system

Abstract

The system according to this embodiment aims to generate new business ideas by effectively utilizing the company's intellectual assets. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a generation unit, an evaluation unit, and a proposal unit. The collection unit collects intellectual assets. The learning unit learns the intellectual assets collected by the collection unit. The generation unit generates business ideas based on the intellectual assets learned by the learning unit. The evaluation unit evaluates the business ideas generated by the generation unit. The proposal unit proposes the business ideas selected by the evaluation unit as a concrete business plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the effective utilization of in-house intellectual assets to create new business ideas has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize in-house intellectual assets to create new business ideas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a learning unit, a generation unit, an evaluation unit, and a proposal unit. The collection unit collects intellectual assets. The learning unit learns from the intellectual assets collected by the collection unit. The generation unit generates business ideas based on the intellectual assets learned by the learning unit. The evaluation unit evaluates the business ideas generated by the generation unit. The proposal unit proposes the business ideas selected by the evaluation unit as a concrete business plan. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize internal intellectual assets to generate new business ideas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The intellectual asset aggregation system according to an embodiment of the present invention is a mechanism that continuously generates new business ideas by aggregating and integrating intellectual assets (including the knowledge and experiential knowledge of individual employees) that are not yet recognized within the company using AI. The intellectual asset aggregation system trains the AI ​​with the intellectual assets of the entire company and all employees. Intellectual assets refer to intangible assets such as human resources, technology, organizational strength, customer networks, and brands that are the source of a company's competitiveness. For example, this includes knowledge acquired by employees in their hobbies and work experience. Next, the AI ​​generates new business ideas based on the learned intellectual assets. For example, by combining A's drone piloting skills, B's English ability, and C's network with a specific company, a new business idea is created. Furthermore, the AI ​​evaluates the generated business ideas and determines their feasibility and marketability. As a result, the most promising business idea is selected and a concrete business plan is proposed. This mechanism makes it possible to constantly make the most of the intellectual assets lying dormant within the company and generate an unlimited number of new business ideas. For example, by adding D's expertise in a specific field, an even more concrete and feasible business plan is created. In this way, AI learns from and integrates the company's intellectual assets, enabling it to continuously generate new business ideas that individuals might not have considered. This allows the intellectual asset aggregation system to maximize the use of the company's intellectual assets and generate an unlimited number of new business ideas.

[0029] The intellectual asset aggregation system according to this embodiment comprises a collection unit, a learning unit, a generation unit, an evaluation unit, and a proposal unit. The collection unit collects intellectual assets. Intellectual assets include, but are not limited to, patents, technical documents, research papers, and employee knowledge and experience. The collection unit can, for example, automatically collect employee self-reports and work histories. For example, the collection unit can collect employee knowledge and experience in the form of questionnaires. The collection unit can also automatically collect daily work reports and project progress reports. Furthermore, the collection unit can analyze employees' social media activities and collect relevant intellectual assets. For example, the collection unit collects relevant intellectual assets based on information shared by employees on social media. The learning unit learns the intellectual assets collected by the collection unit. The learning unit learns intellectual assets using, for example, machine learning algorithms. For example, the learning unit can learn intellectual assets using deep learning. The learning unit can also learn intellectual assets using natural language processing technology. Furthermore, the learning unit can apply different learning algorithms to each category of intellectual assets. For example, the learning unit applies a technology-specific learning algorithm to technical intellectual assets and an experience-specific learning algorithm to experiential knowledge. The generation unit generates business ideas based on the intellectual assets learned by the learning unit. The generation unit generates new business ideas by combining the learned intellectual assets, for example. For example, the generation unit creates a new business idea by combining A's drone piloting skills, B's English ability, and C's network of contacts with a specific company. The generation unit can also adjust the level of detail of the generation based on the importance of the intellectual assets. For example, the generation unit generates detailed business ideas based on high-importance intellectual assets and concise business ideas based on low-importance intellectual assets. The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit determines the feasibility and marketability of the generated business ideas, for example. For example, the evaluation unit evaluates the feasibility of the business idea by conducting technical feasibility and cost evaluations. The evaluation unit can also evaluate the marketability of the business idea based on market research results and competitive analysis.Furthermore, the evaluation department can weight the evaluation based on when the intellectual assets were submitted. For example, the evaluation department can give higher weight to recently submitted intellectual assets and lower weight to older intellectual assets. The proposal department proposes the business ideas selected by the evaluation department as concrete business plans. For example, the proposal department creates a business plan based on the business ideas selected by the evaluation department. For example, the proposal department proposes detailed specifications and marketing strategies for the business ideas. The proposal department can also apply different proposal methods to each category of intellectual assets. For example, the proposal department can apply a technology-specific proposal method to technology-based business plans and an experience-specific proposal method to business plans based on experiential knowledge. As a result, the intellectual asset aggregation system according to this embodiment can make maximum use of the company's intellectual assets and generate an unlimited number of new business ideas.

[0030] The collection department collects intellectual assets. These intellectual assets include, but are not limited to, patents, technical documents, research papers, and employee knowledge and experience. For example, the collection department can automatically collect employee self-reports and work histories. Specifically, it can collect employee knowledge and experience through questionnaires. These questionnaires are conducted via online forms or dedicated applications, where employees fill in details about their expertise and past project experience. The collection department can also automatically collect daily work reports and project progress reports. This includes mechanisms that integrate with project management tools and task management systems to automatically capture daily work content and progress. Furthermore, the collection department can analyze employees' social media activity and collect relevant intellectual assets. For example, it can collect relevant intellectual assets based on information shared by employees on social media. This includes a process that uses natural language processing technology to analyze posts and extract information related to specific keywords and topics. Additionally, the collection department can collect intellectual assets from external databases and publicly available information sources. For example, the latest technical information and research results can be obtained from patent databases and academic paper databases and integrated with the company's internal intellectual assets. This allows the data collection department to gather a wide range of intellectual assets from diverse sources, enriching the knowledge base of the entire system.

[0031] The learning unit learns from the intellectual assets collected by the collection unit. The learning unit learns from intellectual assets using, for example, machine learning algorithms. Specifically, the learning unit can learn from intellectual assets using deep learning. Deep learning models are trained using large amounts of data and automatically extract patterns and features of intellectual assets. The learning unit can also learn from intellectual assets using natural language processing (NLP) techniques. NLP is used to analyze and understand the meaning of text data, gaining a deeper understanding of the content of intellectual assets. For example, the learning unit analyzes the text of patent documents and research papers to extract important technical elements and research results. Furthermore, the learning unit can apply different learning algorithms to different categories of intellectual assets. For example, the learning unit can apply a technology-specific learning algorithm to technical intellectual assets and an experience-specific learning algorithm to experiential knowledge. Technology-specific learning algorithms are designed to understand technical details and terminology and to deeply learn technical knowledge. On the other hand, experience-specific learning algorithms are used to analyze past project experience and work history to learn practical knowledge and know-how. This allows the learning unit to efficiently and effectively learn from the collected intellectual assets, thereby strengthening the knowledge base of the entire system.

[0032] The generation unit generates business ideas based on the intellectual assets learned by the learning unit. For example, the generation unit generates new business ideas by combining learned intellectual assets. Specifically, the generation unit creates a new business idea by combining Person A's drone piloting skills, Person B's English ability, and Person C's network with a specific company. When combining these intellectual assets, the generation unit considers the interrelationships and complementarity of each intellectual asset. For example, combining drone piloting skills and English ability makes it possible to provide international drone-related services. Also, utilizing the network with a specific company increases the feasibility of the business idea. Furthermore, the generation unit can adjust the level of detail of the generation based on the importance of the intellectual assets. For example, the generation unit generates detailed business ideas based on high-importance intellectual assets and concise business ideas based on low-importance intellectual assets. This allows the generation unit to maximize the value of intellectual assets and create concrete and feasible business ideas. In addition, the generation unit can use AI to simulate business ideas and consider multiple scenarios. This allows the generation unit to identify the most effective and feasible business idea and provide it to the proposal unit.

[0033] The evaluation unit assesses the business ideas generated by the generation unit. For example, the evaluation unit determines the feasibility and marketability of the generated business ideas. Specifically, the evaluation unit assesses the feasibility of the business ideas by conducting technical feasibility and cost evaluations. Technical feasibility evaluations include expert opinions and technical verification. Cost evaluations include estimates of development and operating costs. Furthermore, the evaluation unit can also assess the marketability of business ideas based on market research results and competitive analysis. Market research results include the size and growth potential of the target market, as well as customer needs and trends. Competitive analysis includes an evaluation of competitors' products, services, and competitiveness. Additionally, the evaluation unit can weight the evaluation based on the timing of intellectual asset submission. For example, the evaluation unit can give higher weight to recently submitted intellectual assets and lower weight to older intellectual assets. This allows the evaluation unit to prioritize the latest information and conduct rapid and accurate evaluations. Furthermore, the evaluation unit can automate the evaluation process using AI to achieve efficient and consistent evaluations. This allows the evaluation department to accurately assess the feasibility and marketability of the generated business ideas and provide this information to the proposal department.

[0034] The proposal department proposes business plans based on the business ideas selected by the evaluation department. For example, the proposal department creates a business plan based on the business ideas selected by the evaluation department. Specifically, the proposal department proposes detailed specifications and marketing strategies for the business ideas. Specifications include detailed design and functions of the product or service, and a development schedule. Marketing strategies include target market selection, promotion plans, and sales channel development. The proposal department can also apply different proposal methods depending on the category of intellectual assets. For example, the proposal department applies a technology-focused proposal method to technology-based business plans and an experience-focused proposal method to business plans based on experiential knowledge. Technology-focused proposal methods include proposals that emphasize technical details and specialized knowledge. On the other hand, experience-focused proposal methods include proposals that leverage practical experience and know-how. Furthermore, the proposal department also proposes risk management plans and funding plans to enhance the feasibility of the business plans. Risk management plans include identifying potential risks and risk mitigation measures. Funding plans include estimating the necessary funds and funding methods. This allows the proposal department to propose concrete and feasible business plans, supporting the growth and development of companies.

[0035] The data collection unit can automatically collect employee self-reports and work history data. For example, the data collection unit can collect employee self-reports in the form of questionnaires. For example, the data collection unit can provide questionnaires for employees to conduct self-assessments and collect knowledge and experience. The data collection unit can also automatically collect work history data. For example, the data collection unit can automatically collect project progress reports and daily work reports. Furthermore, the data collection unit can analyze employees' social media activity and collect relevant intellectual assets. For example, the data collection unit can collect relevant intellectual assets based on information shared by employees on social media. This improves the efficiency of intellectual asset collection by automatically collecting employee self-reports and work history data. 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 employee self-report data into AI, which can analyze the data and extract intellectual assets.

[0036] The learning unit can learn collected intellectual assets using AI. For example, the learning unit can learn intellectual assets using deep learning. For example, the learning unit inputs collected intellectual assets into a deep learning model, and the model learns the intellectual assets. The learning unit can also learn intellectual assets using natural language processing technology. For example, the learning unit inputs collected text data into a natural language processing model, and the model analyzes the text data and learns. Furthermore, the learning unit can apply different learning algorithms to each category of intellectual assets. For example, the learning unit can apply a technology-specific learning algorithm to technology-related intellectual assets and an experience-specific learning algorithm to experiential knowledge. This improves the accuracy of learning by having the AI ​​learn the intellectual assets. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input collected intellectual assets into a generative AI, and the generative AI can learn the intellectual assets.

[0037] The generation unit can generate new business ideas based on learned intellectual assets. For example, the generation unit can generate new business ideas by combining learned intellectual assets. For example, the generation unit can create a new business idea by combining A's drone piloting skills, B's English ability, and C's network with a specific company. The generation unit can also adjust the level of detail of the generation based on the importance of the intellectual assets. For example, the generation unit can generate detailed business ideas based on highly important intellectual assets and concise business ideas based on less important intellectual assets. Furthermore, the generation unit can apply different generation algorithms depending on the category of intellectual assets. For example, the generation unit can apply a technology-specific generation algorithm to technology-related intellectual assets and an experience-specific generation algorithm to experiential knowledge. This makes it possible to create new business ideas by generating business ideas based on learned intellectual assets. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input learned intellectual assets into a generation AI, and the generation AI can generate new business ideas.

[0038] The evaluation unit can determine the feasibility and marketability of the generated business ideas. For example, the evaluation unit can perform technical feasibility and cost evaluations of the generated business ideas. For instance, the evaluation unit can evaluate the technical feasibility of a business idea and identify the necessary technologies and resources. The evaluation unit can also perform cost evaluations of business ideas and calculate the costs required for implementation. Furthermore, the evaluation unit can evaluate the marketability of a business idea based on market research results and competitive analysis. For example, the evaluation unit can evaluate the demand for a business idea based on market research results and evaluate the competitiveness of a business idea based on competitive analysis. By determining the feasibility and marketability of a business idea, promising business ideas can be selected. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the generated business ideas into a generative AI, which can then evaluate their feasibility and marketability.

[0039] The proposal department can propose business ideas selected by the evaluation department as concrete business plans. For example, the proposal department can create a business plan based on the business ideas selected by the evaluation department. For example, the proposal department can propose detailed specifications and marketing strategies for the business ideas. The proposal department can also apply different proposal methods depending on the category of intellectual assets. For example, the proposal department can apply a technology-specific proposal method to technology-based business plans and an experience-specific proposal method to business plans based on experiential knowledge. This allows for the proposal of concrete business plans based on selected business ideas, resulting in feasible business plans. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can input the business ideas selected by the evaluation department into a generative AI, which can then propose a concrete business plan.

[0040] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the unit can select an intellectual asset collection method for similar projects based on an employee's past successful project history. For example, the unit can analyze an employee's past unsuccessful project history and select a data collection method to avoid failure. The unit can also select a data collection method based on a specific skill set from an employee's work history. This allows the unit to select the optimal data collection method by analyzing past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee work history data into an AI, which can then analyze the data and select the optimal data collection method.

[0041] The collection unit can filter intellectual assets based on an employee's current projects and areas of interest during the collection process. For example, the collection unit can collect only intellectual assets related to the project the employee is currently working on. For example, the collection unit can prioritize the collection of relevant intellectual assets based on the employee's areas of interest. The collection unit can also filter and collect necessary intellectual assets according to the progress of the employee's current projects. This allows for the collection of highly relevant intellectual assets by filtering based on current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input employee project data and area of ​​interest data into an AI, which can then analyze and filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant intellectual assets based on employees' geographical location information. For example, if an employee is on a business trip, the data collection unit will prioritize the collection of intellectual assets related to the destination. For example, if an employee is working in a specific region, the data collection unit will prioritize the collection of intellectual assets related to that region. Furthermore, if an employee is working remotely, the data collection unit can also prioritize the collection of intellectual assets related to remote work. This improves collection efficiency by collecting highly relevant intellectual assets based on geographical location information. 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 employees' geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant intellectual assets.

[0043] The collection unit can analyze employees' social media activities and collect relevant intellectual assets when collecting them. For example, the collection unit can collect relevant intellectual assets based on information shared by employees on social media. For example, the collection unit can collect relevant intellectual assets based on information about accounts that employees follow on social media. The collection unit can also collect relevant intellectual assets based on information about groups that employees participate in on social media. This allows for the efficient collection of relevant intellectual assets by analyzing social media activities. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input employee social media data into AI, which can then analyze the data and collect relevant intellectual assets.

[0044] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can analyze the data and optimize the learning algorithm.

[0045] The learning unit can apply different learning algorithms to each category of intellectual asset during the learning process. For example, the learning unit can apply a technology-specific learning algorithm to technical intellectual assets. For example, it can apply an experience-specific learning algorithm to experiential knowledge. Furthermore, the learning unit can also apply a network-specific learning algorithm to network information. By applying different learning algorithms to each category, the accuracy of learning is improved. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input different learning algorithms for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0046] The learning unit can weight the training data based on the submission date of the intellectual assets during training. For example, the learning unit can assign higher weights to recently submitted intellectual assets during training, and lower weights to older intellectual assets during training. The learning unit can also dynamically adjust the weights of intellectual assets according to their submission date during training. This improves the accuracy of training by weighting the training data based on the submission date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission date data of intellectual assets into an AI, which can then analyze the data and assign weights.

[0047] The learning unit can improve the accuracy of its learning by referring to relevant literature on intellectual assets during the learning process. For example, the learning unit can learn by referring to the latest research papers related to intellectual assets. For example, the learning unit can learn by referring to patent documents related to intellectual assets. The learning unit can also learn by referring to industry reports related to intellectual assets. This improves the accuracy of learning by referring to relevant literature. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input relevant literature data on intellectual assets into an AI, which can then analyze the data to improve the accuracy of learning.

[0048] The generation unit can adjust the level of detail generated based on the importance of the intellectual assets when generating business ideas. For example, the generation unit can generate detailed business ideas based on highly important intellectual assets. For example, the generation unit can generate concise business ideas based on less important intellectual assets. The generation unit can also dynamically adjust the level of detail generated according to the importance of the intellectual assets. This allows for the generation of more effective business ideas by adjusting the level of detail based on the importance of the intellectual assets. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input intellectual asset importance data into AI, and the AI ​​can analyze the data to adjust the level of detail generated.

[0049] The generation unit can apply different generation algorithms depending on the category of intellectual asset when generating business ideas. For example, the generation unit can apply a technology-specific generation algorithm to technology-related intellectual assets. For example, it can apply an experience-specific generation algorithm to experiential knowledge. Furthermore, the generation unit can also apply a network-specific generation algorithm to network information. By applying a generation algorithm according to the category, more appropriate business ideas can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input different generation algorithms for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0050] The generation unit can determine the generation priority based on the submission timing of intellectual assets when generating business ideas. For example, the generation unit may prioritize generating business ideas based on recently submitted intellectual assets. For example, it may postpone generating business ideas based on older intellectual assets. The generation unit can also dynamically adjust the generation priority according to the submission timing. This allows for the generation of more effective business ideas by determining the generation priority based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input intellectual asset submission timing data into AI, which can then analyze the data to determine the generation priority.

[0051] The generation unit can adjust the order of business idea generation based on the relevance of intellectual assets. For example, the generation unit may prioritize generating business ideas based on highly relevant intellectual assets. For example, it may postpone generating business ideas based on less relevant intellectual assets. The generation unit can also dynamically adjust the order of generation according to the relevance of intellectual assets. This allows for the generation of more effective business ideas by adjusting the order of generation based on relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input intellectual asset relevance data into an AI, which can then analyze the data and adjust the order of generation.

[0052] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating business ideas. For example, the evaluation unit can select the optimal evaluation algorithm based on past evaluation data. For example, the evaluation unit can analyze past evaluation data and adjust the parameters of the evaluation algorithm. The evaluation unit can also improve the accuracy of the evaluation algorithm by referring to past evaluation data. In this way, the accuracy of the evaluation algorithm is improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation data into AI, and the AI ​​can analyze the data and optimize the evaluation algorithm.

[0053] The evaluation unit can apply different evaluation methods to each category of intellectual asset when evaluating business ideas. For example, the evaluation unit can apply a technology-specific evaluation method to technology-based business ideas. For example, the evaluation unit can apply an experience-specific evaluation method to business ideas based on experiential knowledge. Furthermore, the evaluation unit can also apply a network-specific evaluation method to business ideas based on personal network information. By applying different evaluation methods to each category, a more appropriate evaluation becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input different evaluation methods for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0054] The evaluation unit can weight business ideas based on the submission date of intellectual assets. For example, the evaluation unit may assign a higher weight to recently submitted intellectual assets and a lower weight to older intellectual assets. The evaluation unit can also dynamically adjust the weights of intellectual assets according to their submission date. This allows for a more appropriate evaluation by weighting the evaluation based on the submission date. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input intellectual asset submission date data into an AI, which can then analyze the data and assign evaluation weights.

[0055] The evaluation unit can perform business idea evaluations by referring to relevant market data on intellectual assets. For example, the evaluation unit can evaluate the marketability of a business idea based on market data related to intellectual assets. For example, the evaluation unit can evaluate the competitiveness of a business idea by referring to competitive data related to intellectual assets. The evaluation unit can also evaluate the demand for a business idea based on consumer data related to intellectual assets. This allows for a more appropriate evaluation by referring to relevant market data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant market data on intellectual assets into an AI, which can then analyze the data and perform the evaluation.

[0056] The proposal department can optimize its proposal algorithm by referring to past proposal data when proposing a business plan. For example, the proposal department can select the optimal proposal algorithm based on past proposal data. For example, the proposal department can analyze past proposal data and adjust the parameters of the proposal algorithm. The proposal department can also improve the accuracy of the proposal algorithm by referring to past proposal data. In this way, the accuracy of the proposal algorithm is improved by referring to past proposal data. Some or all of the above processes in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input past proposal data into AI, and the AI ​​can analyze the data and optimize the proposal algorithm.

[0057] The proposal department can apply different proposal methods to each category of intellectual asset when proposing a business plan. For example, the proposal department can apply a technology-specific proposal method to a technology-based business plan. For example, it can apply an experience-specific proposal method to a business plan based on experiential knowledge. Furthermore, the proposal department can also apply a network-specific proposal method to a business plan based on personal network information. By applying different proposal methods to each category, it is possible to propose more appropriate business plans. 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 different proposal methods for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0058] The proposal department can weight business plan proposals based on the submission date of the intellectual assets. For example, the proposal department may prioritize business plans based on recently submitted intellectual assets, or postpone proposals based on older intellectual assets. The proposal department can also dynamically adjust the weighting of proposals according to their submission date. This allows for the proposal of more appropriate business plans by weighting proposals based on their submission date. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input intellectual asset submission date data into an AI, which can then analyze the data and weight the proposals.

[0059] The proposal department can improve the accuracy of its proposals by referring to relevant literature on intellectual assets when proposing business plans. For example, the proposal department may refer to the latest research papers related to intellectual assets. For example, the proposal department may refer to patent documents related to intellectual assets. The proposal department may also refer to industry reports related to intellectual assets. This improves the accuracy of the proposals by referring to relevant literature. 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 relevant literature data on intellectual assets into an AI, which can then analyze the data to improve the accuracy of the proposals.

[0060] The proposal department can improve the accuracy of its business plan proposals by referring to relevant market data on intellectual assets. For example, the proposal department can evaluate the marketability of a business plan based on market data related to intellectual assets. For example, the proposal department can evaluate the competitiveness of a business plan by referring to competitive data related to intellectual assets. The proposal department can also evaluate the demand for a business plan based on consumer data related to intellectual assets. In this way, the accuracy of the proposal is improved by referring to relevant market data. 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 relevant market data on intellectual assets into an AI, and the AI ​​can analyze the data to improve the accuracy of the proposal.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The intellectual asset aggregation system may further include a feedback unit. The feedback unit collects employee feedback on proposed business plans and provides it to the evaluation unit. For example, the feedback unit can provide an interface for employees to comment on and evaluate proposed business plans. The feedback unit can also analyze employee feedback and provide a summary of the feedback to the evaluation unit. This allows for the selection of more practical and promising business plans by evaluating them based on employee feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input employee feedback data into an AI, which can then analyze the data and provide a summary of the feedback.

[0063] The intellectual asset aggregation system may further include an incentive department. The incentive department provides rewards and benefits to employees for providing intellectual assets and proposing business ideas. For example, the incentive department could award points to employees who provide intellectual assets, which can then be exchanged for goods or services. The incentive department could also offer recognition and promotion opportunities to employees who propose excellent business ideas. This can increase employee motivation and encourage the provision of intellectual assets and the proposal of business ideas. Some or all of the above processes in the incentive department may be performed using AI, for example, or not. For example, the incentive department could input employee contribution data into an AI, which could then analyze the data to determine rewards and benefits.

[0064] The intellectual asset aggregation system may further include a training department. The training department provides training programs aimed at improving employees' skills. For example, the training department can create and provide individualized training plans based on employees' intellectual assets. The training department can also adjust the training content according to the employees' skill levels. Furthermore, the training department can monitor the progress of training and provide feedback as needed. This can improve employees' skills and enhance the quality of intellectual assets. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input employee skill data into an AI, which can then analyze the data and create a training plan.

[0065] The intellectual asset aggregation system may also include a networking section. The networking section facilitates interaction among employees and supports the sharing of intellectual assets. For example, the networking section can provide a platform for employees to connect with other employees who share common interests or areas of expertise. The networking section can also hold regular networking events and workshops to promote the sharing of knowledge and experience among employees. Furthermore, the networking section can analyze employees' network data and perform optimal matching. This promotes the sharing of intellectual assets through interaction among employees and supports the creation of new business ideas. Some or all of the above processes in the networking section may be performed using AI, for example, or not using AI. For example, the networking section can input employee network data into AI, which can analyze the data and perform optimal matching.

[0066] The intellectual asset aggregation system may further include a performance evaluation unit. The performance evaluation unit assesses employees' work performance and measures their contribution to providing intellectual assets and proposing business ideas. For example, the performance evaluation unit can evaluate employees based on their work results and project progress. It can also score contributions to providing intellectual assets and proposing business ideas, using this as a basis for compensation and benefits. Furthermore, the performance evaluation unit can provide employees with evaluation results as feedback, offering opportunities for self-improvement. This allows for the evaluation of employees' work performance and promotes the provision of intellectual assets and the proposal of business ideas. Some or all of the above-described processes in the performance evaluation unit may be performed using AI, for example, or not. For example, the performance evaluation unit can input employee work data into an AI, which can then analyze the data and perform the evaluation.

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

[0068] Step 1: The collection unit collects intellectual assets. Intellectual assets include patents, technical documents, research papers, and employee knowledge and experience. The collection unit automatically collects employee self-reports and work history. For example, it collects employee knowledge and experience through questionnaires and automatically collects daily work reports and project progress reports. It can also analyze employees' social media activity and collect relevant intellectual assets. Step 2: The learning unit learns from the intellectual assets collected by the collection unit. The learning unit learns from the intellectual assets using machine learning algorithms, deep learning, and natural language processing techniques. Different learning algorithms can also be applied to each category of intellectual assets. Step 3: The generation unit generates business ideas based on the intellectual assets learned by the learning unit. The generation unit combines the learned intellectual assets to generate new business ideas and adjusts the level of detail of the generation based on the importance of the intellectual assets. Step 4: The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit determines the feasibility and marketability of the business ideas and weights the evaluation based on the timing of intellectual property submission. Step 5: The proposal team proposes the business ideas selected by the evaluation team as concrete business plans. The proposal team proposes detailed specifications and marketing strategies for the business ideas, and applies different proposal methodologies for each category of intellectual property.

[0069] (Example of form 2) The intellectual asset aggregation system according to an embodiment of the present invention is a mechanism that continuously generates new business ideas by aggregating and integrating intellectual assets (including the knowledge and experiential knowledge of individual employees) that are not yet recognized within the company using AI. The intellectual asset aggregation system trains the AI ​​with the intellectual assets of the entire company and all employees. Intellectual assets refer to intangible assets such as human resources, technology, organizational strength, customer networks, and brands that are the source of a company's competitiveness. For example, this includes knowledge acquired by employees in their hobbies and work experience. Next, the AI ​​generates new business ideas based on the learned intellectual assets. For example, by combining A's drone piloting skills, B's English ability, and C's network with a specific company, a new business idea is created. Furthermore, the AI ​​evaluates the generated business ideas and determines their feasibility and marketability. As a result, the most promising business idea is selected and a concrete business plan is proposed. This mechanism makes it possible to constantly make the most of the intellectual assets lying dormant within the company and generate an unlimited number of new business ideas. For example, by adding D's expertise in a specific field, an even more concrete and feasible business plan is created. In this way, AI learns from and integrates the company's intellectual assets, enabling it to continuously generate new business ideas that individuals might not have considered. This allows the intellectual asset aggregation system to maximize the use of the company's intellectual assets and generate an unlimited number of new business ideas.

[0070] The intellectual asset aggregation system according to this embodiment comprises a collection unit, a learning unit, a generation unit, an evaluation unit, and a proposal unit. The collection unit collects intellectual assets. Intellectual assets include, but are not limited to, patents, technical documents, research papers, and employee knowledge and experience. The collection unit can, for example, automatically collect employee self-reports and work histories. For example, the collection unit can collect employee knowledge and experience in the form of questionnaires. The collection unit can also automatically collect daily work reports and project progress reports. Furthermore, the collection unit can analyze employees' social media activities and collect relevant intellectual assets. For example, the collection unit collects relevant intellectual assets based on information shared by employees on social media. The learning unit learns the intellectual assets collected by the collection unit. The learning unit learns intellectual assets using, for example, machine learning algorithms. For example, the learning unit can learn intellectual assets using deep learning. The learning unit can also learn intellectual assets using natural language processing technology. Furthermore, the learning unit can apply different learning algorithms to each category of intellectual assets. For example, the learning unit applies a technology-specific learning algorithm to technical intellectual assets and an experience-specific learning algorithm to experiential knowledge. The generation unit generates business ideas based on the intellectual assets learned by the learning unit. The generation unit generates new business ideas by combining the learned intellectual assets, for example. For example, the generation unit creates a new business idea by combining A's drone piloting skills, B's English ability, and C's network of contacts with a specific company. The generation unit can also adjust the level of detail of the generation based on the importance of the intellectual assets. For example, the generation unit generates detailed business ideas based on high-importance intellectual assets and concise business ideas based on low-importance intellectual assets. The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit determines the feasibility and marketability of the generated business ideas, for example. For example, the evaluation unit evaluates the feasibility of the business idea by conducting technical feasibility and cost evaluations. The evaluation unit can also evaluate the marketability of the business idea based on market research results and competitive analysis.Furthermore, the evaluation department can weight the evaluation based on when the intellectual assets were submitted. For example, the evaluation department can give higher weight to recently submitted intellectual assets and lower weight to older intellectual assets. The proposal department proposes the business ideas selected by the evaluation department as concrete business plans. For example, the proposal department creates a business plan based on the business ideas selected by the evaluation department. For example, the proposal department proposes detailed specifications and marketing strategies for the business ideas. The proposal department can also apply different proposal methods to each category of intellectual assets. For example, the proposal department can apply a technology-specific proposal method to technology-based business plans and an experience-specific proposal method to business plans based on experiential knowledge. As a result, the intellectual asset aggregation system according to this embodiment can make maximum use of the company's intellectual assets and generate an unlimited number of new business ideas.

[0071] The collection department collects intellectual assets. These intellectual assets include, but are not limited to, patents, technical documents, research papers, and employee knowledge and experience. For example, the collection department can automatically collect employee self-reports and work histories. Specifically, it can collect employee knowledge and experience through questionnaires. These questionnaires are conducted via online forms or dedicated applications, where employees fill in details about their expertise and past project experience. The collection department can also automatically collect daily work reports and project progress reports. This includes mechanisms that integrate with project management tools and task management systems to automatically capture daily work content and progress. Furthermore, the collection department can analyze employees' social media activity and collect relevant intellectual assets. For example, it can collect relevant intellectual assets based on information shared by employees on social media. This includes a process that uses natural language processing technology to analyze posts and extract information related to specific keywords and topics. Additionally, the collection department can collect intellectual assets from external databases and publicly available information sources. For example, the latest technical information and research results can be obtained from patent databases and academic paper databases and integrated with the company's internal intellectual assets. This allows the data collection department to gather a wide range of intellectual assets from diverse sources, enriching the knowledge base of the entire system.

[0072] The learning unit learns from the intellectual assets collected by the collection unit. The learning unit learns from intellectual assets using, for example, machine learning algorithms. Specifically, the learning unit can learn from intellectual assets using deep learning. Deep learning models are trained using large amounts of data and automatically extract patterns and features of intellectual assets. The learning unit can also learn from intellectual assets using natural language processing (NLP) techniques. NLP is used to analyze and understand the meaning of text data, gaining a deeper understanding of the content of intellectual assets. For example, the learning unit analyzes the text of patent documents and research papers to extract important technical elements and research results. Furthermore, the learning unit can apply different learning algorithms to different categories of intellectual assets. For example, the learning unit can apply a technology-specific learning algorithm to technical intellectual assets and an experience-specific learning algorithm to experiential knowledge. Technology-specific learning algorithms are designed to understand technical details and terminology and to deeply learn technical knowledge. On the other hand, experience-specific learning algorithms are used to analyze past project experience and work history to learn practical knowledge and know-how. This allows the learning unit to efficiently and effectively learn from the collected intellectual assets, thereby strengthening the knowledge base of the entire system.

[0073] The generation unit generates business ideas based on the intellectual assets learned by the learning unit. For example, the generation unit generates new business ideas by combining learned intellectual assets. Specifically, the generation unit creates a new business idea by combining Person A's drone piloting skills, Person B's English ability, and Person C's network with a specific company. When combining these intellectual assets, the generation unit considers the interrelationships and complementarity of each intellectual asset. For example, combining drone piloting skills and English ability makes it possible to provide international drone-related services. Also, utilizing the network with a specific company increases the feasibility of the business idea. Furthermore, the generation unit can adjust the level of detail of the generation based on the importance of the intellectual assets. For example, the generation unit generates detailed business ideas based on high-importance intellectual assets and concise business ideas based on low-importance intellectual assets. This allows the generation unit to maximize the value of intellectual assets and create concrete and feasible business ideas. In addition, the generation unit can use AI to simulate business ideas and consider multiple scenarios. This allows the generation unit to identify the most effective and feasible business idea and provide it to the proposal unit.

[0074] The evaluation unit assesses the business ideas generated by the generation unit. For example, the evaluation unit determines the feasibility and marketability of the generated business ideas. Specifically, the evaluation unit assesses the feasibility of the business ideas by conducting technical feasibility and cost evaluations. Technical feasibility evaluations include expert opinions and technical verification. Cost evaluations include estimates of development and operating costs. Furthermore, the evaluation unit can also assess the marketability of business ideas based on market research results and competitive analysis. Market research results include the size and growth potential of the target market, as well as customer needs and trends. Competitive analysis includes an evaluation of competitors' products, services, and competitiveness. Additionally, the evaluation unit can weight the evaluation based on the timing of intellectual asset submission. For example, the evaluation unit can give higher weight to recently submitted intellectual assets and lower weight to older intellectual assets. This allows the evaluation unit to prioritize the latest information and conduct rapid and accurate evaluations. Furthermore, the evaluation unit can automate the evaluation process using AI to achieve efficient and consistent evaluations. This allows the evaluation department to accurately assess the feasibility and marketability of the generated business ideas and provide this information to the proposal department.

[0075] The proposal department proposes business plans based on the business ideas selected by the evaluation department. For example, the proposal department creates a business plan based on the business ideas selected by the evaluation department. Specifically, the proposal department proposes detailed specifications and marketing strategies for the business ideas. Specifications include detailed design and functions of the product or service, and a development schedule. Marketing strategies include target market selection, promotion plans, and sales channel development. The proposal department can also apply different proposal methods depending on the category of intellectual assets. For example, the proposal department applies a technology-focused proposal method to technology-based business plans and an experience-focused proposal method to business plans based on experiential knowledge. Technology-focused proposal methods include proposals that emphasize technical details and specialized knowledge. On the other hand, experience-focused proposal methods include proposals that leverage practical experience and know-how. Furthermore, the proposal department also proposes risk management plans and funding plans to enhance the feasibility of the business plans. Risk management plans include identifying potential risks and risk mitigation measures. Funding plans include estimating the necessary funds and funding methods. This allows the proposal department to propose concrete and feasible business plans, supporting the growth and development of companies.

[0076] The data collection unit can automatically collect employee self-reports and work history data. For example, the data collection unit can collect employee self-reports in the form of questionnaires. For example, the data collection unit can provide questionnaires for employees to conduct self-assessments and collect knowledge and experience. The data collection unit can also automatically collect work history data. For example, the data collection unit can automatically collect project progress reports and daily work reports. Furthermore, the data collection unit can analyze employees' social media activity and collect relevant intellectual assets. For example, the data collection unit can collect relevant intellectual assets based on information shared by employees on social media. This improves the efficiency of intellectual asset collection by automatically collecting employee self-reports and work history data. 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 employee self-report data into AI, which can analyze the data and extract intellectual assets.

[0077] The learning unit can learn collected intellectual assets using AI. For example, the learning unit can learn intellectual assets using deep learning. For example, the learning unit inputs collected intellectual assets into a deep learning model, and the model learns the intellectual assets. The learning unit can also learn intellectual assets using natural language processing technology. For example, the learning unit inputs collected text data into a natural language processing model, and the model analyzes the text data and learns. Furthermore, the learning unit can apply different learning algorithms to each category of intellectual assets. For example, the learning unit can apply a technology-specific learning algorithm to technology-related intellectual assets and an experience-specific learning algorithm to experiential knowledge. This improves the accuracy of learning by having the AI ​​learn the intellectual assets. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input collected intellectual assets into a generative AI, and the generative AI can learn the intellectual assets.

[0078] The generation unit can generate new business ideas based on learned intellectual assets. For example, the generation unit can generate new business ideas by combining learned intellectual assets. For example, the generation unit can create a new business idea by combining A's drone piloting skills, B's English ability, and C's network with a specific company. The generation unit can also adjust the level of detail of the generation based on the importance of the intellectual assets. For example, the generation unit can generate detailed business ideas based on highly important intellectual assets and concise business ideas based on less important intellectual assets. Furthermore, the generation unit can apply different generation algorithms depending on the category of intellectual assets. For example, the generation unit can apply a technology-specific generation algorithm to technology-related intellectual assets and an experience-specific generation algorithm to experiential knowledge. This makes it possible to create new business ideas by generating business ideas based on learned intellectual assets. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input learned intellectual assets into a generation AI, and the generation AI can generate new business ideas.

[0079] The evaluation unit can determine the feasibility and marketability of the generated business ideas. For example, the evaluation unit can perform technical feasibility and cost evaluations of the generated business ideas. For instance, the evaluation unit can evaluate the technical feasibility of a business idea and identify the necessary technologies and resources. The evaluation unit can also perform cost evaluations of business ideas and calculate the costs required for implementation. Furthermore, the evaluation unit can evaluate the marketability of a business idea based on market research results and competitive analysis. For example, the evaluation unit can evaluate the demand for a business idea based on market research results and evaluate the competitiveness of a business idea based on competitive analysis. By determining the feasibility and marketability of a business idea, promising business ideas can be selected. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the generated business ideas into a generative AI, which can then evaluate their feasibility and marketability.

[0080] The proposal department can propose business ideas selected by the evaluation department as concrete business plans. For example, the proposal department can create a business plan based on the business ideas selected by the evaluation department. For example, the proposal department can propose detailed specifications and marketing strategies for the business ideas. The proposal department can also apply different proposal methods depending on the category of intellectual assets. For example, the proposal department can apply a technology-specific proposal method to technology-based business plans and an experience-specific proposal method to business plans based on experiential knowledge. This allows for the proposal of concrete business plans based on selected business ideas, resulting in feasible business plans. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can input the business ideas selected by the evaluation department into a generative AI, which can then propose a concrete business plan.

[0081] The data collection unit can estimate employees' emotions and adjust the timing of intellectual asset collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can delay collection to allow the employee to relax. For example, if an employee is relaxed, the data collection unit can immediately collect the intellectual assets. The data collection unit can also collect intellectual assets when an employee is focused. By adjusting the collection timing based on employees' emotions, intellectual assets can be collected at a more appropriate time. 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 employee emotion data into a generative AI, which can estimate the emotion and adjust the collection timing.

[0082] The data collection unit can analyze an employee's past work history and select the optimal data collection method. For example, the unit can select an intellectual asset collection method for similar projects based on an employee's past successful project history. For example, the unit can analyze an employee's past unsuccessful project history and select a data collection method to avoid failure. The unit can also select a data collection method based on a specific skill set from an employee's work history. This allows the unit to select the optimal data collection method by analyzing past work history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input employee work history data into an AI, which can then analyze the data and select the optimal data collection method.

[0083] The collection unit can filter intellectual assets based on an employee's current projects and areas of interest during the collection process. For example, the collection unit can collect only intellectual assets related to the project the employee is currently working on. For example, the collection unit can prioritize the collection of relevant intellectual assets based on the employee's areas of interest. The collection unit can also filter and collect necessary intellectual assets according to the progress of the employee's current projects. This allows for the collection of highly relevant intellectual assets by filtering based on current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input employee project data and area of ​​interest data into an AI, which can then analyze and filter the data.

[0084] The data collection unit can estimate employees' emotions and determine the priority of intellectual assets to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit will prioritize collecting intellectual assets that help reduce stress. For example, if an employee is relaxed, the data collection unit will prioritize collecting intellectual assets related to long-term projects. Also, if an employee is focused, the data collection unit can prioritize collecting intellectual assets directly related to their current work. This allows for more effective collection of intellectual assets by prioritizing them based on employees' 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 employee emotion data into a generative AI, which can estimate emotions and determine the priority of intellectual assets.

[0085] The data collection unit can prioritize the collection of highly relevant intellectual assets based on employees' geographical location information. For example, if an employee is on a business trip, the data collection unit will prioritize the collection of intellectual assets related to the destination. For example, if an employee is working in a specific region, the data collection unit will prioritize the collection of intellectual assets related to that region. Furthermore, if an employee is working remotely, the data collection unit can also prioritize the collection of intellectual assets related to remote work. This improves collection efficiency by collecting highly relevant intellectual assets based on geographical location information. 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 employees' geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant intellectual assets.

[0086] The collection unit can analyze employees' social media activities and collect relevant intellectual assets when collecting them. For example, the collection unit can collect relevant intellectual assets based on information shared by employees on social media. For example, the collection unit can collect relevant intellectual assets based on information about accounts that employees follow on social media. The collection unit can also collect relevant intellectual assets based on information about groups that employees participate in on social media. This allows for the efficient collection of relevant intellectual assets by analyzing social media activities. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input employee social media data into AI, which can then analyze the data and collect relevant intellectual assets.

[0087] The learning unit can estimate employees' emotions and select training data based on those estimated emotions. For example, if an employee is relaxed, the learning unit will select data that allows them to learn in a relaxed state. For example, if an employee is stressed, the learning unit will select data to reduce stress. The learning unit can also select data that helps maintain concentration if an employee is focused. By selecting training data based on employees' emotions, the efficiency of learning is improved. 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 learning unit may be performed using AI, or not using AI. For example, the learning unit can input employee emotion data into a generative AI, which can estimate emotions and select training data.

[0088] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can analyze the data and optimize the learning algorithm.

[0089] The learning unit can apply different learning algorithms to each category of intellectual asset during the learning process. For example, the learning unit can apply a technology-specific learning algorithm to technical intellectual assets. For example, it can apply an experience-specific learning algorithm to experiential knowledge. Furthermore, the learning unit can also apply a network-specific learning algorithm to network information. By applying different learning algorithms to each category, the accuracy of learning is improved. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input different learning algorithms for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0090] The learning unit can estimate an employee's emotions and adjust the learning frequency based on the estimated emotions. For example, if an employee is stressed, the learning unit can reduce the learning frequency to help them relax. For example, if an employee is relaxed, the learning unit can increase the learning frequency to help them learn more efficiently. The learning unit can also optimize the learning frequency to maintain concentration if an employee is focused. This improves learning efficiency by adjusting the learning frequency based on the employee'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 learning unit may be performed using AI or not using AI. For example, the learning unit can input employee emotion data into a generative AI, which can estimate emotions and adjust the learning frequency.

[0091] The learning unit can weight the training data based on the submission date of the intellectual assets during training. For example, the learning unit can assign higher weights to recently submitted intellectual assets during training, and lower weights to older intellectual assets during training. The learning unit can also dynamically adjust the weights of intellectual assets according to their submission date during training. This improves the accuracy of training by weighting the training data based on the submission date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission date data of intellectual assets into an AI, which can then analyze the data and assign weights.

[0092] The learning unit can improve the accuracy of its learning by referring to relevant literature on intellectual assets during the learning process. For example, the learning unit can learn by referring to the latest research papers related to intellectual assets. For example, the learning unit can learn by referring to patent documents related to intellectual assets. The learning unit can also learn by referring to industry reports related to intellectual assets. This improves the accuracy of learning by referring to relevant literature. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input relevant literature data on intellectual assets into an AI, which can then analyze the data to improve the accuracy of learning.

[0093] The generation unit can estimate employees' emotions and adjust the way business ideas are expressed based on those estimated emotions. For example, if an employee is relaxed, the generation unit can generate detailed business ideas. For example, if an employee is stressed, the generation unit can generate concise business ideas. The generation unit can also generate specific business ideas if an employee is focused. This allows for the generation of more appropriate business ideas by adjusting the expression of business ideas based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into a generation AI, which can estimate emotions and adjust the expression of business ideas.

[0094] The generation unit can adjust the level of detail generated based on the importance of the intellectual assets when generating business ideas. For example, the generation unit can generate detailed business ideas based on highly important intellectual assets. For example, the generation unit can generate concise business ideas based on less important intellectual assets. The generation unit can also dynamically adjust the level of detail generated according to the importance of the intellectual assets. This allows for the generation of more effective business ideas by adjusting the level of detail based on the importance of the intellectual assets. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input intellectual asset importance data into AI, and the AI ​​can analyze the data to adjust the level of detail generated.

[0095] The generation unit can apply different generation algorithms depending on the category of intellectual asset when generating business ideas. For example, the generation unit can apply a technology-specific generation algorithm to technology-related intellectual assets. For example, it can apply an experience-specific generation algorithm to experiential knowledge. Furthermore, the generation unit can also apply a network-specific generation algorithm to network information. By applying a generation algorithm according to the category, more appropriate business ideas can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input different generation algorithms for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0096] The generation unit can estimate employees' emotions and adjust the length of the business ideas it generates based on those emotions. For example, if an employee is relaxed, the generation unit will generate longer business ideas. For example, if an employee is stressed, the generation unit will generate shorter business ideas. The generation unit can also generate business ideas of an appropriate length if an employee is focused. By adjusting the length of business ideas based on employees' emotions, more appropriate business ideas can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input employee emotion data into a generation AI, which can estimate emotions and adjust the length of the business ideas.

[0097] The generation unit can determine the generation priority based on the submission timing of intellectual assets when generating business ideas. For example, the generation unit may prioritize generating business ideas based on recently submitted intellectual assets. For example, it may postpone generating business ideas based on older intellectual assets. The generation unit can also dynamically adjust the generation priority according to the submission timing. This allows for the generation of more effective business ideas by determining the generation priority based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input intellectual asset submission timing data into AI, which can then analyze the data to determine the generation priority.

[0098] The generation unit can adjust the order of business idea generation based on the relevance of intellectual assets. For example, the generation unit may prioritize generating business ideas based on highly relevant intellectual assets. For example, it may postpone generating business ideas based on less relevant intellectual assets. The generation unit can also dynamically adjust the order of generation according to the relevance of intellectual assets. This allows for the generation of more effective business ideas by adjusting the order of generation based on relevance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input intellectual asset relevance data into an AI, which can then analyze the data and adjust the order of generation.

[0099] The evaluation unit can estimate employees' emotions and adjust the evaluation criteria for business ideas based on the estimated emotions. For example, if an employee is relaxed, the evaluation unit can apply detailed evaluation criteria. For example, if an employee is stressed, the evaluation unit can apply concise evaluation criteria. The evaluation unit can also apply specific evaluation criteria if an employee is focused. This allows for more appropriate evaluations by adjusting the evaluation criteria based on employees' 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 evaluation unit may be performed using AI or not. For example, the evaluation unit can input employee emotion data into a generative AI, which can estimate emotions and adjust the evaluation criteria.

[0100] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating business ideas. For example, the evaluation unit can select the optimal evaluation algorithm based on past evaluation data. For example, the evaluation unit can analyze past evaluation data and adjust the parameters of the evaluation algorithm. The evaluation unit can also improve the accuracy of the evaluation algorithm by referring to past evaluation data. In this way, the accuracy of the evaluation algorithm is improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation data into AI, and the AI ​​can analyze the data and optimize the evaluation algorithm.

[0101] The evaluation unit can apply different evaluation methods to each category of intellectual asset when evaluating business ideas. For example, the evaluation unit can apply a technology-specific evaluation method to technology-based business ideas. For example, the evaluation unit can apply an experience-specific evaluation method to business ideas based on experiential knowledge. Furthermore, the evaluation unit can also apply a network-specific evaluation method to business ideas based on personal network information. By applying different evaluation methods to each category, a more appropriate evaluation becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input different evaluation methods for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0102] The evaluation unit can estimate an employee's emotions and adjust how the evaluation results are displayed based on the estimated emotions. For example, if an employee is relaxed, the evaluation unit can display detailed evaluation results. For example, if an employee is stressed, the evaluation unit can display concise evaluation results. The evaluation unit can also display specific evaluation results if an employee is focused. By adjusting how evaluation results are displayed based on an employee's emotions, more appropriate evaluation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input employee emotion data into a generative AI, which can estimate emotions and adjust how the evaluation results are displayed.

[0103] The evaluation unit can weight business ideas based on the submission date of intellectual assets. For example, the evaluation unit may assign a higher weight to recently submitted intellectual assets and a lower weight to older intellectual assets. The evaluation unit can also dynamically adjust the weights of intellectual assets according to their submission date. This allows for a more appropriate evaluation by weighting the evaluation based on the submission date. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input intellectual asset submission date data into an AI, which can then analyze the data and assign evaluation weights.

[0104] The evaluation unit can perform business idea evaluations by referring to relevant market data on intellectual assets. For example, the evaluation unit can evaluate the marketability of a business idea based on market data related to intellectual assets. For example, the evaluation unit can evaluate the competitiveness of a business idea by referring to competitive data related to intellectual assets. The evaluation unit can also evaluate the demand for a business idea based on consumer data related to intellectual assets. This allows for a more appropriate evaluation by referring to relevant market data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant market data on intellectual assets into an AI, which can then analyze the data and perform the evaluation.

[0105] The proposal department can estimate employees' emotions and adjust the way business plans are proposed based on those estimated emotions. For example, if an employee is relaxed, the proposal department might propose a detailed business plan. If an employee is stressed, it might propose a concise business plan. Furthermore, if an employee is focused, the proposal department might propose a specific business plan. This allows for the proposal of more appropriate business plans by adjusting the proposal method based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input employee emotion data into a generative AI, which can estimate emotions and adjust the way business plans are proposed.

[0106] The proposal department can optimize its proposal algorithm by referring to past proposal data when proposing a business plan. For example, the proposal department can select the optimal proposal algorithm based on past proposal data. For example, the proposal department can analyze past proposal data and adjust the parameters of the proposal algorithm. The proposal department can also improve the accuracy of the proposal algorithm by referring to past proposal data. In this way, the accuracy of the proposal algorithm is improved by referring to past proposal data. Some or all of the above processes in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input past proposal data into AI, and the AI ​​can analyze the data and optimize the proposal algorithm.

[0107] The proposal department can apply different proposal methods to each category of intellectual asset when proposing a business plan. For example, the proposal department can apply a technology-specific proposal method to a technology-based business plan. For example, it can apply an experience-specific proposal method to a business plan based on experiential knowledge. Furthermore, the proposal department can also apply a network-specific proposal method to a business plan based on personal network information. By applying different proposal methods to each category, it is possible to propose more appropriate business plans. 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 different proposal methods for each category of intellectual asset into the AI, and the AI ​​can analyze the data and apply them.

[0108] The proposal department can estimate employees' emotions and prioritize proposals based on those emotions. For example, if an employee is relaxed, the proposal department will prioritize proposing long-term business plans. For example, if an employee is stressed, the proposal department will prioritize proposing short-term business plans. Furthermore, if an employee is focused, the proposal department can also prioritize proposing business plans directly related to their current work. This allows for the proposal of more appropriate business plans by prioritizing proposals based on employees' 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 proposal department may be performed using AI or not. For example, the proposal department can input employee emotion data into a generative AI, which can estimate emotions and determine the priority of proposals.

[0109] The proposal department can weight business plan proposals based on the submission date of the intellectual assets. For example, the proposal department may prioritize business plans based on recently submitted intellectual assets, or postpone proposals based on older intellectual assets. The proposal department can also dynamically adjust the weighting of proposals according to their submission date. This allows for the proposal of more appropriate business plans by weighting proposals based on their submission date. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input intellectual asset submission date data into an AI, which can then analyze the data and weight the proposals.

[0110] The proposal department can improve the accuracy of its proposals by referring to relevant literature on intellectual assets when proposing business plans. For example, the proposal department may refer to the latest research papers related to intellectual assets. For example, the proposal department may refer to patent documents related to intellectual assets. The proposal department may also refer to industry reports related to intellectual assets. This improves the accuracy of the proposals by referring to relevant literature. 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 relevant literature data on intellectual assets into an AI, which can then analyze the data to improve the accuracy of the proposals.

[0111] The proposal department can improve the accuracy of its business plan proposals by referring to relevant market data on intellectual assets. For example, the proposal department can evaluate the marketability of a business plan based on market data related to intellectual assets. For example, the proposal department can evaluate the competitiveness of a business plan by referring to competitive data related to intellectual assets. The proposal department can also evaluate the demand for a business plan based on consumer data related to intellectual assets. In this way, the accuracy of the proposal is improved by referring to relevant market data. 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 relevant market data on intellectual assets into an AI, and the AI ​​can analyze the data to improve the accuracy of the proposal.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The intellectual asset aggregation system may further include a feedback unit. The feedback unit collects employee feedback on proposed business plans and provides it to the evaluation unit. For example, the feedback unit can provide an interface for employees to comment on and evaluate proposed business plans. The feedback unit can also analyze employee feedback and provide a summary of the feedback to the evaluation unit. This allows for the selection of more practical and promising business plans by evaluating them based on employee feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input employee feedback data into an AI, which can then analyze the data and provide a summary of the feedback.

[0114] The intellectual asset aggregation system may further include an incentive department. The incentive department provides rewards and benefits to employees for providing intellectual assets and proposing business ideas. For example, the incentive department could award points to employees who provide intellectual assets, which can then be exchanged for goods or services. The incentive department could also offer recognition and promotion opportunities to employees who propose excellent business ideas. This can increase employee motivation and encourage the provision of intellectual assets and the proposal of business ideas. Some or all of the above processes in the incentive department may be performed using AI, for example, or not. For example, the incentive department could input employee contribution data into an AI, which could then analyze the data to determine rewards and benefits.

[0115] The intellectual asset aggregation system may further include a training department. The training department provides training programs aimed at improving employees' skills. For example, the training department can create and provide individualized training plans based on employees' intellectual assets. The training department can also adjust the training content according to the employees' skill levels. Furthermore, the training department can monitor the progress of training and provide feedback as needed. This can improve employees' skills and enhance the quality of intellectual assets. Some or all of the above processes in the training department may be performed using AI, for example, or not. For example, the training department can input employee skill data into an AI, which can then analyze the data and create a training plan.

[0116] The intellectual asset aggregation system may also include a networking section. The networking section facilitates interaction among employees and supports the sharing of intellectual assets. For example, the networking section can provide a platform for employees to connect with other employees who share common interests or areas of expertise. The networking section can also hold regular networking events and workshops to promote the sharing of knowledge and experience among employees. Furthermore, the networking section can analyze employees' network data and perform optimal matching. This promotes the sharing of intellectual assets through interaction among employees and supports the creation of new business ideas. Some or all of the above processes in the networking section may be performed using AI, for example, or not using AI. For example, the networking section can input employee network data into AI, which can analyze the data and perform optimal matching.

[0117] The intellectual asset aggregation system may further include an emotion analysis unit. The emotion analysis unit monitors employees' emotions in real time and optimizes the timing of intellectual asset collection and learning. For example, the emotion analysis unit can estimate emotions by analyzing employees' facial expressions, voice, and text data. It can also adjust the timing of intellectual asset collection and learning based on the estimated emotions. Furthermore, the emotion analysis unit can accumulate employee emotion data and analyze long-term changes in emotions. This allows for the optimization of the timing of intellectual asset collection and learning based on employee emotions, supporting the efficient utilization of intellectual assets. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input employee emotion data into AI, which can analyze the data to estimate emotions and adjust the timing of collection and learning.

[0118] The intellectual asset aggregation system may further include a performance evaluation unit. The performance evaluation unit assesses employees' work performance and measures their contribution to providing intellectual assets and proposing business ideas. For example, the performance evaluation unit can evaluate employees based on their work results and project progress. It can also score contributions to providing intellectual assets and proposing business ideas, using this as a basis for compensation and benefits. Furthermore, the performance evaluation unit can provide employees with evaluation results as feedback, offering opportunities for self-improvement. This allows for the evaluation of employees' work performance and promotes the provision of intellectual assets and the proposal of business ideas. Some or all of the above-described processes in the performance evaluation unit may be performed using AI, for example, or not. For example, the performance evaluation unit can input employee work data into an AI, which can then analyze the data and perform the evaluation.

[0119] The intellectual asset aggregation system may further include an emotional feedback unit. The emotional feedback unit estimates the employee's emotions and provides feedback based on the estimated emotions. For example, if an employee is feeling stressed, the emotional feedback unit can provide advice on how to relax. It can also provide advice on how to improve concentration if the employee is relaxed. Furthermore, the emotional feedback unit can accumulate employee emotional data, analyze long-term emotional changes, and provide feedback. This allows for the provision of appropriate feedback based on employee emotions, thereby improving work efficiency. Some or all of the above-described processes in the emotional feedback unit may be performed using AI, for example, or without AI. For example, the emotional feedback unit can input employee emotional data into AI, which can then analyze the data and provide feedback.

[0120] The intellectual asset aggregation system may further include an emotion monitoring unit. The emotion monitoring unit monitors employees' emotions in real time and optimizes the timing of intellectual asset collection and learning. For example, the emotion monitoring unit can estimate emotions by analyzing employees' facial expressions, voice, and text data. It can also adjust the timing of intellectual asset collection and learning based on the estimated emotions. Furthermore, the emotion monitoring unit can accumulate employee emotion data and analyze long-term changes in emotions. This allows for the optimization of the timing of intellectual asset collection and learning based on employees' emotions, supporting the efficient utilization of intellectual assets. Some or all of the above processing in the emotion monitoring unit may be performed using AI, for example, or without AI. For example, the emotion monitoring unit can input employee emotion data into AI, which can analyze the data to estimate emotions and adjust the timing of collection and learning.

[0121] The intellectual asset aggregation system may further include an emotion prediction unit. This unit predicts future emotions based on employees' past emotional data and plans for the collection and learning of intellectual assets. For example, the emotion prediction unit can analyze employees' past emotional data and predict changes in emotions at specific times or situations. It can also plan the collection and learning of intellectual assets based on the predicted emotions. Furthermore, the emotion prediction unit can accumulate employee emotional data and predict long-term emotional changes. This allows for the optimization of intellectual asset collection and learning plans based on employee emotions, supporting the efficient utilization of intellectual assets. Some or all of the above-described processes in the emotion prediction unit may be performed using AI, for example, or without AI. For example, the emotion prediction unit can input employee emotional data into an AI, which analyzes the data to predict emotions and plan collection and learning.

[0122] The intellectual asset aggregation system may further include an emotion adjustment unit. The emotion adjustment unit monitors employees' emotions in real time and adjusts the work environment and tasks. For example, if an employee is feeling stressed, the emotion adjustment unit can adjust tasks to reduce their workload. It can also provide tasks to improve concentration if an employee is relaxed. Furthermore, the emotion adjustment unit can accumulate employee emotional data and analyze long-term emotional changes to improve the work environment. This allows for adjustments to the work environment and tasks based on employee emotions, thereby improving work efficiency. Some or all of the above-described processes in the emotion adjustment unit may be performed using AI, for example, or without AI. For example, the emotion adjustment unit can input employee emotional data into AI, which can analyze the data to estimate emotions and adjust the work environment and tasks accordingly.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects intellectual assets. Intellectual assets include patents, technical documents, research papers, and employee knowledge and experience. The collection unit automatically collects employee self-reports and work history. For example, it collects employee knowledge and experience through questionnaires and automatically collects daily work reports and project progress reports. It can also analyze employees' social media activity and collect relevant intellectual assets. Step 2: The learning unit learns from the intellectual assets collected by the collection unit. The learning unit learns from the intellectual assets using machine learning algorithms, deep learning, and natural language processing techniques. Different learning algorithms can also be applied to each category of intellectual assets. Step 3: The generation unit generates business ideas based on the intellectual assets learned by the learning unit. The generation unit combines the learned intellectual assets to generate new business ideas and adjusts the level of detail of the generation based on the importance of the intellectual assets. Step 4: The evaluation unit evaluates the business ideas generated by the generation unit. The evaluation unit determines the feasibility and marketability of the business ideas and weights the evaluation based on the timing of intellectual property submission. Step 5: The proposal team proposes the business ideas selected by the evaluation team as concrete business plans. The proposal team proposes detailed specifications and marketing strategies for the business ideas, and applies different proposal methodologies for each category of intellectual property.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, evaluation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee knowledge and experience using the control unit 46A of the smart device 14 and analyzes work history and social media activity using the specific processing unit 290 of the data processing unit 12. The learning unit learns intellectual assets using a machine learning algorithm using the specific processing unit 290 of the data processing unit 12. The generation unit generates business ideas based on the learned intellectual assets using the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the feasibility and marketability of the business ideas generated by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a concrete business plan based on the business ideas evaluated by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, evaluation unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects employees' knowledge and experience using the control unit 46A of the smart glasses 214 and analyzes their work history and social media activities using the specific processing unit 290 of the data processing unit 12. The learning unit learns intellectual assets using a machine learning algorithm using the specific processing unit 290 of the data processing unit 12. The generation unit generates business ideas based on the intellectual assets learned by the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the feasibility and marketability of the business ideas generated by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a concrete business plan based on the business ideas evaluated by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, evaluation unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employees' knowledge and experience using the control unit 46A of the headset terminal 314 and analyzes their work history and social media activities using the specific processing unit 290 of the data processing unit 12. The learning unit learns intellectual assets using a machine learning algorithm using the specific processing unit 290 of the data processing unit 12. The generation unit generates business ideas based on the learned intellectual assets using the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the feasibility and marketability of the business ideas generated by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a concrete business plan based on the business ideas evaluated by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, evaluation unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employees' knowledge and experience using the control unit 46A of the robot 414 and analyzes work history and social media activities using the specific processing unit 290 of the data processing unit 12. The learning unit learns intellectual assets using a machine learning algorithm using the specific processing unit 290 of the data processing unit 12. The generation unit generates business ideas based on the learned intellectual assets using the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the feasibility and marketability of the business ideas generated by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a concrete business plan based on the business ideas evaluated by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The collection department collects intellectual property, A learning unit that learns from the intellectual assets collected by the aforementioned collection unit, A generation unit that generates business ideas based on the intellectual assets learned by the learning unit, An evaluation unit that evaluates the business ideas generated by the generation unit, The system includes a proposal unit that proposes the business ideas selected by the evaluation unit as concrete business plans. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system automatically collects employee self-reports and work history data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Collected intellectual assets are used to learn from AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate new business ideas based on learned intellectual assets. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Determining the feasibility and marketability of the generated business ideas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The business ideas selected by the evaluation department will be proposed as concrete business plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of intellectual asset acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past work history and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting intellectual assets, filter them based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate employees' emotions and prioritize the intellectual assets to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting intellectual assets, the system prioritizes the collection of highly relevant intellectual assets based on employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting intellectual assets, analyze employees' social media activity and collect relevant intellectual assets. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, The system estimates the emotions of employees and selects training data based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, different learning algorithms are applied to each category of intellectual property. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, The system estimates employees' emotions and adjusts the frequency of learning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the training data is weighted based on when the intellectual assets were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During learning, refer to relevant literature on intellectual property to improve the accuracy of learning. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate employees' emotions and adjust the way business ideas are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating business ideas, adjust the level of detail based on the importance of the intellectual assets. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating business ideas, different generation algorithms are applied depending on the category of intellectual asset. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates employee sentiment and adjusts the length of the business ideas generated based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating business ideas, prioritize their creation based on the timing of intellectual property submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating business ideas, adjust the generation order based on the relevance of intellectual assets. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, We estimate employees' emotions and adjust the evaluation criteria for business ideas based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, When evaluating business ideas, we optimize the evaluation algorithm by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, When evaluating business ideas, different evaluation methods are applied to each category of intellectual asset. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, The system estimates employees' emotions and adjusts how evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, When evaluating business ideas, weight the evaluation based on the timing of intellectual asset submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit, When evaluating business ideas, the evaluation is conducted by referring to relevant market data on intellectual assets. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, We estimate employees' emotions and adjust the way we propose business plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When proposing a business plan, we optimize the proposal algorithm by referring to past proposal data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When proposing a business plan, apply different proposal methods to each category of intellectual asset. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, The system estimates employees' emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When proposing a business plan, weight the proposals based on the timing of intellectual property submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When proposing a business plan, refer to relevant intellectual property literature to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When proposing a business plan, referencing relevant market data on intellectual assets can improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 collects intellectual property, A learning unit that learns from the intellectual assets collected by the aforementioned collection unit, A generation unit that generates business ideas based on the intellectual assets learned by the learning unit, An evaluation unit that evaluates the business ideas generated by the generation unit, The system includes a proposal unit that proposes the business ideas selected by the evaluation unit as concrete business plans. A system characterized by the following features.

2. The aforementioned collection unit is The system automatically collects employee self-reports and work history data. The system according to feature 1.

3. The aforementioned learning unit, The collected intellectual assets are used to learn from AI. The system according to feature 1.

4. The generating unit is Generate new business ideas based on learned intellectual assets. The system according to feature 1.

5. The evaluation unit, Determining the feasibility and marketability of the generated business ideas. The system according to feature 1.

6. The aforementioned proposal section is, The business ideas selected by the aforementioned evaluation department will be proposed as concrete business plans. The system according to feature 1.

7. The aforementioned collection unit is We estimate employees' emotions and adjust the timing of intellectual asset acquisition based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze employees' past work history and select the most suitable data collection method. The system according to feature 1.

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