example, if the data creator is an expert, the collection unit will prioritize collecting that data. For example, if the data creator is a newcomer, the collection unit will postpone collecting that data. For example, the collection unit will evaluate the data creator's past performance to determine the collection priority. For example, if the data creator is an expert, the collection unit will prioritize collecting that data and store it in the database. For example, if the data creator is a newcomer, the collection unit will postpone collecting that data and prioritize collecting high-importance data. For example, the collection unit will evaluate the data creator's past performance to determine the collection priority. This allows the collection to be performed while considering the attribute information of the data creator, and important data to be collected preferentially. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the attribute information of the data creator into a generating AI and have the generating AI determine the collection priority.

[0043] The data collection unit can adjust the collection order based on the relevance of the materials during collection. For example, the data collection unit can prioritize the collection of highly relevant materials to aid in subsequent analysis. For example, the data collection unit can postpone the collection of less relevant materials and prioritize the collection of important materials. For example, the data collection unit can evaluate the relevance of materials in real time and dynamically adjust the collection order. This allows for efficient data collection by adjusting the collection order based on the relevance of the materials. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the relevance of materials into a generating AI and have the generating AI perform the adjustment of the collection order.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data to provide deep insights. For example, the analysis unit can perform a concise analysis on low-importance data to grasp the overall picture. For example, the analysis unit can evaluate the importance of the data in real time and dynamically adjust the level of detail of the analysis. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the data during analysis. For example, the analysis unit can apply an analysis algorithm using natural language processing to text data. For example, the analysis unit can apply an analysis algorithm using image recognition technology to image data. For example, the analysis unit can apply an analysis algorithm using speech recognition technology to audio data. For example, the analysis unit can perform analysis on text data using natural language processing technology. For example, the analysis unit can perform analysis on image data using image recognition technology. For example, the analysis unit can perform analysis on audio data using speech recognition technology. This allows for the application of an appropriate analysis algorithm according to the category of the data, enabling efficient data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the creation date of the data during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data to provide the latest information. For example, the analysis unit may postpone the analysis of older data and prioritize the analysis of the latest data. For example, the analysis unit may evaluate the creation date of the data in real time and dynamically adjust the analysis priority. This allows the analysis priority to be determined based on the creation date of the data and the latest information to be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the creation date of the data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data to aid in subsequent decision-making. For example, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. For example, the analysis unit can evaluate the relevance of data in real time and dynamically adjust the order of analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships between materials. For example, the decision-making unit analyzes the interrelationships between materials and makes decisions based on highly relevant materials. For example, the decision-making unit evaluates the interrelationships between materials and improves the accuracy of its decisions. For example, the decision-making unit evaluates the interrelationships between materials in real time and dynamically adjusts the accuracy of its decisions. This allows for improved accuracy by considering the interrelationships between materials, thereby achieving accurate decisions. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the interrelationships between materials into a generating AI and have the generating AI perform the improvement of the accuracy of its decisions.

[0049] The decision-making unit can make decisions by considering the attribute information of the document creator. For example, if the document creator is an expert, the decision-making unit will give more weight to that document when making a decision. For example, if the document creator is a newcomer, the decision-making unit will only use that document as reference. For example, the decision-making unit will evaluate the document creator's past performance and adjust the criteria for judgment. For example, if the document creator is an expert, the decision-making unit will give more weight to that document when making a decision. For example, if the document creator is a newcomer, the decision-making unit will only use that document as reference. For example, the decision-making unit will evaluate the document creator's past performance and adjust the criteria for judgment. This allows the decision-making unit to make decisions by considering the attribute information of the document creator and to achieve accurate judgments. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the attribute information of the document creator into a generating AI and have the generating AI perform the adjustment of the criteria for judgment.

[0050] The decision-making unit can make decisions while considering the geographical distribution of the data. For example, the decision-making unit can analyze the geographical distribution of the data and make decisions while considering the characteristics of each region. For example, the decision-making unit can prioritize the evaluation of geographically close data to improve the accuracy of the decision. For example, the decision-making unit can evaluate the geographical distribution of the data in real time and dynamically adjust the criteria for judgment. This makes it possible to make decisions while considering the geographical distribution of the data and to achieve accurate judgments that reflect the characteristics of each region. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the adjustment of the criteria for judgment.

[0051] The decision-making unit can improve the accuracy of its judgment by referring to relevant literature related to the material at the time of judgment. For example, the decision-making unit can refer to relevant literature related to the material to improve the accuracy of its judgment. For example, the decision-making unit can evaluate the reliability of the material based on the relevant literature and make a judgment. For example, the decision-making unit can refer to relevant literature related to the material in real time and dynamically adjust the criteria for judgment. This makes it possible to improve the accuracy of judgment by referring to relevant literature related to the material and to achieve accurate judgment. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input relevant literature related to the material into a generating AI and have the generating AI perform the improvement of the accuracy of the judgment.

[0052] The proposal department can adjust the level of detail in a proposal based on the importance of the materials used. For example, the proposal department may include detailed explanations in proposals based on highly important materials, while keeping proposals based on less important materials concise. The proposal department may also evaluate the importance of materials in real time and dynamically adjust the level of detail in the proposal. This allows for efficient information provision by adjusting the level of detail in the proposal based on the importance of the materials. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department may input the importance of materials into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0053] The proposal unit can apply different proposal algorithms depending on the category of the material when making a proposal. For example, the proposal unit can apply a proposal algorithm using natural language processing to text data. For example, the proposal unit can apply a proposal algorithm using image recognition technology to image data. For example, the proposal unit can apply a proposal algorithm using speech recognition technology to audio data. For example, the proposal unit makes proposals to text data using natural language processing technology. For example, the proposal unit makes proposals to image data using image recognition technology. For example, the proposal unit makes proposals to audio data using speech recognition technology. This makes it possible to apply an appropriate proposal algorithm according to the category of the material and realize efficient information provision. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the material into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0054] The proposal department can determine the priority of proposals based on the creation date of the materials at the time of proposal submission. For example, the proposal department will prioritize proposals based on the latest materials. For example, the proposal department will postpone proposals based on older materials. For example, the proposal department can evaluate the creation date of the materials in real time and dynamically adjust the priority of proposals. This allows the department to determine the priority of proposals based on the creation date of the materials and provide the latest information. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the creation date of the materials into a generating AI and have the generating AI perform the determination of proposal priority.

[0055] The proposal department can adjust the order of proposals based on the relevance of the materials when submitting proposals. For example, the proposal department will prioritize proposals based on highly relevant materials. For example, the proposal department will postpone proposals based on less relevant materials. For example, the proposal department can evaluate the relevance of materials in real time and dynamically adjust the order of proposals. This allows for efficient information provision by adjusting the order of proposals based on the relevance of the materials. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the relevance of materials into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0056] The utilization proposal department can analyze the past usage history of a document and select the optimal utilization method when making a utilization proposal. For example, the utilization proposal department can analyze the past usage history of a document and propose the most effective utilization method. For example, the utilization proposal department can propose utilization methods for similar documents based on past usage history. For example, the utilization proposal department can evaluate the past usage history of a document in real time and dynamically select the optimal utilization method. This enables efficient information provision by analyzing the past usage history of a document and selecting the optimal utilization method. Some or all of the above processing in the utilization proposal department may be performed using AI, for example, or without AI. For example, the utilization proposal department can input the past usage history of a document into a generating AI and have the generating AI select the optimal utilization method.

[0057] The utilization proposal unit can customize the means of utilization proposals based on the current market trends of the material when making a proposal. For example, the utilization proposal unit can analyze current market trends and propose the most effective means of utilization. For example, the utilization proposal unit can propose means of utilization that maximize the value of the material based on market trends. For example, the utilization proposal unit can evaluate the current market trends of the material in real time and dynamically customize the means of utilization proposals. This makes it possible to customize the means of utilization proposals based on the current market trends of the material and realize efficient information provision. Some or all of the above processing in the utilization proposal unit may be performed using AI, for example, or without AI. For example, the utilization proposal unit can input the market trends of the material into a generating AI and have the generating AI perform the customization of the means of utilization proposals.

[0058] The utilization proposal unit can select the optimal utilization method when proposing utilization, taking into account the geographical distribution of the materials. For example, the utilization proposal unit can analyze the geographical distribution of the materials and propose the optimal utilization method considering the characteristics of each region. For example, the utilization proposal unit can prioritize the evaluation of geographically close materials and select the optimal utilization method. For example, the utilization proposal unit can evaluate the geographical distribution of the materials in real time and dynamically adjust the means of utilization proposal. This makes it possible to select the optimal utilization method considering the geographical distribution of the materials and realize efficient information provision. Some or all of the above processing in the utilization proposal unit may be performed using AI, for example, or without AI. For example, the utilization proposal unit can input the geographical distribution of the materials into a generating AI and have the generating AI select the optimal utilization method.

[0059] The utilization proposal unit can improve the accuracy of its utilization proposals by referring to related literature for the materials when making a proposal. For example, the utilization proposal unit can refer to related literature for the materials to improve the accuracy of its utilization proposals. For example, the utilization proposal unit can evaluate the reliability of the materials based on related literature and make utilization proposals. For example, the utilization proposal unit can refer to related literature for the materials in real time and dynamically adjust the criteria for utilization proposals. This makes it possible to improve the accuracy of utilization proposals by referring to related literature for the materials and to achieve efficient information provision. Some or all of the above processing in the utilization proposal unit may be performed using AI, for example, or without AI. For example, the utilization proposal unit can input related literature for the materials into a generating AI and have the generating AI perform the task of improving the accuracy of utilization proposals.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The intellectual property mining system can prioritize data collection based on the importance of the data in its collection unit. For example, it can prioritize the collection of highly important data and provide it quickly for subsequent analysis. Alternatively, it can postpone the collection of less important data and prioritize the collection of highly important data. Furthermore, it can evaluate the importance of data in real time and dynamically adjust the collection priority. This allows for the determination of collection priorities based on the importance of the data, ensuring that important data is collected preferentially.

[0062] The intellectual property mining system can improve the accuracy of its judgments by considering the interrelationships between data in its decision-making unit. For example, it can analyze the interrelationships between data and make decisions based on highly relevant data. It can also evaluate the interrelationships between data and improve the accuracy of its judgments. Furthermore, it can evaluate the interrelationships between data in real time and dynamically adjust the accuracy of its judgments. This allows for improved judgment accuracy by considering the interrelationships between data, thereby achieving accurate decisions.

[0063] The intellectual property mining system allows the utilization proposal department to analyze the past usage history of materials and select the optimal utilization method. For example, it can analyze the past usage history of a material and propose the most effective utilization method. It can also propose utilization methods for similar materials based on past usage history. Furthermore, it can evaluate the past usage history of a material in real time and dynamically select the optimal utilization method. This enables efficient information provision by analyzing the past usage history of materials and selecting the optimal utilization method.

[0064] The intellectual property mining system can apply different collection algorithms to the data in its collection unit depending on the format of the data. For example, a collection algorithm using natural language processing can be applied to text data. Similarly, a collection algorithm using image recognition technology can be applied to image data. Furthermore, a collection algorithm using speech recognition technology can be applied to audio data. This allows for the application of an appropriate collection algorithm according to the data format, enabling efficient data collection.

[0065] The intellectual property mining system allows the analysis department to apply different analysis algorithms depending on the category of the data. For example, natural language processing can be applied to text data. Image data can be analyzed using image recognition technology. Furthermore, audio data can be analyzed using speech recognition technology. This enables the application of appropriate analysis algorithms according to the category of data, resulting in efficient data analysis.

[0066] The intellectual property mining system allows the proposal department to prioritize proposals based on the creation date of the documents. For example, proposals based on the most recent documents can be given priority, while proposals based on older documents can be postponed. Furthermore, the system can evaluate the creation date of documents in real time and dynamically adjust the proposal priority. This allows for the prioritization of proposals based on the creation date of the documents and the provision of the latest information.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects internal company materials. The data collection unit can collect data such as text, images, and audio from sources such as shared folders, emails, and chats. For example, the data collection unit can collect technical documents from shared folders. The data collection unit can also collect research reports from email attachments. Furthermore, the data collection unit can collect meeting records from chat logs. For example, the data collection unit can collect PDF files from shared folders and save them to a database. It can collect JPEG images from email attachments and save them to an image database. It can collect MP3 audio files from chat logs and save them to an audio database. Step 2: The analysis department analyzes the materials collected by the collection department. The analysis department can, for example, analyze the collected data in detail to understand the context and interpret technical terms. The analysis department can, for example, analyze text data using natural language processing techniques. The analysis department can also interpret technical terms using machine learning models. Furthermore, the analysis department can infer potential value using data mining techniques. For example, the analysis department can analyze the content of technical documents using natural language processing techniques and extract important keywords. It can interpret technical terms in research reports and understand their meaning using machine learning models. It can infer potential value from meeting records using data mining techniques. Step 3: The Judgment Unit determines the potential of intellectual property based on the results of the analysis conducted by the Analysis Unit. For example, the Judgment Unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. The Judgment Unit determines the potential of intellectual property based on criteria such as novelty, inventiveness, and industrial applicability. The Judgment Unit can also evaluate the value of intellectual property by considering patent information and market trends. Furthermore, the Judgment Unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. For example, the Judgment Unit evaluates the content of a technical document based on the novelty criterion and determines the likelihood of obtaining a patent. It evaluates the content of a research report based on the inventiveness criterion and determines the likelihood of obtaining a utility model. It evaluates the content of a meeting record based on the industrial applicability criterion and determines the value of the intellectual property. Step 4: The Proposal Department proposes an application to the Japan Patent Office based on the results determined by the Judgment Department. The Proposal Department may, for example, propose the preparation of patent application documents. The Proposal Department may, for example, propose a method for preparing patent application documents. The Proposal Department may also propose the preparation of utility model application documents. Furthermore, the Proposal Department may propose the application procedure to the Japan Patent Office. For example, the Proposal Department may propose a method for preparing patent application documents and explain how to submit them to the Japan Patent Office. It may propose a method for preparing utility model application documents and explain how to submit them to the Japan Patent Office. It may propose the application procedure to the Japan Patent Office and explain how to prepare the necessary documents. Step 5: The Utilization Proposal Department proposes ways to utilize the intellectual property based on the results proposed by the Proposal Department. For example, the Utilization Proposal Department can propose ways to utilize the intellectual property based on the company's business strategy. For example, the Utilization Proposal Department can propose a license agreement. The Utilization Proposal Department can also propose joint research. Furthermore, the Utilization Proposal Department can also propose product development. For example, the Utilization Proposal Department can propose a license agreement and explain the contract terms. It can propose joint research and explain the research content and cooperation system. It can propose product development and explain the product development plan and method of market introduction.

[0069] (Example of form 2) An intellectual property mining system according to an embodiment of the present invention is a system that unearths intellectual property hidden within a company and determines its feasibility as a patent or utility model. The intellectual property mining system uses multimodal generative AI to cross-sectionally unearth and analyze groups of information that can be established as intellectual property from all kinds of materials scattered within the company. Next, it determines the feasibility of obtaining patents, utility models, etc., for the unearthed groups of information and proposes an application to the Japan Patent Office. This system makes it possible to asset out intellectual property hidden within a company, maximize corporate value, and enhance technological superiority over competitors. For example, the intellectual property mining system collects all kinds of materials scattered within the company. For example, it collects data such as text, images, and audio from shared folders, emails, chats, etc. At this stage, it also performs preprocessing such as data normalization and deduplication to improve the accuracy of subsequent analysis. Next, the intellectual property mining system analyzes the collected data in detail. It uses advanced natural language processing to analyze text-based information in detail. The system utilizes advanced language comprehension capabilities, including contextual understanding, interpretation of technical terms, and inference of potential value, to explore the potential of intellectual property. Furthermore, the intellectual property mining system builds a RAG (Retrieval-Augmented Generation) system that integrates specialized knowledge to identify and evaluate intellectual property. This system constantly learns the latest patent information and market trends, providing highly accurate evaluations. Finally, the intellectual property mining system uses AI to understand a company's management strategy and propose ways to utilize intellectual property based on that understanding. Machine learning models learn from past success stories and market trends to generate strategies optimized for each company. This allows intellectual property hidden within a company to be turned into assets, maximizing corporate value and enhancing technological superiority over competitors. For example, in the case of manufacturing company A, after system implementation, the AI ​​analyzed experimental data from 10 years ago and discovered the potential of a new material that aligns with current sustainability trends. This material, which was not noticed at the time, possessed properties that comply with current environmental regulations. The AI ​​evaluated the market value of the new material, generated detailed proposals for patenting and product development, and the management decided to launch a new business. With the support of AI, we were able to quickly file a patent application and successfully launch a new product into the market one year later.This allows the intellectual property mining system to uncover hidden intellectual property within a company, determine its potential for patent or utility model registration, and maximize corporate value.

[0070] The intellectual property mining system according to this embodiment comprises a collection unit, an analysis unit, a judgment unit, a proposal unit, and a utilization proposal unit. The collection unit collects internal company materials. The collection unit can collect data such as text, images, and audio from sources such as shared folders, emails, and chats. For example, the collection unit collects technical documents from shared folders. The collection unit can also collect research reports from email attachments. Furthermore, the collection unit can collect meeting records from chat logs. For example, the collection unit collects PDF files from shared folders and saves them to a database. It collects JPEG images from email attachments and saves them to an image database. It collects MP3 audio files from chat logs and saves them to an audio database. The analysis unit analyzes the materials collected by the collection unit. For example, the analysis unit can analyze the collected data in detail and perform contextual understanding and interpretation of technical terms. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also interpret technical terms using machine learning models. Furthermore, the analysis unit can infer potential value using data mining techniques. For example, the analysis unit can analyze the content of technical documents using natural language processing techniques and extract important keywords. It can interpret specialized terminology in research reports and understand their meaning using machine learning models. It can infer potential value from meeting records using data mining techniques. The judgment unit determines the potential of intellectual property based on the results analyzed by the analysis unit. For example, the judgment unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. The judgment unit can determine the potential of intellectual property based on criteria such as novelty, inventiveness, and industrial applicability. The judgment unit can also evaluate the value of intellectual property by considering patent information and market trends. Furthermore, the judgment unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. For example, the judgment unit can evaluate the content of technical documents based on the novelty criterion and determine the likelihood of obtaining a patent. It can evaluate the content of research reports based on the inventiveness criterion and determine the likelihood of obtaining a utility model. It can evaluate the content of meeting records based on the industrial applicability criterion and determine the value of intellectual property.The Proposal Department proposes applications to the Japan Patent Office based on the results determined by the Judgment Department. The Proposal Department can, for example, propose the preparation of patent application documents. The Proposal Department can, for example, propose methods for preparing patent application documents. The Proposal Department can also propose the preparation of utility model application documents. Furthermore, the Proposal Department can also propose application procedures to the Japan Patent Office. For example, the Proposal Department proposes methods for preparing patent application documents and explains how to submit them to the Japan Patent Office. It proposes methods for preparing utility model application documents and explains how to submit them to the Japan Patent Office. It proposes application procedures to the Japan Patent Office and explains how to prepare the necessary documents. The Utilization Proposal Department proposes ways to utilize intellectual property based on the results proposed by the Proposal Department. The Utilization Proposal Department can, for example, propose ways to utilize intellectual property based on a company's business strategy. The Utilization Proposal Department makes proposals for license agreements. Furthermore, the Utilization Proposal Department can also propose joint research. Furthermore, the Utilization Proposal Department can also propose commercialization. For example, the Utilization Proposal Department makes proposals for license agreements and explains the contract terms. It makes proposals for joint research and explains the research content and cooperation system. We propose product development and explain the product development plan and market launch method. This allows the intellectual property mining system, according to the embodiment, to uncover hidden intellectual property within a company, determine its potential for patent or utility model registration, and maximize corporate value.

[0071] The data collection department collects internal company documents. For example, it can collect data such as text, images, and audio from shared folders, emails, and chats. Specifically, when collecting technical documents from shared folders, it automatically scans PDF and Word documents within the folder and extracts relevant technical information. When collecting research reports from email attachments, it accesses the email server, filters based on specific keywords and senders, and extracts the necessary files. Furthermore, when collecting meeting records from chat logs, it can utilize the chat application's API to retrieve specific chat rooms and conversation content, and use speech recognition technology to convert audio data into text. For example, the data collection department can collect PDF files from shared folders and save them to a database. It can collect JPEG images from email attachments and save them to an image database. It can collect MP3 audio files from chat logs and save them to an audio database. This allows the data collection department to efficiently gather information from diverse data sources and comprehensively understand the company's intellectual property information. Furthermore, the data collection department can centrally manage the collected data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and decision-making units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis department analyzes the materials collected by the collection department. For example, the analysis department can analyze the collected data in detail, understanding the context and interpreting technical terms. Specifically, it can analyze text data using natural language processing techniques to understand the content of documents. For instance, it can analyze the content of technical documents and extract important keywords. This can utilize topic modeling and text classification algorithms. It can also interpret technical terms using machine learning models. For example, it can use pre-trained models to interpret technical terms in research reports and understand their meaning. Furthermore, it can infer potential value using data mining techniques. For example, it can use association rule mining and clustering techniques to infer potential value from meeting records. This allows the analysis department to analyze collected data quickly and accurately, gaining a deep understanding of intellectual property information within the company. Additionally, the analysis department can leverage historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict trends in specific technological fields based on past technical documents and research reports, suggesting future research and development directions. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The Judgment Unit determines the potential of intellectual property based on the results of the analysis conducted by the Analysis Unit. For example, the Judgment Unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. Specifically, it determines the potential of intellectual property based on criteria such as novelty, inventive step, and industrial applicability. For example, it evaluates the content of a technical document based on the novelty criterion to determine the likelihood of obtaining a patent. It evaluates the content of a research report based on the inventive step criterion to determine the likelihood of obtaining a utility model. It evaluates the content of a meeting record based on the industrial applicability criterion to determine the value of the intellectual property. The Judgment Unit can also evaluate the value of intellectual property by considering patent information and market trends. For example, it can refer to patent databases, investigate patent information for similar technologies, and understand the trends of competitors. Furthermore, the Judgment Unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. The RAG system automatically evaluates the value of intellectual property and determines the priority of patent applications based on past patent information and market data. This allows the Judgment Unit to evaluate the potential of intellectual property with high accuracy and optimize the company's intellectual property strategy. Furthermore, based on the evaluation results, the decision-making unit can formulate a patent application strategy and make specific suggestions to strengthen the company's intellectual property portfolio. For example, it can determine the priority of patent applications in specific technological fields and optimize resource allocation. The decision-making unit can also conduct a risk assessment of patent applications and take measures to minimize those risks. In this way, the decision-making unit can effectively support the company's intellectual property strategy and maximize the value of its intellectual property.

[0074] The Proposal Department proposes applications to the Japan Patent Office (JPO) based on the decisions made by the Judgment Department. For example, the Proposal Department can propose the preparation of patent application documents. Specifically, it can propose methods for preparing patent application documents and explain how to submit them to the JPO. For example, it can propose methods for preparing patent application documents and explain how to submit them to the JPO. It can also propose methods for preparing utility model application documents and explain how to submit them to the JPO. Furthermore, it can propose application procedures to the JPO and explain how to prepare the necessary documents. The Proposal Department collects the information necessary for preparing patent application documents and prepares the documents in the appropriate format. The Proposal Department also supports the submission procedures to the JPO, providing deadlines and checklists of necessary documents. In addition, the Proposal Department can formulate patent application strategies and make specific suggestions to strengthen a company's intellectual property portfolio. For example, it can determine the priority of patent applications in specific technological fields and optimize resource allocation. The Proposal Department can also conduct risk assessments of patent applications and take measures to minimize risks. This allows the Proposal Department to effectively support a company's intellectual property strategy and maximize the value of its intellectual property. Furthermore, the proposal department can monitor the progress of patent applications and make suggestions for revisions or additions as needed. For example, it can suggest revisions to application documents or provide additional information based on the examination results from the patent office. In addition, the proposal department can analyze past application data and develop optimal application strategies to improve the success rate of patent applications. This allows the proposal department to continuously improve the company's intellectual property strategy and maximize the value of intellectual property.

[0075] The Intellectual Property Utilization Proposal Department proposes methods for utilizing intellectual property based on the results proposed by the Proposal Department. For example, the Department can propose methods for utilizing intellectual property based on a company's management strategy. Specifically, it can propose license agreements and explain the contract terms. For example, it can propose a license agreement for a specific technology and explain the contract terms and how royalties are set. The Department can also propose joint research and explain the research content and cooperation system. For example, it can propose joint research in a specific technological field and explain the research content, cooperation system, and how research results will be shared. Furthermore, the Department can propose product commercialization and explain the product development plan and how to bring the product to market. For example, it can propose a development plan for a new product based on a specific technology and explain the timing of market entry and marketing strategy. In this way, the Department can make concrete proposals to maximize the utilization of a company's intellectual property and improve its corporate value. Furthermore, the Department can collect feedback on the utilization of intellectual property and continuously improve the accuracy and effectiveness of its proposals. For example, it can monitor the implementation status of license agreements and the progress of joint research and revise the proposals as needed. Furthermore, the Intellectual Property Utilization Proposal Department can analyze market trends and competitor activities related to intellectual property utilization and formulate optimal utilization strategies. This allows the Department to make concrete proposals for effectively utilizing a company's intellectual property and maximizing corporate value. In addition, the Department can provide education and training on intellectual property utilization, improving intellectual property awareness within the company. For example, it can hold seminars and workshops on the importance of intellectual property and methods of utilization to raise employees' awareness. The Department can also share successful case studies related to intellectual property utilization, promoting its use throughout the company. This enables the Department to make concrete proposals for maximizing the utilization of a company's intellectual property and improving corporate value.

[0076] The data collection unit can collect data such as text, images, and audio from shared folders, emails, chats, etc. For example, the data collection unit can collect technical documents from shared folders. For example, the data collection unit can collect research reports from email attachments. For example, the data collection unit can collect meeting records from chat logs. For example, the data collection unit can collect PDF files from shared folders and save them to a database. For example, the data collection unit can collect JPEG images from email attachments and save them to an image database. For example, the data collection unit can collect MP3 audio files from chat logs and save them to an audio database. This allows for the collection of all kinds of internal materials, which can be used to uncover intellectual property. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input technical documents from shared folders into a generating AI and have the generating AI perform the collection of technical documents.

[0077] The analysis department can analyze the collected data in detail, understand the context, and interpret technical terms. For example, the analysis department can analyze text data using natural language processing technology. For example, the analysis department can interpret technical terms using machine learning models. For example, the analysis department can infer potential value using data mining technology. For example, the analysis department can analyze the content of technical documents using natural language processing technology and extract important keywords. For example, the analysis department can interpret technical terms in research reports and understand their meaning using machine learning models. For example, the analysis department can infer potential value from meeting records using data mining technology. This allows for a detailed analysis of the collected data and the exploration of intellectual property potential. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected text data into a generating AI and have the generating AI perform the analysis of the text data.

[0078] The judgment unit can determine the potential of intellectual property based on the analysis results. For example, the judgment unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. For example, the judgment unit can determine the potential of intellectual property based on criteria such as novelty, inventiveness, and industrial applicability. For example, the judgment unit can evaluate the value of intellectual property by considering patent information and market trends. For example, the judgment unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. For example, the judgment unit can evaluate the content of a technical document based on the novelty criterion and determine the likelihood of obtaining a patent. For example, the judgment unit can evaluate the content of a research report based on the inventiveness criterion and determine the likelihood of obtaining a utility model. For example, the judgment unit can evaluate the content of a meeting record based on the industrial applicability criterion and determine the value of intellectual property. This allows the judgment unit to determine the potential of intellectual property based on the analysis results and evaluate the likelihood of obtaining a patent or utility model. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the analysis results into a generating AI and have the generating AI perform the determination of the potential of intellectual property.

[0079] The proposal department can propose an application to the Japan Patent Office. The proposal department can, for example, propose the preparation of patent application documents. The proposal department can, for example, propose a method for preparing patent application documents. The proposal department can, for example, propose the preparation of utility model application documents. The proposal department can, for example, propose the application procedure to the Japan Patent Office. For example, the proposal department can propose a method for preparing patent application documents and explain how to submit them to the Japan Patent Office. For example, the proposal department can propose a method for preparing utility model application documents and explain how to submit them to the Japan Patent Office. The proposal department can, for example, propose an application procedure to the Japan Patent Office and explain how to prepare the necessary documents. This allows the proposal department to propose an application to the Japan Patent Office and support the acquisition of intellectual property rights. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the preparation of patent application documents into a generating AI and have the generating AI perform the preparation of patent application documents.

[0080] The Intellectual Property Utilization Proposal Department can propose ways to utilize intellectual property based on a company's business strategy. For example, the Department can propose license agreements. For example, the Department can propose joint research. For example, the Department can propose product development. For example, the Department can propose license agreements and explain the contract terms. For example, the Department can propose joint research and explain the research content and cooperation system. For example, the Department can propose product development and explain the product development plan and market launch method. In this way, it is possible to propose ways to utilize intellectual property based on a company's business strategy and maximize corporate value. Some or all of the above processes in the Intellectual Property Utilization Proposal Department may be performed using AI, for example, or not using AI. For example, the Department can input a company's business strategy into a generating AI and have the generating AI execute proposals for ways to utilize intellectual property.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect the data when the user is relaxed. For example, if the user is focused, the data collection unit can collect the data immediately to avoid disrupting the workflow. For example, if the user is tired, the data collection unit can adjust the timing of collection and collect the data while the user is taking a break. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit can determine the priority of data collection based on the importance of the data at the time of collection. For example, the data collection unit can prioritize the collection of highly important data and provide it quickly for subsequent analysis. For example, the data collection unit can postpone the collection of less important data and prioritize the collection of highly important data. For example, the data collection unit can evaluate the importance of the data in real time and dynamically adjust the collection priority. This allows the collection priority to be determined based on the importance of the data, and important data to be collected preferentially. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the importance of the data into a generating AI and have the generating AI perform the determination of the collection priority.

[0083] The data collection unit can apply different collection algorithms depending on the format of the data during collection. For example, the data collection unit can apply a collection algorithm using natural language processing to text data. For example, the data collection unit can apply a collection algorithm using image recognition technology to image data. For example, the data collection unit can apply a collection algorithm using speech recognition technology to audio data. For example, the data collection unit can perform data collection using natural language processing technology for text data. For example, the data collection unit can perform data collection using image recognition technology for image data. For example, the data collection unit can perform data collection using speech recognition technology for audio data. This makes it possible to apply an appropriate collection algorithm according to the format of the data and achieve efficient data collection. 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 format of the data into a generating AI and have the generating AI execute the application of the collection algorithm.

[0084] The data collection unit can estimate the user's emotions and determine the priority of the materials to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting less important materials to reduce the user's burden. For example, if the user is focused, the data collection unit will prioritize collecting highly important materials to improve work efficiency. For example, if the user is relaxed, the data collection unit will collect materials in a balanced manner to provide overall information. This allows for efficient data collection by determining the priority of materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The collection unit can perform data collection while considering the attribute information of the data creator. For example, if the data creator is an expert, the collection unit will prioritize collecting that data. For example, if the data creator is a newcomer, the collection unit will postpone collecting that data. For example, the collection unit will evaluate the data creator's past performance to determine the collection priority. For example, if the data creator is an expert, the collection unit will prioritize collecting that data and store it in the database. For example, if the data creator is a newcomer, the collection unit will postpone collecting that data and prioritize collecting high-importance data. For example, the collection unit will evaluate the data creator's past performance to determine the collection priority. This allows the collection to be performed while considering the attribute information of the data creator, and important data to be collected preferentially. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the attribute information of the data creator into a generating AI and have the generating AI determine the collection priority.

[0086] The data collection unit can adjust the collection order based on the relevance of the materials during collection. For example, the data collection unit can prioritize the collection of highly relevant materials to aid in subsequent analysis. For example, the data collection unit can postpone the collection of less relevant materials and prioritize the collection of important materials. For example, the data collection unit can evaluate the relevance of materials in real time and dynamically adjust the collection order. This allows for efficient data collection by adjusting the collection order based on the relevance of the materials. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the relevance of materials into a generating AI and have the generating AI perform the adjustment of the collection order.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. If the user is excited, the analysis unit provides analysis results using visually stimulating graphs and charts. This allows for efficient data analysis by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data to provide deep insights. For example, the analysis unit can perform a concise analysis on low-importance data to grasp the overall picture. For example, the analysis unit can evaluate the importance of the data in real time and dynamically adjust the level of detail of the analysis. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of the data during analysis. For example, the analysis unit can apply an analysis algorithm using natural language processing to text data. For example, the analysis unit can apply an analysis algorithm using image recognition technology to image data. For example, the analysis unit can apply an analysis algorithm using speech recognition technology to audio data. For example, the analysis unit can perform analysis on text data using natural language processing technology. For example, the analysis unit can perform analysis on image data using image recognition technology. For example, the analysis unit can perform analysis on audio data using speech recognition technology. This allows for the application of an appropriate analysis algorithm according to the category of the data, enabling efficient data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0090] 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 provides a short, concise analysis. If the user is relaxed, the analysis unit provides a longer analysis with detailed explanations. If the user is excited, the analysis unit provides an analysis with visually stimulating effects. This allows for efficient data analysis by adjusting the length of the analysis 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analysis based on the creation date of the data during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data to provide the latest information. For example, the analysis unit may postpone the analysis of older data and prioritize the analysis of the latest data. For example, the analysis unit may evaluate the creation date of the data in real time and dynamically adjust the analysis priority. This allows the analysis priority to be determined based on the creation date of the data and the latest information to be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the creation date of the data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data to aid in subsequent decision-making. For example, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. For example, the analysis unit can evaluate the relevance of data in real time and dynamically adjust the order of analysis. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The decision unit can estimate the user's emotions and adjust its decision criteria based on the estimated emotions. For example, if the user is relaxed, the decision unit will use detailed criteria to make a decision. For example, if the user is in a hurry, the decision unit will use concise criteria to make a quick decision. For example, if the user is excited, the decision unit will use visually stimulating criteria to make a decision. This allows for efficient decision-making by adjusting the decision criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The decision-making unit can improve the accuracy of its decisions by considering the interrelationships between materials. For example, the decision-making unit analyzes the interrelationships between materials and makes decisions based on highly relevant materials. For example, the decision-making unit evaluates the interrelationships between materials and improves the accuracy of its decisions. For example, the decision-making unit evaluates the interrelationships between materials in real time and dynamically adjusts the accuracy of its decisions. This allows for improved accuracy by considering the interrelationships between materials, thereby achieving accurate decisions. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the interrelationships between materials into a generating AI and have the generating AI perform the improvement of the accuracy of its decisions.

[0095] The decision-making unit can make decisions by considering the attribute information of the document creator. For example, if the document creator is an expert, the decision-making unit will give more weight to that document when making a decision. For example, if the document creator is a newcomer, the decision-making unit will only use that document as reference. For example, the decision-making unit will evaluate the document creator's past performance and adjust the criteria for judgment. For example, if the document creator is an expert, the decision-making unit will give more weight to that document when making a decision. For example, if the document creator is a newcomer, the decision-making unit will only use that document as reference. For example, the decision-making unit will evaluate the document creator's past performance and adjust the criteria for judgment. This allows the decision-making unit to make decisions by considering the attribute information of the document creator and to achieve accurate judgments. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the attribute information of the document creator into a generating AI and have the generating AI perform the adjustment of the criteria for judgment.

[0096] The decision unit can estimate the user's emotions and adjust the order in which the decision results are displayed based on the estimated user emotions. For example, if the user is relaxed, the decision unit may prioritize displaying detailed results. For example, if the user is in a hurry, the decision unit may prioritize displaying concise results. For example, if the user is excited, the decision unit may prioritize displaying visually stimulating results. This allows for efficient information delivery by adjusting the order in which the decision results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The decision-making unit can make decisions while considering the geographical distribution of the data. For example, the decision-making unit can analyze the geographical distribution of the data and make decisions while considering the characteristics of each region. For example, the decision-making unit can prioritize the evaluation of geographically close data to improve the accuracy of the decision. For example, the decision-making unit can evaluate the geographical distribution of the data in real time and dynamically adjust the criteria for judgment. This makes it possible to make decisions while considering the geographical distribution of the data and to achieve accurate judgments that reflect the characteristics of each region. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the adjustment of the criteria for judgment.

[0098] The decision-making unit can improve the accuracy of its judgment by referring to relevant literature related to the material at the time of judgment. For example, the decision-making unit can refer to relevant literature related to the material to improve the accuracy of its judgment. For example, the decision-making unit can evaluate the reliability of the material based on the relevant literature and make a judgment. For example, the decision-making unit can refer to relevant literature related to the material in real time and dynamically adjust the criteria for judgment. This makes it possible to improve the accuracy of judgment by referring to relevant literature related to the material and to achieve accurate judgment. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input relevant literature related to the material into a generating AI and have the generating AI perform the improvement of the accuracy of the judgment.

[0099] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit will provide suggestions using visually stimulating graphs and charts. This allows the suggestion unit to adjust the way it presents its suggestions according to the user's emotions, enabling efficient information delivery. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The proposal department can adjust the level of detail in a proposal based on the importance of the materials used. For example, the proposal department may include detailed explanations in proposals based on highly important materials, while keeping proposals based on less important materials concise. The proposal department may also evaluate the importance of materials in real time and dynamically adjust the level of detail in the proposal. This allows for efficient information provision by adjusting the level of detail in the proposal based on the importance of the materials. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department may input the importance of materials into a generating AI and have the generating AI adjust the level of detail in the proposal.

[0101] The proposal unit can apply different proposal algorithms depending on the category of the material when making a proposal. For example, the proposal unit can apply a proposal algorithm using natural language processing to text data. For example, the proposal unit can apply a proposal algorithm using image recognition technology to image data. For example, the proposal unit can apply a proposal algorithm using speech recognition technology to audio data. For example, the proposal unit makes proposals to text data using natural language processing technology. For example, the proposal unit makes proposals to image data using image recognition technology. For example, the proposal unit makes proposals to audio data using speech recognition technology. This makes it possible to apply an appropriate proposal algorithm according to the category of the material and realize efficient information provision. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the material into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0102] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, concise suggestion. If the user is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit will provide a suggestion with visually stimulating effects. This allows for efficient information delivery by adjusting the length of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The proposal department can determine the priority of proposals based on the creation date of the materials at the time of proposal submission. For example, the proposal department will prioritize proposals based on the latest materials. For example, the proposal department will postpone proposals based on older materials. For example, the proposal department can evaluate the creation date of the materials in real time and dynamically adjust the priority of proposals. This allows the department to determine the priority of proposals based on the creation date of the materials and provide the latest information. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the creation date of the materials into a generating AI and have the generating AI perform the determination of proposal priority.

[0104] The proposal department can adjust the order of proposals based on the relevance of the materials when submitting proposals. For example, the proposal department will prioritize proposals based on highly relevant materials. For example, the proposal department will postpone proposals based on less relevant materials. For example, the proposal department can evaluate the relevance of materials in real time and dynamically adjust the order of proposals. This allows for efficient information provision by adjusting the order of proposals based on the relevance of the materials. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the relevance of materials into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0105] The application suggestion unit can estimate the user's emotions and adjust the method of application suggestion based on the estimated user emotions. For example, if the user is relaxed, the application suggestion unit will provide detailed application suggestions. For example, if the user is in a hurry, the application suggestion unit will provide concise application suggestions that get straight to the point. For example, if the user is excited, the application suggestion unit will provide application suggestions using visually stimulating graphs and charts. This allows the application suggestion unit to adjust the method of application suggestion according to the user's emotions, thereby achieving efficient information delivery. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application suggestion unit may be performed using AI, for example, or without AI. For example, the application suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The utilization proposal department can analyze the past usage history of a document and select the optimal utilization method when making a utilization proposal. For example, the utilization proposal department can analyze the past usage history of a document and propose the most effective utilization method. For example, the utilization proposal department can propose utilization methods for similar documents based on past usage history. For example, the utilization proposal department can evaluate the past usage history of a document in real time and dynamically select the optimal utilization method. This enables efficient information provision by analyzing the past usage history of a document and selecting the optimal utilization method. Some or all of the above processing in the utilization proposal department may be performed using AI, for example, or without AI. For example, the utilization proposal department can input the past usage history of a document into a generating AI and have the generating AI select the optimal utilization method.

[0107] The utilization proposal unit can customize the means of utilization proposals based on the current market trends of the material when making a proposal. For example, the utilization proposal unit can analyze current market trends and propose the most effective means of utilization. For example, the utilization proposal unit can propose means of utilization that maximize the value of the material based on market trends. For example, the utilization proposal unit can evaluate the current market trends of the material in real time and dynamically customize the means of utilization proposals. This makes it possible to customize the means of utilization proposals based on the current market trends of the material and realize efficient information provision. Some or all of the above processing in the utilization proposal unit may be performed using AI, for example, or without AI. For example, the utilization proposal unit can input the market trends of the material into a generating AI and have the generating AI perform the customization of the means of utilization proposals.

[0108] The application suggestion unit can estimate the user's emotions and determine the priority of application suggestions based on the estimated emotions. For example, if the user is relaxed, the application suggestion unit will prioritize providing detailed application suggestions. If the user is in a hurry, for example, the application suggestion unit will prioritize providing concise application suggestions that get straight to the point. If the user is excited, for example, the application suggestion unit will prioritize providing visually stimulating application suggestions. This allows for efficient information delivery by determining the priority of application suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application suggestion unit may be performed using AI, for example, or without AI. For example, the application suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The utilization proposal unit can select the optimal utilization method when proposing utilization, taking into account the geographical distribution of the materials. For example, the utilization proposal unit can analyze the geographical distribution of the materials and propose the optimal utilization method considering the characteristics of each region. For example, the utilization proposal unit can prioritize the evaluation of geographically close materials and select the optimal utilization method. For example, the utilization proposal unit can evaluate the geographical distribution of the materials in real time and dynamically adjust the means of utilization proposal. This makes it possible to select the optimal utilization method considering the geographical distribution of the materials and realize efficient information provision. Some or all of the above processing in the utilization proposal unit may be performed using AI, for example, or without AI. For example, the utilization proposal unit can input the geographical distribution of the materials into a generating AI and have the generating AI select the optimal utilization method.

[0110] The utilization proposal unit can improve the accuracy of its utilization proposals by referring to related literature for the materials when making a proposal. For example, the utilization proposal unit can refer to related literature for the materials to improve the accuracy of its utilization proposals. For example, the utilization proposal unit can evaluate the reliability of the materials based on related literature and make utilization proposals. For example, the utilization proposal unit can refer to related literature for the materials in real time and dynamically adjust the criteria for utilization proposals. This makes it possible to improve the accuracy of utilization proposals by referring to related literature for the materials and to achieve efficient information provision. Some or all of the above processing in the utilization proposal unit may be performed using AI, for example, or without AI. For example, the utilization proposal unit can input related literature for the materials into a generating AI and have the generating AI perform the task of improving the accuracy of utilization proposals.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The intellectual property mining system can further estimate the user's emotions and adjust the data collection method based on those emotions. For example, if the user is stressed, the collection unit can delay the collection timing and collect data when the user is relaxed. Conversely, if the user is focused, it can collect data immediately to avoid disrupting the workflow. Furthermore, if the user is tired, the collection timing can be adjusted to collect data while the user is taking a break. This allows for efficient data collection by adjusting the data collection method according to the user's emotions.

[0113] The intellectual property mining system can prioritize data collection based on the importance of the data in its collection unit. For example, it can prioritize the collection of highly important data and provide it quickly for subsequent analysis. Alternatively, it can postpone the collection of less important data and prioritize the collection of highly important data. Furthermore, it can evaluate the importance of data in real time and dynamically adjust the collection priority. This allows for the determination of collection priorities based on the importance of the data, ensuring that important data is collected preferentially.

[0114] The intellectual property mining system can estimate the user's emotions in its analysis department and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide analysis results using visually stimulating graphs and charts. This allows the system to adjust the presentation of the analysis according to the user's emotions, enabling efficient data analysis.

[0115] The intellectual property mining system can improve the accuracy of its judgments by considering the interrelationships between data in its decision-making unit. For example, it can analyze the interrelationships between data and make decisions based on highly relevant data. It can also evaluate the interrelationships between data and improve the accuracy of its judgments. Furthermore, it can evaluate the interrelationships between data in real time and dynamically adjust the accuracy of its judgments. This allows for improved judgment accuracy by considering the interrelationships between data, thereby achieving accurate decisions.

[0116] The intellectual property mining system can estimate the user's emotions in the proposal section and adjust the presentation of the proposal based on those emotions. For example, if the user is relaxed, a detailed proposal can be provided. If the user is in a hurry, a concise proposal focusing on the key points can be provided. Furthermore, if the user is excited, a proposal using visually stimulating graphs and charts can be provided. This allows the system to adjust the presentation of proposals according to the user's emotions, enabling efficient information delivery.

[0117] The intellectual property mining system allows the utilization proposal department to analyze the past usage history of materials and select the optimal utilization method. For example, it can analyze the past usage history of a material and propose the most effective utilization method. It can also propose utilization methods for similar materials based on past usage history. Furthermore, it can evaluate the past usage history of a material in real time and dynamically select the optimal utilization method. This enables efficient information provision by analyzing the past usage history of materials and selecting the optimal utilization method.

[0118] The intellectual property mining system can apply different collection algorithms to the data in its collection unit depending on the format of the data. For example, a collection algorithm using natural language processing can be applied to text data. Similarly, a collection algorithm using image recognition technology can be applied to image data. Furthermore, a collection algorithm using speech recognition technology can be applied to audio data. This allows for the application of an appropriate collection algorithm according to the data format, enabling efficient data collection.

[0119] The intellectual property mining system allows the analysis department to apply different analysis algorithms depending on the category of the data. For example, natural language processing can be applied to text data. Image data can be analyzed using image recognition technology. Furthermore, audio data can be analyzed using speech recognition technology. This enables the application of appropriate analysis algorithms according to the category of data, resulting in efficient data analysis.

[0120] The intellectual property mining system can estimate the user's emotions in its decision-making unit and adjust the decision criteria based on those emotions. For example, if the user is relaxed, detailed criteria can be used for the decision. If the user is in a hurry, concise criteria can be used for a quick decision. Furthermore, if the user is excited, visually stimulating criteria can be used for the decision. This allows the system to adjust the decision criteria according to the user's emotions, enabling efficient decision-making.

[0121] The intellectual property mining system allows the proposal department to prioritize proposals based on the creation date of the documents. For example, proposals based on the most recent documents can be given priority, while proposals based on older documents can be postponed. Furthermore, the system can evaluate the creation date of documents in real time and dynamically adjust the proposal priority. This allows for the prioritization of proposals based on the creation date of the documents and the provision of the latest information.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects internal company materials. The data collection unit can collect data such as text, images, and audio from sources such as shared folders, emails, and chats. For example, the data collection unit can collect technical documents from shared folders. The data collection unit can also collect research reports from email attachments. Furthermore, the data collection unit can collect meeting records from chat logs. For example, the data collection unit can collect PDF files from shared folders and save them to a database. It can collect JPEG images from email attachments and save them to an image database. It can collect MP3 audio files from chat logs and save them to an audio database. Step 2: The analysis department analyzes the materials collected by the collection department. The analysis department can, for example, analyze the collected data in detail to understand the context and interpret technical terms. The analysis department can, for example, analyze text data using natural language processing techniques. The analysis department can also interpret technical terms using machine learning models. Furthermore, the analysis department can infer potential value using data mining techniques. For example, the analysis department can analyze the content of technical documents using natural language processing techniques and extract important keywords. It can interpret technical terms in research reports and understand their meaning using machine learning models. It can infer potential value from meeting records using data mining techniques. Step 3: The Judgment Unit determines the potential of intellectual property based on the results of the analysis conducted by the Analysis Unit. For example, the Judgment Unit can evaluate the likelihood of obtaining a patent or utility model based on the analysis results. The Judgment Unit determines the potential of intellectual property based on criteria such as novelty, inventiveness, and industrial applicability. The Judgment Unit can also evaluate the value of intellectual property by considering patent information and market trends. Furthermore, the Judgment Unit can identify and evaluate intellectual property using a RAG system that integrates specialized knowledge. For example, the Judgment Unit evaluates the content of a technical document based on the novelty criterion and determines the likelihood of obtaining a patent. It evaluates the content of a research report based on the inventiveness criterion and determines the likelihood of obtaining a utility model. It evaluates the content of a meeting record based on the industrial applicability criterion and determines the value of the intellectual property. Step 4: The Proposal Department proposes an application to the Japan Patent Office based on the results determined by the Judgment Department. The Proposal Department may, for example, propose the preparation of patent application documents. The Proposal Department may, for example, propose a method for preparing patent application documents. The Proposal Department may also propose the preparation of utility model application documents. Furthermore, the Proposal Department may propose the application procedure to the Japan Patent Office. For example, the Proposal Department may propose a method for preparing patent application documents and explain how to submit them to the Japan Patent Office. It may propose a method for preparing utility model application documents and explain how to submit them to the Japan Patent Office. It may propose the application procedure to the Japan Patent Office and explain how to prepare the necessary documents. Step 5: The Utilization Proposal Department proposes ways to utilize the intellectual property based on the results proposed by the Proposal Department. For example, the Utilization Proposal Department can propose ways to utilize the intellectual property based on the company's business strategy. For example, the Utilization Proposal Department can propose a license agreement. The Utilization Proposal Department can also propose joint research. Furthermore, the Utilization Proposal Department can also propose product development. For example, the Utilization Proposal Department can propose a license agreement and explain the contract terms. It can propose joint research and explain the research content and cooperation system. It can propose product development and explain the product development plan and method of market introduction.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the data collection unit, analysis unit, judgment unit, proposal unit, and utilization proposal unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects data from shared folders, emails, chats, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The judgment unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the feasibility of obtaining a patent or utility model. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes an application to the Japan Patent Office. The utilization proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a method for utilizing intellectual property. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, judgment unit, proposal unit, and utilization proposal unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from shared folders, emails, chats, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The judgment unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the feasibility of obtaining a patent or utility model. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes an application to the Japan Patent Office. The utilization proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a method for utilizing intellectual property. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, judgment unit, proposal unit, and utilization proposal unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from shared folders, emails, chats, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data in detail. The judgment unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the feasibility of obtaining a patent or utility model. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes an application to the Japan Patent Office. The utilization proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a method for utilizing intellectual property. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, judgment unit, proposal unit, and utilization proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data from shared folders, emails, chats, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in detail. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the feasibility of obtaining a patent or utility model. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes an application to the Japan Patent Office. The utilization proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a method for utilizing intellectual property. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The collection department is responsible for collecting internal company documents, An analysis unit analyzes the data collected by the aforementioned collection unit, A judgment unit that determines the potential for intellectual property based on the results of the analysis performed by the aforementioned analysis unit, Based on the results determined by the aforementioned determination unit, a proposal unit proposes an application to the Japan Patent Office, The system comprises a utilization proposal unit that proposes methods for utilizing intellectual property based on the results proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as text, images, and audio from shared folders, emails, chats, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed in detail to understand the context and interpret technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The unit that makes the determination said, Identify the potential of intellectual property based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, I propose filing an application with the Japan Patent Office. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned utilization proposal department, We propose methods for utilizing intellectual property based on a company's business strategy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting materials, prioritize collection based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the format of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the materials to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, consider the attribute information of the data creator. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During collection, adjust the order of collection based on the relevance of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is 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 is During the analysis, prioritize the analysis based on when the data was created. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The unit that makes the determination said, It estimates the user's emotions and adjusts the criteria for decision-making based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The unit that makes the determination said, When making a decision, consider the interrelationships between the materials to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 21) The unit that makes the determination said, When making a decision, the attribute information of the document's creator will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The unit that makes the determination said, It estimates the user's emotions and adjusts the order in which decisions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The unit that makes the determination said, When making a decision, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unit that makes the determination said, When making a decision, refer to related literature in the materials to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the document. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the materials were prepared. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned utilization proposal department, It estimates the user's emotions and adjusts the method of suggesting usage based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned utilization proposal department, When proposing a use for the materials, we analyze the past usage history of the materials to select the most suitable method of use. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned utilization proposal department, When proposing applications, customize the methods of the proposal based on the current market trends of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned utilization proposal department, It estimates user emotions and determines the priority of usage suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned utilization proposal department, When proposing utilization methods, the most suitable method of utilization will be selected considering the geographical distribution of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned utilization proposal department, When proposing applications, refer to related literature in the materials to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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. The collection department is responsible for collecting internal company documents, An analysis unit analyzes the data collected by the aforementioned collection unit, A judgment unit that determines the potential for intellectual property based on the results of the analysis performed by the aforementioned analysis unit, Based on the results determined by the aforementioned determination unit, a proposal unit proposes an application to the Japan Patent Office, The system comprises a utilization proposal unit that proposes methods for utilizing intellectual property based on the results proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as text, images, and audio from shared folders, emails, chats, etc. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed in detail to understand the context and interpret technical terms. The system according to feature 1.

4. The unit that makes the determination said, Identify the potential of intellectual property based on the analysis results. The system according to feature 1.

5. The aforementioned proposal section is, I propose filing an application with the Japan Patent Office. The system according to feature 1.

6. The aforementioned utilization proposal department, We propose methods for utilizing intellectual property based on a company's business strategy. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting materials, prioritize collection based on their importance. The system according to feature 1.

9. The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the format of the data. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of the materials to collect based on the estimated user emotions. The system according to feature 1.

Images