system

Abstract

The system according to this embodiment aims to create new products and services by integrating technologies held by small and medium-sized enterprises. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a test unit, a development unit, and a release unit. The analysis unit analyzes technical data. The proposal unit proposes a technology fusion scenario based on the data analyzed by the analysis unit. The test unit tests the market applicability of the scenario proposed by the proposal unit. The development unit develops a prototype based on the feedback data obtained by the test unit. The release unit releases the prototype developed by the development unit to expand the market.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for small and medium-sized enterprises to effectively integrate their own technologies and create new products and services.

[0005] The system according to the embodiment aims to integrate the technologies held by small and medium-sized enterprises and create new products and services.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an analysis unit, a proposal unit, a test unit, a development unit, and a release unit. The analysis unit analyzes technical data. The proposal unit proposes a technology integration scenario based on the data analyzed by the analysis unit. The test unit tests the market applicability of the scenario proposed by the proposal unit. The development unit develops a prototype based on the feedback data obtained by the test unit. The release unit releases the prototype developed by the development unit to expand the market. [Effects of the Invention]

[0007] The system according to this embodiment can integrate technologies held by small and medium-sized enterprises to create new products and services. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The technology fusion system according to an embodiment of the present invention is a system that uses generative AI to combine unique technologies held by small and medium-sized enterprises (SMEs) in Japan to create new products and services. The technology fusion system uses generative AI to analyze the technology data held by each company and evaluate its suitability with market needs. Next, the generative AI proposes an optimal technology fusion scenario that takes into account the compatibility of the technologies and market needs. Subsequently, the market adaptability of the proposed scenario is tested, and feedback data is collected and analyzed to identify areas for improvement. Furthermore, the system supports prototype development from optimization and risk prediction at the design stage, and finally releases the product to expand the market. This mechanism maximizes the technological capabilities of SMEs, achieving improved profitability and strengthened competitiveness. It also contributes to solving issues such as succession problems and declining profitability, supporting sustainable growth. For example, a special paint manufacturer and an environmental technology venture can collaborate to develop an "environmentally friendly new paint" that combines a paint with excellent waterproofing and corrosion resistance with harmful substance decomposition technology. This new paint can have the effect of reducing air pollution on the exterior walls of buildings and infrastructure. Furthermore, by combining food container technology, temperature-sensitive technology, and visual interface technology, it is possible to develop containers that visually display the freshness and optimal eating time of food. In this way, by utilizing generative AI, it is possible to integrate the technologies of small and medium-sized enterprises (SMEs) and create new products and services. As a result, the technology integration system can maximize the technological capabilities of SMEs, leading to improved profitability and strengthened competitiveness.

[0029] The technology fusion system according to this embodiment comprises an analysis unit, a proposal unit, a test unit, a development unit, and a release unit. The analysis unit analyzes technology data. For example, the analysis unit collects technology data held by each company and analyzes it using data mining techniques. The analysis unit can also grasp trends in technology data using statistical analysis. Furthermore, the analysis unit can extract patterns in technology data using machine learning algorithms. For example, the analysis unit collects product data and evaluates its suitability with market needs using data mining techniques. It grasps trends in technology data and identifies strengths and weaknesses of technology using statistical analysis. It extracts patterns in technology data and evaluates the compatibility of technologies using machine learning algorithms. The proposal unit proposes technology fusion scenarios based on the data analyzed by the analysis unit. For example, the proposal unit proposes an optimal technology fusion scenario using a generation AI, taking into account the compatibility of technologies and market needs. Furthermore, the proposal unit can evaluate combinations of technologies and consider their feasibility. Furthermore, the proposal unit can identify application fields of the technology fusion scenarios and make specific proposals. For example, the proposal department evaluates the compatibility of technologies and proposes optimal technology fusion scenarios using generation AI. They evaluate the combination of technologies and consider feasibility. They identify application areas for the technology fusion scenarios and make specific proposals. The testing department tests the market applicability of the scenarios proposed by the proposal department. For example, the testing department conducts market research to evaluate the market applicability of the proposed scenarios. The testing department can also conduct consumer testing to evaluate the acceptability of the proposed scenarios. Furthermore, the testing department can conduct competitive analysis to evaluate the competitiveness of the proposed scenarios. For example, the testing department conducts market research to evaluate the market applicability of the proposed scenarios. They conduct consumer testing to evaluate the acceptability of the proposed scenarios. They conduct competitive analysis to evaluate the competitiveness of the proposed scenarios. The development department develops prototypes based on the feedback data obtained by the testing department. For example, the development department optimizes the design phase to improve the performance of the prototypes. The development department can also perform risk prediction to minimize the development risks of the prototypes.Furthermore, the development department can optimize the prototype manufacturing process and support efficient development. For example, the development department can optimize the design phase to improve the performance of the prototype. It can also predict risks and minimize development risks for the prototype. It can optimize the prototype manufacturing process and support efficient development. The release department releases the prototype developed by the development department and expands the market. For example, the release department can formulate marketing strategies and support the market launch of the product. It can also expand sales channels and promote product sales. Furthermore, the release department can support international expansion and help the product enter the global market. For example, the release department can formulate marketing strategies and support the market launch of the product. It can expand sales channels and promote product sales. It can support international expansion and help the product enter the global market. As a result, the technology fusion system according to the embodiment can efficiently perform a series of processes from the analysis of technical data to market expansion. Some or all of the above-described processes in the release department may be performed using AI, for example, or not using AI. For example, in formulating a marketing strategy, the release department can use AI to analyze market data and formulate an optimal strategy.

[0030] The analysis department analyzes technical data. For example, it collects technical data held by various companies and analyzes it using data mining techniques. Specifically, it collects technical data from companies' patent databases, research papers, product specifications, etc., and integrates and analyzes this data. By using data mining techniques, it is possible to extract useful information from technical data and grasp technological trends and market needs. The analysis department can also grasp trends in technical data using statistical analysis. For example, it performs time-series analysis of technical data to predict technological evolution and market fluctuations. Furthermore, the analysis department can extract patterns in technical data using machine learning algorithms. For example, it uses clustering algorithms to classify technical data based on similarity and evaluate the compatibility and potential combination of technologies. This allows the analysis department to perform multifaceted analysis of technical data, identify the strengths and weaknesses of technologies, and evaluate the potential for technological fusion. Furthermore, the analysis department can perform text analysis of technical data using natural language processing techniques. For example, it analyzes text data from patent documents and research papers to evaluate the relevance and novelty of technologies. This allows the analysis department to analyze various aspects of technical data and provide foundational information for technological fusion.

[0031] The proposal department proposes technology fusion scenarios based on data analyzed by the analysis department. For example, the proposal department uses a generative AI to propose optimal technology fusion scenarios, considering factors such as technological compatibility and market needs. Specifically, it sets prompts for the generative AI to generate optimal technology fusion scenarios by inputting technological and market data. The generative AI generates scenarios that match the technology combinations and market needs based on the input data. The proposal department can also evaluate technology combinations and consider feasibility. For example, it evaluates the costs and risks associated with technology combinations and selects feasible technology fusion scenarios. Furthermore, the proposal department can identify application areas for the technology fusion scenarios and make specific proposals. For example, based on the scenarios proposed by the generative AI, it evaluates the applicability in specific markets and industries and proposes specific products and services. This allows the proposal department to propose feasible and market-appropriate technology fusion scenarios based on the data analysis results from the analysis department, thereby supporting the practical application of technology. Additionally, the proposal department can evaluate the output results of the generative AI and make corrections or improvements as needed. For example, it can select the most suitable scenario from those proposed by the generative AI and make revisions based on expert opinions. This allows the proposal department to propose more accurate technology integration scenarios and promote the practical application of the technology.

[0032] The Test Department tests the market applicability of the scenarios proposed by the Proposal Department. For example, the Test Department conducts market research to evaluate the market applicability of the proposed scenarios. Specifically, it conducts consumer surveys and interviews to collect market reactions to the proposed technology fusion scenarios. The Test Department can also conduct consumer tests to evaluate the acceptability of the proposed scenarios. For example, it conducts usability tests using prototypes to evaluate consumer experience and satisfaction. Furthermore, the Test Department can conduct competitive analysis to evaluate the competitiveness of the proposed scenarios. For example, it compares them with competing products and technologies to identify the advantages and differentiating points of the proposed scenarios. This allows the Test Department to comprehensively evaluate the market applicability of the proposed technology fusion scenarios and verify their feasibility for practical application. In addition, the Test Department can provide feedback to the Proposal Department and Development Department based on the test results to support the improvement of the technology fusion scenarios. For example, based on the results of market research and consumer tests, it can propose revisions and improvements to the technology fusion scenarios to optimize the development process. This allows the Test Department to increase the market applicability of the technology fusion scenarios and improve the success rate of practical application.

[0033] The development department develops prototypes based on feedback data obtained from the testing department. For example, the development department optimizes the design phase to improve prototype performance. Specifically, they create detailed design drawings using CAD software and optimize the design through simulations. The development department can also minimize development risks by predicting risks. For example, they use risk assessment tools to evaluate risks at each stage of the development process and implement risk mitigation measures. Furthermore, the development department can optimize the prototype manufacturing process to support efficient development. For example, they introduce robots and automated equipment to automate and streamline the manufacturing process, reducing manufacturing costs and time. This allows the development department to efficiently develop high-performance prototypes that reflect feedback from the testing department, supporting the realization of technology integration scenarios. Additionally, the development department can conduct tests during the prototype stage to verify performance and quality. For example, they conduct environmental and durability tests to evaluate the reliability and durability of the prototype. This allows the development department to make final adjustments for practical application and prepare to launch high-quality products into the market.

[0034] The Release Department releases prototypes developed by the Development Department to expand the market. For example, the Release Department formulates marketing strategies and supports product launch. Specifically, it analyzes target markets and develops optimal marketing strategies. For instance, it utilizes digital marketing to increase product awareness through online advertising and social media. The Release Department can also expand sales channels and promote product sales. For example, it strengthens sales in online and physical stores to expand consumer access. Furthermore, the Release Department can support international expansion and assist in the product's entry into global markets. For example, it conducts overseas market research and develops marketing strategies that are adapted to local regulations and cultures. This allows the Release Department to effectively launch developed prototypes into the market and support product success. Additionally, the Release Department can use AI to analyze market data and develop optimal strategies in marketing strategy formulation. For example, it can use AI to analyze consumer purchasing behavior and market trends and design advertising campaigns optimized for the target market. This enables the Release Department to implement advanced AI-powered marketing strategies and ensure successful product launches.

[0035] The analysis unit can analyze the technical data held by each company and evaluate its suitability to market needs. For example, the analysis unit can collect technical data held by each company and analyze it using data mining techniques. The analysis unit can also grasp trends in the technical data using statistical analysis. Furthermore, the analysis unit can extract patterns in the technical data using machine learning algorithms. For example, the analysis unit can collect product data and evaluate its suitability to market needs using data mining techniques. It can grasp trends in the technical data and identify the strengths and weaknesses of the technology using statistical analysis. It can extract patterns in the technical data and evaluate the compatibility of the technologies using machine learning algorithms. By evaluating the market suitability of the technical data, it is possible to propose more effective technology integration scenarios. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input technical data into a generative AI, and the generative AI can evaluate its suitability to market needs.

[0036] The proposal department can propose optimal technology fusion scenarios that take into account technological compatibility and market needs. For example, the proposal department can propose optimal technology fusion scenarios using generative AI, taking into account technological compatibility and market needs. The proposal department can also evaluate combinations of technologies and examine their feasibility. Furthermore, the proposal department can identify application fields for the technology fusion scenarios and make specific proposals. For example, the proposal department evaluates technological compatibility and proposes optimal technology fusion scenarios using generative AI. It evaluates combinations of technologies and examines their feasibility. It identifies application fields for the technology fusion scenarios and makes specific proposals. By considering technological compatibility and market needs, it is possible to propose more feasible technology fusion scenarios. Some or all of the above processes in the proposal department may be performed using generative AI, for example, or without generative AI. For example, the proposal department can input technological data into generative AI, and the generative AI can propose optimal technology fusion scenarios.

[0037] The testing unit can test the market applicability of the proposed scenario, collect and analyze feedback data, and identify areas for improvement. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. The testing unit can also conduct consumer testing to evaluate the acceptability of the proposed scenario. Furthermore, the testing unit can conduct competitive analysis to evaluate the competitiveness of the proposed scenario. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. Conduct consumer testing to evaluate the acceptability of the proposed scenario. Conduct competitive analysis to evaluate the competitiveness of the proposed scenario. By testing market applicability, areas for improvement of the scenario can be identified, enabling more appropriate technological integration. Some or all of the above processes in the testing unit may be performed using, for example, generative AI, or without generative AI. For example, the testing unit can input the proposed scenario into a generative AI, which can evaluate market applicability and collect and analyze feedback data.

[0038] The development department can support prototype development by performing optimization and risk prediction during the design phase. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. The development department can also minimize the development risks of the prototype by performing risk prediction. Furthermore, the development department can support efficient development by optimizing the prototype manufacturing process. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. It can minimize the development risks of the prototype by performing risk prediction. It can support efficient development by optimizing the prototype manufacturing process. In this way, prototype development can be efficiently supported by performing optimization and risk prediction during the design phase. Some or all of the above processes performed by the development department may be performed using, for example, generative AI, or not using generative AI. For example, the development department can input design data into generative AI, which can perform optimization and risk prediction.

[0039] The release department can expand the market by releasing products. For example, the release department can formulate marketing strategies and support product launch. It can also expand sales channels and promote product sales. Furthermore, the release department can support international expansion and assist in the product's entry into global markets. For example, the release department can formulate marketing strategies and support product launch; expand sales channels and promote product sales; and support international expansion and assist in the product's entry into global markets. This allows for market expansion and improved profitability through product release. Some or all of the above processes in the release department may be performed using, for example, generative AI, or without generative AI. For example, the release department can input marketing data into generative AI, which can then propose an optimal marketing strategy.

[0040] The analysis unit can analyze past success stories of each company's technology data and optimize the analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on past successful technology fusion cases. The analysis unit can also analyze data from success stories, extract common factors, and reflect them in the algorithm. Furthermore, the analysis unit can evaluate the market applicability of success stories and feed this feedback into the analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on past successful technology fusion cases. It analyzes data from success stories, extracts common factors, and reflects them in the algorithm. It evaluates the market applicability of success stories and feeds this feedback into the analysis algorithm. In this way, by analyzing past success stories, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input success story data into a generative AI, and the generative AI can optimize the analysis algorithm.

[0041] The analysis unit can evaluate the reliability of technical data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. The analysis unit can also check the consistency of the data and select highly reliable data. Furthermore, the analysis unit can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. It can check the consistency of the data and select highly reliable data. It can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. By evaluating the reliability of the technical data, the analysis unit can prioritize the analysis of highly reliable data and improve the accuracy of the analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input technical data into a generative AI, which can evaluate the reliability and prioritize the analysis of highly reliable data.

[0042] The analysis unit can perform analysis while considering the geographical location information of each company. For example, the analysis unit can perform analysis considering region-specific market needs based on the geographical location information of each company. The analysis unit can also perform analysis considering logistics costs based on geographical location information. Furthermore, the analysis unit can perform analysis considering the competitive situation in the region based on geographical location information. For example, the analysis unit can perform analysis considering region-specific market needs based on the geographical location information of each company. It can perform analysis considering logistics costs based on geographical location information. It can perform analysis considering the competitive situation in the region based on geographical location information. As a result, by considering geographical location information, it becomes possible to perform analysis that reflects region-specific market needs and logistics costs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input geographical location information into a generative AI, and the generative AI can perform analysis considering region-specific market needs and logistics costs.

[0043] The analysis unit can analyze each company's social media activities and prioritize the analysis of relevant technical data during the analysis process. For example, the analysis unit can analyze each company's social media activities and prioritize the analysis of highly relevant technical data. The analysis unit can also determine the priority of analysis based on social media reactions. Furthermore, the analysis unit can analyze social media trends and prioritize the analysis of relevant technical data. For example, the analysis unit can analyze each company's social media activities and prioritize the analysis of highly relevant technical data. It determines the priority of analysis based on social media reactions. It analyzes social media trends and prioritizes the analysis of relevant technical data. This allows for more effective analysis by prioritizing the analysis of highly relevant technical data through the analysis of social media activities. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input social media data into a generative AI, which can then prioritize the analysis of highly relevant technical data.

[0044] The proposal department can adjust the level of detail in a proposal based on the importance of the technology. For example, the proposal department can provide detailed proposals for highly important technologies, and concise proposals for less important technologies. Furthermore, the proposal department can adjust the priority of proposals according to the importance of the technology. For example, the proposal department can provide detailed proposals for highly important technologies, and concise proposals for less important technologies, adjusting the priority of proposals according to the importance of the technology. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the technology. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input technical data into a generative AI, which can then adjust the level of detail of the proposal based on the importance of the technology.

[0045] The proposal unit can apply different proposal algorithms depending on the category of technology when making a proposal. For example, for environmental technologies, the proposal unit can apply a proposal algorithm that emphasizes environmental impact assessment. For medical technologies, the proposal unit can also apply a proposal algorithm that emphasizes safety assessment. Furthermore, for IT technologies, the proposal unit can also apply a proposal algorithm that emphasizes security assessment. For example, for environmental technologies, the proposal unit can apply a proposal algorithm that emphasizes environmental impact assessment. For medical technologies, it can apply a proposal algorithm that emphasizes safety assessment. For IT technologies, it can apply a proposal algorithm that emphasizes security assessment. By applying proposal algorithms according to the category of technology, more appropriate proposals become possible. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input technology data into a generative AI, and the generative AI can apply different proposal algorithms depending on the category of technology.

[0046] The proposal department can determine the priority of proposals based on the timing of technology submission. For example, the proposal department may prioritize proposals for newer technologies. The proposal department may also re-evaluate and propose older technologies. Furthermore, the proposal department can adjust the priority of proposals based on the submission timing. For example, the proposal department may prioritize proposals for newer technologies, re-evaluate and propose older technologies, and adjust the priority of proposals based on the submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the technology submission timing. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input technology data into a generative AI, which can then determine the priority of proposals based on the technology submission timing.

[0047] The proposal department can adjust the order of proposals based on the relevance of the technologies. For example, the proposal department may prioritize proposing technologies that are highly relevant. It may also postpone proposing technologies that are less relevant. Furthermore, the proposal department can adjust the order of proposals based on the relevance of the technologies. For example, the proposal department may prioritize proposing technologies that are highly relevant. It may postpone proposing technologies that are less relevant. It adjusts the order of proposals based on the relevance of the technologies. This allows for more appropriate proposals to be made by adjusting the order of proposals based on the relevance of the technologies. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input technology data into a generative AI, and the generative AI can adjust the order of proposals based on the relevance of the technologies.

[0048] The testing unit can perform tests while considering the attribute information of the technology submitter. For example, the testing unit can consider the submitter's area of ​​expertise and perform appropriate tests. The testing unit can also adjust the test priority based on the submitter's past performance. Furthermore, the testing unit can improve the accuracy of the tests by considering the submitter's attribute information. For example, the testing unit can consider the submitter's area of ​​expertise and perform appropriate tests. It can adjust the test priority based on the submitter's past performance. It can improve the accuracy of the tests by considering the submitter's attribute information. This makes it possible to perform more appropriate tests by considering the submitter's attribute information. Some or all of the above processes in the testing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the testing unit can input the submitter's attribute information into a generative AI, and the generative AI can adjust the test priority.

[0049] The testing unit can perform tests while considering the geographical distribution of the technology. For example, the testing unit can perform tests considering region-specific market needs based on the geographical distribution of the technology. The testing unit can also perform tests considering logistics costs based on the geographical distribution. Furthermore, the testing unit can perform tests considering the competitive situation in the region based on the geographical distribution. For example, the testing unit can perform tests considering region-specific market needs based on the geographical distribution of the technology. It can perform tests considering logistics costs based on the geographical distribution. It can perform tests considering the competitive situation in the region based on the geographical distribution. This makes it possible to perform tests that reflect region-specific market needs and logistics costs by considering geographical distribution. Some or all of the above processing in the testing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the testing unit can input geographical distribution data into a generative AI, and the generative AI can perform tests considering region-specific market needs and logistics costs.

[0050] The testing unit can improve the accuracy of tests by referring to relevant technical documentation during testing. For example, the testing unit can improve the accuracy of tests by referring to relevant technical documentation. The testing unit can also adjust the test priorities based on the relevant documentation. Furthermore, the testing unit can improve the accuracy of tests by referring to relevant documentation. For example, the testing unit can improve the accuracy of tests by referring to relevant technical documentation. It adjusts the test priorities based on the relevant documentation. It improves the accuracy of tests by referring to relevant documentation. Thus, the accuracy of tests can be improved by referring to relevant documentation. Some or all of the above processing in the testing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the testing unit can input relevant documentation data into a generative AI, and the generative AI can improve the accuracy of the tests.

[0051] The development department can select the optimal development method by analyzing the past development history of the technology during development. For example, the development department can select the optimal development method based on past development history. The development department can also analyze the development history, extract common factors, and reflect them in the development method. Furthermore, the development department can select the optimal development method based on successful cases in the development history. For example, the development department can select the optimal development method based on past development history. They analyze the development history, extract common factors, and reflect them in the development method. They select the optimal development method based on successful cases in the development history. In this way, by analyzing past development history, the optimal development method can be selected and the accuracy of development can be improved. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input past development history data into a generative AI, and the generative AI can select the optimal development method.

[0052] The development department can customize the means of development during development based on the current market conditions of the technology. For example, the development department can customize the means of development based on the current market conditions. The development department can also adjust the means of development considering market demand. Furthermore, the development department can customize the means of development based on the competitive situation in the market. For example, the development department can customize the means of development based on the current market conditions. It can adjust the means of development considering market demand. It can customize the means of development based on the competitive situation in the market. By customizing the means of development based on the current market conditions, more effective development becomes possible. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input market condition data into a generative AI, and the generative AI can customize the means of development.

[0053] The development department can select the optimal development method during development by considering the geographical location of the technology. For example, the development department can select a development method based on the geographical location of the technology, taking into account the specific market needs of the region. The development department can also select a development method that takes logistics costs into account, based on the geographical location of the technology. Furthermore, the development department can select a development method that takes regional competitive conditions into account, based on the geographical location of the technology. For example, the development department can select a development method based on the geographical location of the technology, taking into account the specific market needs of the region. For example, the development department can select a development method that takes logistics costs into account, based on the geographical location of the technology. For example, the development department can select a development method that takes regional competitive conditions into account, based on the geographical location of the technology. This makes it possible to develop products that reflect regional market needs and logistics costs by considering geographical location. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not. For example, the development department can input geographical location information into a generative AI, and the generative AI can select a development method that takes regional market needs and logistics costs into account.

[0054] The development department can improve the accuracy of development by referring to relevant technical literature during development. For example, the development department can improve the accuracy of development by referring to relevant technical literature. The development department can also adjust development priorities based on the relevant literature. Furthermore, the development department can improve the accuracy of development by referring to relevant literature. For example, the development department can improve the accuracy of development by referring to relevant technical literature. Based on the relevant literature, it adjusts development priorities. By referring to relevant literature, it improves the accuracy of development. Thus, by referring to relevant literature, the accuracy of development can be improved. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input relevant literature data into a generative AI, and the generative AI can improve the accuracy of development.

[0055] The release unit can analyze the technology's past release history to select the optimal release method at the time of release. For example, the release unit can select the optimal release method based on past release history. The release unit can also analyze the release history, extract common factors, and reflect them in the release method. Furthermore, the release unit can select the optimal release method based on successful release history cases. For example, the release unit can select the optimal release method based on past release history. It analyzes the release history, extracts common factors, and reflects them in the release method. It selects the optimal release method based on successful release history cases. In this way, by analyzing past release history, the optimal release method can be selected and the accuracy of the release can be improved. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or without a generative AI. For example, the release unit can input past release history data into a generative AI, and the generative AI can select the optimal release method.

[0056] The release unit can customize the release method at the time of release based on the current market conditions of the technology. For example, the release unit customizes the release method based on the current market conditions. The release unit can also adjust the release method considering market demand. Furthermore, the release unit can customize the release method based on the competitive market conditions. For example, the release unit customizes the release method based on the current market conditions. It adjusts the release method considering market demand. It customizes the release method based on the competitive market conditions. This makes it possible to perform a more effective release by customizing the release method based on the current market conditions. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the release unit can input market conditions data into a generative AI, and the generative AI can customize the release method.

[0057] The release unit can select the optimal release method at the time of release, taking into account the geographical location of the technology. For example, the release unit can select a release method based on the geographical location of the technology, taking into account the specific market needs of the region. The release unit can also select a release method that takes logistics costs into account, based on the geographical location of the technology. Furthermore, the release unit can select a release method that takes regional competitive conditions into account, based on the geographical location of the technology. For example, the release unit can select a release method based on the geographical location of the technology, taking into account the specific market needs of the region. For example, the release unit can select a release method that takes logistics costs into account, based on the geographical location of the technology. For example, the release unit can input geographical location information into a generating AI, which can then select a release method that takes regional market needs and logistics costs into account. This makes it possible to perform releases that reflect regional market needs and logistics costs by considering geographical location information. Some or all of the above processing in the release unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the release unit can input geographical location information into a generating AI, and the generating AI can select a release method that takes regional market needs and logistics costs into account.

[0058] The release unit can improve the accuracy of the release by referring to relevant technical documentation at the time of release. The release unit can, for example, refer to relevant technical documentation to improve the accuracy of the release. The release unit can also adjust the release priority based on the relevant documentation. Furthermore, the release unit can improve the accuracy of the release by referring to relevant documentation. For example, the release unit can improve the accuracy of the release by referring to relevant technical documentation. Based on the relevant documentation, the release priority is adjusted. By referring to relevant documentation, the accuracy of the release can be improved. In this way, the accuracy of the release can be improved by referring to relevant documentation. Some or all of the above processing in the release unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the release unit can input relevant documentation data into a generating AI, and the generating AI can improve the accuracy of the release.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The analysis unit can evaluate the reliability of technical data and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. The analysis unit can also check the consistency of the data and select highly reliable data. Furthermore, the analysis unit can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. It can check the consistency of the data and select highly reliable data. It can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. By evaluating the reliability of the technical data, the analysis unit can prioritize the analysis of highly reliable data and improve the accuracy of the analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input technical data into a generative AI, which can evaluate the reliability and prioritize the analysis of highly reliable data.

[0061] The proposal department can propose optimal technology fusion scenarios that take into account technological compatibility and market needs. For example, the proposal department can propose optimal technology fusion scenarios using generative AI, taking into account technological compatibility and market needs. The proposal department can also evaluate combinations of technologies and examine their feasibility. Furthermore, the proposal department can identify application fields for the technology fusion scenarios and make specific proposals. For example, the proposal department evaluates technological compatibility and proposes optimal technology fusion scenarios using generative AI. It evaluates combinations of technologies and examines their feasibility. It identifies application fields for the technology fusion scenarios and makes specific proposals. By considering technological compatibility and market needs, it is possible to propose more feasible technology fusion scenarios. Some or all of the above processes in the proposal department may be performed using generative AI, for example, or without generative AI. For example, the proposal department can input technological data into generative AI, and the generative AI can propose optimal technology fusion scenarios.

[0062] The testing unit can test the market applicability of the proposed scenario, collect and analyze feedback data, and identify areas for improvement. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. The testing unit can also conduct consumer testing to evaluate the acceptability of the proposed scenario. Furthermore, the testing unit can conduct competitive analysis to evaluate the competitiveness of the proposed scenario. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. Conduct consumer testing to evaluate the acceptability of the proposed scenario. Conduct competitive analysis to evaluate the competitiveness of the proposed scenario. By testing market applicability, areas for improvement of the scenario can be identified, enabling more appropriate technological integration. Some or all of the above processes in the testing unit may be performed using, for example, generative AI, or without generative AI. For example, the testing unit can input the proposed scenario into a generative AI, which can evaluate market applicability and collect and analyze feedback data.

[0063] The development department can support prototype development by performing optimization and risk prediction during the design phase. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. The development department can also minimize the development risks of the prototype by performing risk prediction. Furthermore, the development department can support efficient development by optimizing the prototype manufacturing process. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. It can minimize the development risks of the prototype by performing risk prediction. It can support efficient development by optimizing the prototype manufacturing process. In this way, prototype development can be efficiently supported by performing optimization and risk prediction during the design phase. Some or all of the above processes performed by the development department may be performed using, for example, generative AI, or not using generative AI. For example, the development department can input design data into generative AI, which can perform optimization and risk prediction.

[0064] The release department can expand the market by releasing products. For example, the release department can formulate marketing strategies and support product launch. It can also expand sales channels and promote product sales. Furthermore, the release department can support international expansion and assist in the product's entry into global markets. For example, the release department can formulate marketing strategies and support product launch; expand sales channels and promote product sales; and support international expansion and assist in the product's entry into global markets. This allows for market expansion and improved profitability through product release. Some or all of the above processes in the release department may be performed using, for example, generative AI, or without generative AI. For example, the release department can input marketing data into generative AI, which can then propose an optimal marketing strategy.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis department analyzes the technical data. The analysis department collects technical data held by each company and analyzes it using data mining techniques. It also uses statistical analysis to understand trends in the technical data and machine learning algorithms to extract patterns in the technical data. For example, it collects product data, evaluates its suitability with market needs, identifies the strengths and weaknesses of the technology, and evaluates the compatibility of the technology. Step 2: The proposal department proposes technology fusion scenarios based on the data analyzed by the analysis department. The proposal department uses AI to generate optimal technology fusion scenarios, taking into account the compatibility of technologies and market needs, and evaluates the combination of technologies and examines their feasibility. It also identifies application fields for the technology fusion scenarios and makes specific proposals. Step 3: The testing department tests the market applicability of the scenarios proposed by the proposal department. The testing department conducts market research to evaluate the market applicability of the proposed scenarios, conducts consumer testing to evaluate the acceptability of the proposed scenarios, and also conducts competitive analysis to evaluate the competitiveness of the proposed scenarios. Step 4: The development department develops a prototype based on the feedback data obtained by the testing department. The development department optimizes the design phase to improve prototype performance, predicts risks, and minimizes development risks for the prototype. They also optimize the prototype manufacturing process to support efficient development. Step 5: The Release Department releases the prototype developed by the Development Department to expand the market. The Release Department formulates a marketing strategy, supports the product's market launch, expands sales channels, and promotes product sales. It also supports international expansion and assists the product's entry into the global market.

[0067] (Example of form 2) The technology fusion system according to an embodiment of the present invention is a system that uses generative AI to combine unique technologies held by small and medium-sized enterprises (SMEs) in Japan to create new products and services. The technology fusion system uses generative AI to analyze the technology data held by each company and evaluate its suitability with market needs. Next, the generative AI proposes an optimal technology fusion scenario that takes into account the compatibility of the technologies and market needs. Subsequently, the market adaptability of the proposed scenario is tested, and feedback data is collected and analyzed to identify areas for improvement. Furthermore, the system supports prototype development from optimization and risk prediction at the design stage, and finally releases the product to expand the market. This mechanism maximizes the technological capabilities of SMEs, achieving improved profitability and strengthened competitiveness. It also contributes to solving issues such as succession problems and declining profitability, supporting sustainable growth. For example, a special paint manufacturer and an environmental technology venture can collaborate to develop an "environmentally friendly new paint" that combines a paint with excellent waterproofing and corrosion resistance with harmful substance decomposition technology. This new paint can have the effect of reducing air pollution on the exterior walls of buildings and infrastructure. Furthermore, by combining food container technology, temperature-sensitive technology, and visual interface technology, it is possible to develop containers that visually display the freshness and optimal eating time of food. In this way, by utilizing generative AI, it is possible to integrate the technologies of small and medium-sized enterprises (SMEs) and create new products and services. As a result, the technology integration system can maximize the technological capabilities of SMEs, leading to improved profitability and strengthened competitiveness.

[0068] The technology fusion system according to this embodiment comprises an analysis unit, a proposal unit, a test unit, a development unit, and a release unit. The analysis unit analyzes technology data. For example, the analysis unit collects technology data held by each company and analyzes it using data mining techniques. The analysis unit can also grasp trends in technology data using statistical analysis. Furthermore, the analysis unit can extract patterns in technology data using machine learning algorithms. For example, the analysis unit collects product data and evaluates its suitability with market needs using data mining techniques. It grasps trends in technology data and identifies strengths and weaknesses of technology using statistical analysis. It extracts patterns in technology data and evaluates the compatibility of technologies using machine learning algorithms. The proposal unit proposes technology fusion scenarios based on the data analyzed by the analysis unit. For example, the proposal unit proposes an optimal technology fusion scenario using a generation AI, taking into account the compatibility of technologies and market needs. Furthermore, the proposal unit can evaluate combinations of technologies and consider their feasibility. Furthermore, the proposal unit can identify application fields of the technology fusion scenarios and make specific proposals. For example, the proposal department evaluates the compatibility of technologies and proposes optimal technology fusion scenarios using generation AI. They evaluate the combination of technologies and consider feasibility. They identify application areas for the technology fusion scenarios and make specific proposals. The testing department tests the market applicability of the scenarios proposed by the proposal department. For example, the testing department conducts market research to evaluate the market applicability of the proposed scenarios. The testing department can also conduct consumer testing to evaluate the acceptability of the proposed scenarios. Furthermore, the testing department can conduct competitive analysis to evaluate the competitiveness of the proposed scenarios. For example, the testing department conducts market research to evaluate the market applicability of the proposed scenarios. They conduct consumer testing to evaluate the acceptability of the proposed scenarios. They conduct competitive analysis to evaluate the competitiveness of the proposed scenarios. The development department develops prototypes based on the feedback data obtained by the testing department. For example, the development department optimizes the design phase to improve the performance of the prototypes. The development department can also perform risk prediction to minimize the development risks of the prototypes.Furthermore, the development department can optimize the prototype manufacturing process and support efficient development. For example, the development department can optimize the design phase to improve the performance of the prototype. It can also predict risks and minimize development risks for the prototype. It can optimize the prototype manufacturing process and support efficient development. The release department releases the prototype developed by the development department and expands the market. For example, the release department can formulate marketing strategies and support the market launch of the product. It can also expand sales channels and promote product sales. Furthermore, the release department can support international expansion and help the product enter the global market. For example, the release department can formulate marketing strategies and support the market launch of the product. It can expand sales channels and promote product sales. It can support international expansion and help the product enter the global market. As a result, the technology fusion system according to the embodiment can efficiently perform a series of processes from the analysis of technical data to market expansion. Some or all of the above-described processes in the release department may be performed using AI, for example, or not using AI. For example, in formulating a marketing strategy, the release department can use AI to analyze market data and formulate an optimal strategy.

[0069] The analysis department analyzes technical data. For example, it collects technical data held by various companies and analyzes it using data mining techniques. Specifically, it collects technical data from companies' patent databases, research papers, product specifications, etc., and integrates and analyzes this data. By using data mining techniques, it is possible to extract useful information from technical data and grasp technological trends and market needs. The analysis department can also grasp trends in technical data using statistical analysis. For example, it performs time-series analysis of technical data to predict technological evolution and market fluctuations. Furthermore, the analysis department can extract patterns in technical data using machine learning algorithms. For example, it uses clustering algorithms to classify technical data based on similarity and evaluate the compatibility and potential combination of technologies. This allows the analysis department to perform multifaceted analysis of technical data, identify the strengths and weaknesses of technologies, and evaluate the potential for technological fusion. Furthermore, the analysis department can perform text analysis of technical data using natural language processing techniques. For example, it analyzes text data from patent documents and research papers to evaluate the relevance and novelty of technologies. This allows the analysis department to analyze various aspects of technical data and provide foundational information for technological fusion.

[0070] The proposal department proposes technology fusion scenarios based on data analyzed by the analysis department. For example, the proposal department uses a generative AI to propose optimal technology fusion scenarios, considering factors such as technological compatibility and market needs. Specifically, it sets prompts for the generative AI to generate optimal technology fusion scenarios by inputting technological and market data. The generative AI generates scenarios that match the technology combinations and market needs based on the input data. The proposal department can also evaluate technology combinations and consider feasibility. For example, it evaluates the costs and risks associated with technology combinations and selects feasible technology fusion scenarios. Furthermore, the proposal department can identify application areas for the technology fusion scenarios and make specific proposals. For example, based on the scenarios proposed by the generative AI, it evaluates the applicability in specific markets and industries and proposes specific products and services. This allows the proposal department to propose feasible and market-appropriate technology fusion scenarios based on the data analysis results from the analysis department, thereby supporting the practical application of technology. Additionally, the proposal department can evaluate the output results of the generative AI and make corrections or improvements as needed. For example, it can select the most suitable scenario from those proposed by the generative AI and make revisions based on expert opinions. This allows the proposal department to propose more accurate technology integration scenarios and promote the practical application of the technology.

[0071] The Test Department tests the market applicability of the scenarios proposed by the Proposal Department. For example, the Test Department conducts market research to evaluate the market applicability of the proposed scenarios. Specifically, it conducts consumer surveys and interviews to collect market reactions to the proposed technology fusion scenarios. The Test Department can also conduct consumer tests to evaluate the acceptability of the proposed scenarios. For example, it conducts usability tests using prototypes to evaluate consumer experience and satisfaction. Furthermore, the Test Department can conduct competitive analysis to evaluate the competitiveness of the proposed scenarios. For example, it compares them with competing products and technologies to identify the advantages and differentiating points of the proposed scenarios. This allows the Test Department to comprehensively evaluate the market applicability of the proposed technology fusion scenarios and verify their feasibility for practical application. In addition, the Test Department can provide feedback to the Proposal Department and Development Department based on the test results to support the improvement of the technology fusion scenarios. For example, based on the results of market research and consumer tests, it can propose revisions and improvements to the technology fusion scenarios to optimize the development process. This allows the Test Department to increase the market applicability of the technology fusion scenarios and improve the success rate of practical application.

[0072] The development department develops prototypes based on feedback data obtained from the testing department. For example, the development department optimizes the design phase to improve prototype performance. Specifically, they create detailed design drawings using CAD software and optimize the design through simulations. The development department can also minimize development risks by predicting risks. For example, they use risk assessment tools to evaluate risks at each stage of the development process and implement risk mitigation measures. Furthermore, the development department can optimize the prototype manufacturing process to support efficient development. For example, they introduce robots and automated equipment to automate and streamline the manufacturing process, reducing manufacturing costs and time. This allows the development department to efficiently develop high-performance prototypes that reflect feedback from the testing department, supporting the realization of technology integration scenarios. Additionally, the development department can conduct tests during the prototype stage to verify performance and quality. For example, they conduct environmental and durability tests to evaluate the reliability and durability of the prototype. This allows the development department to make final adjustments for practical application and prepare to launch high-quality products into the market.

[0073] The Release Department releases prototypes developed by the Development Department to expand the market. For example, the Release Department formulates marketing strategies and supports product launch. Specifically, it analyzes target markets and develops optimal marketing strategies. For instance, it utilizes digital marketing to increase product awareness through online advertising and social media. The Release Department can also expand sales channels and promote product sales. For example, it strengthens sales in online and physical stores to expand consumer access. Furthermore, the Release Department can support international expansion and assist in the product's entry into global markets. For example, it conducts overseas market research and develops marketing strategies that are adapted to local regulations and cultures. This allows the Release Department to effectively launch developed prototypes into the market and support product success. Additionally, the Release Department can use AI to analyze market data and develop optimal strategies in marketing strategy formulation. For example, it can use AI to analyze consumer purchasing behavior and market trends and design advertising campaigns optimized for the target market. This enables the Release Department to implement advanced AI-powered marketing strategies and ensure successful product launches.

[0074] The analysis unit can analyze the technical data held by each company and evaluate its suitability to market needs. For example, the analysis unit can collect technical data held by each company and analyze it using data mining techniques. The analysis unit can also grasp trends in the technical data using statistical analysis. Furthermore, the analysis unit can extract patterns in the technical data using machine learning algorithms. For example, the analysis unit can collect product data and evaluate its suitability to market needs using data mining techniques. It can grasp trends in the technical data and identify the strengths and weaknesses of the technology using statistical analysis. It can extract patterns in the technical data and evaluate the compatibility of the technologies using machine learning algorithms. By evaluating the market suitability of the technical data, it is possible to propose more effective technology integration scenarios. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input technical data into a generative AI, and the generative AI can evaluate its suitability to market needs.

[0075] The proposal department can propose optimal technology fusion scenarios that take into account technological compatibility and market needs. For example, the proposal department can propose optimal technology fusion scenarios using generative AI, taking into account technological compatibility and market needs. The proposal department can also evaluate combinations of technologies and examine their feasibility. Furthermore, the proposal department can identify application fields for the technology fusion scenarios and make specific proposals. For example, the proposal department evaluates technological compatibility and proposes optimal technology fusion scenarios using generative AI. It evaluates combinations of technologies and examines their feasibility. It identifies application fields for the technology fusion scenarios and makes specific proposals. By considering technological compatibility and market needs, it is possible to propose more feasible technology fusion scenarios. Some or all of the above processes in the proposal department may be performed using generative AI, for example, or without generative AI. For example, the proposal department can input technological data into generative AI, and the generative AI can propose optimal technology fusion scenarios.

[0076] The testing unit can test the market applicability of the proposed scenario, collect and analyze feedback data, and identify areas for improvement. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. The testing unit can also conduct consumer testing to evaluate the acceptability of the proposed scenario. Furthermore, the testing unit can conduct competitive analysis to evaluate the competitiveness of the proposed scenario. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. Conduct consumer testing to evaluate the acceptability of the proposed scenario. Conduct competitive analysis to evaluate the competitiveness of the proposed scenario. By testing market applicability, areas for improvement of the scenario can be identified, enabling more appropriate technological integration. Some or all of the above processes in the testing unit may be performed using, for example, generative AI, or without generative AI. For example, the testing unit can input the proposed scenario into a generative AI, which can evaluate market applicability and collect and analyze feedback data.

[0077] The development department can support prototype development by performing optimization and risk prediction during the design phase. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. The development department can also minimize the development risks of the prototype by performing risk prediction. Furthermore, the development department can support efficient development by optimizing the prototype manufacturing process. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. It can minimize the development risks of the prototype by performing risk prediction. It can support efficient development by optimizing the prototype manufacturing process. In this way, prototype development can be efficiently supported by performing optimization and risk prediction during the design phase. Some or all of the above processes performed by the development department may be performed using, for example, generative AI, or not using generative AI. For example, the development department can input design data into generative AI, which can perform optimization and risk prediction.

[0078] The release department can expand the market by releasing products. For example, the release department can formulate marketing strategies and support product launch. It can also expand sales channels and promote product sales. Furthermore, the release department can support international expansion and assist in the product's entry into global markets. For example, the release department can formulate marketing strategies and support product launch; expand sales channels and promote product sales; and support international expansion and assist in the product's entry into global markets. This allows for market expansion and improved profitability through product release. Some or all of the above processes in the release department may be performed using, for example, generative AI, or without generative AI. For example, the release department can input marketing data into generative AI, which can then propose an optimal marketing strategy.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize the analysis of simple technical data. If the user is relaxed, the analysis unit may also prioritize the analysis of complex technical data. Furthermore, if the user is in a hurry, the analysis unit may prioritize the analysis of technical data that most directly relates to market needs. For example, if the user is stressed, the analysis unit may prioritize the analysis of simple technical data. If the user is relaxed, it may prioritize the analysis of complex technical data. If the user is in a hurry, it may prioritize the analysis of technical data that most directly relates to market needs. By adjusting the analysis priority according to the user's emotions, more effective analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust the priority of the analysis.

[0080] The analysis unit can analyze past success stories of each company's technology data and optimize the analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on past successful technology fusion cases. The analysis unit can also analyze data from success stories, extract common factors, and reflect them in the algorithm. Furthermore, the analysis unit can evaluate the market applicability of success stories and feed this feedback into the analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on past successful technology fusion cases. It analyzes data from success stories, extracts common factors, and reflects them in the algorithm. It evaluates the market applicability of success stories and feeds this feedback into the analysis algorithm. In this way, by analyzing past success stories, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input success story data into a generative AI, and the generative AI can optimize the analysis algorithm.

[0081] The analysis unit can evaluate the reliability of technical data during analysis and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. The analysis unit can also check the consistency of the data and select highly reliable data. Furthermore, the analysis unit can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. It can check the consistency of the data and select highly reliable data. It can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. By evaluating the reliability of the technical data, the analysis unit can prioritize the analysis of highly reliable data and improve the accuracy of the analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input technical data into a generative AI, which can evaluate the reliability and prioritize the analysis of highly reliable data.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust how the analysis results are displayed.

[0083] The analysis unit can perform analysis while considering the geographical location information of each company. For example, the analysis unit can perform analysis considering region-specific market needs based on the geographical location information of each company. The analysis unit can also perform analysis considering logistics costs based on geographical location information. Furthermore, the analysis unit can perform analysis considering the competitive situation in the region based on geographical location information. For example, the analysis unit can perform analysis considering region-specific market needs based on the geographical location information of each company. It can perform analysis considering logistics costs based on geographical location information. It can perform analysis considering the competitive situation in the region based on geographical location information. As a result, by considering geographical location information, it becomes possible to perform analysis that reflects region-specific market needs and logistics costs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input geographical location information into a generative AI, and the generative AI can perform analysis considering region-specific market needs and logistics costs.

[0084] The analysis unit can analyze each company's social media activities and prioritize the analysis of relevant technical data during the analysis process. For example, the analysis unit can analyze each company's social media activities and prioritize the analysis of highly relevant technical data. The analysis unit can also determine the priority of analysis based on social media reactions. Furthermore, the analysis unit can analyze social media trends and prioritize the analysis of relevant technical data. For example, the analysis unit can analyze each company's social media activities and prioritize the analysis of highly relevant technical data. It determines the priority of analysis based on social media reactions. It analyzes social media trends and prioritizes the analysis of relevant technical data. This allows for more effective analysis by prioritizing the analysis of highly relevant technical data through the analysis of social media activities. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input social media data into a generative AI, which can then prioritize the analysis of highly relevant technical data.

[0085] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, it provides concise suggestions. If the user is excited, it provides visually stimulating suggestions. By adjusting the way it presents suggestions according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way it presents its suggestions.

[0086] The proposal department can adjust the level of detail in a proposal based on the importance of the technology. For example, the proposal department can provide detailed proposals for highly important technologies, and concise proposals for less important technologies. Furthermore, the proposal department can adjust the priority of proposals according to the importance of the technology. For example, the proposal department can provide detailed proposals for highly important technologies, and concise proposals for less important technologies, adjusting the priority of proposals according to the importance of the technology. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the technology. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input technical data into a generative AI, which can then adjust the level of detail of the proposal based on the importance of the technology.

[0087] The proposal unit can apply different proposal algorithms depending on the category of technology when making a proposal. For example, for environmental technologies, the proposal unit can apply a proposal algorithm that emphasizes environmental impact assessment. For medical technologies, the proposal unit can also apply a proposal algorithm that emphasizes safety assessment. Furthermore, for IT technologies, the proposal unit can also apply a proposal algorithm that emphasizes security assessment. For example, for environmental technologies, the proposal unit can apply a proposal algorithm that emphasizes environmental impact assessment. For medical technologies, it can apply a proposal algorithm that emphasizes safety assessment. For IT technologies, it can apply a proposal algorithm that emphasizes security assessment. By applying proposal algorithms according to the category of technology, more appropriate proposals become possible. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input technology data into a generative AI, and the generative AI can apply different proposal algorithms depending on the category of technology.

[0088] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, it will provide concise suggestions. If the user is excited, it will provide visually stimulating suggestions. This allows for more effective suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions.

[0089] The proposal department can determine the priority of proposals based on the timing of technology submission. For example, the proposal department may prioritize proposals for newer technologies. The proposal department may also re-evaluate and propose older technologies. Furthermore, the proposal department can adjust the priority of proposals based on the submission timing. For example, the proposal department may prioritize proposals for newer technologies, re-evaluate and propose older technologies, and adjust the priority of proposals based on the submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the technology submission timing. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal department can input technology data into a generative AI, which can then determine the priority of proposals based on the technology submission timing.

[0090] The proposal department can adjust the order of proposals based on the relevance of the technologies. For example, the proposal department may prioritize proposing technologies that are highly relevant. It may also postpone proposing technologies that are less relevant. Furthermore, the proposal department can adjust the order of proposals based on the relevance of the technologies. For example, the proposal department may prioritize proposing technologies that are highly relevant. It may postpone proposing technologies that are less relevant. It adjusts the order of proposals based on the relevance of the technologies. This allows for more appropriate proposals to be made by adjusting the order of proposals based on the relevance of the technologies. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input technology data into a generative AI, and the generative AI can adjust the order of proposals based on the relevance of the technologies.

[0091] The testing unit can estimate the user's emotions and adjust the test criteria based on the estimated emotions. For example, if the user is relaxed, the testing unit can conduct a detailed test. If the user is in a hurry, the testing unit can conduct a concise test. Furthermore, if the user is excited, the testing unit can conduct a visually stimulating test. For example, if the user is relaxed, the testing unit conducts a detailed test. If the user is in a hurry, it conducts a concise test. If the user is excited, it conducts a visually stimulating test. This allows for more effective testing by adjusting the test criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the testing unit can input user emotion data into a generative AI, which can then adjust the test criteria.

[0092] The testing unit can perform tests while considering the attribute information of the technology submitter. For example, the testing unit can consider the submitter's area of ​​expertise and perform appropriate tests. The testing unit can also adjust the test priority based on the submitter's past performance. Furthermore, the testing unit can improve the accuracy of the tests by considering the submitter's attribute information. For example, the testing unit can consider the submitter's area of ​​expertise and perform appropriate tests. It can adjust the test priority based on the submitter's past performance. It can improve the accuracy of the tests by considering the submitter's attribute information. This makes it possible to perform more appropriate tests by considering the submitter's attribute information. Some or all of the above processes in the testing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the testing unit can input the submitter's attribute information into a generative AI, and the generative AI can adjust the test priority.

[0093] The testing unit can estimate the user's emotions and adjust the display order of the test results based on the estimated emotions. For example, if the user is relaxed, the testing unit can display detailed test results. If the user is in a hurry, the testing unit can also display concise test results. Furthermore, if the user is excited, the testing unit can display visually stimulating test results. For example, if the user is relaxed, the testing unit displays detailed test results. If the user is in a hurry, it displays concise test results. If the user is excited, it displays visually stimulating test results. This allows for a more user-friendly display by adjusting the display order of the test results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using a generative AI, or not. For example, the testing unit can input user emotion data into a generative AI, and the generative AI can adjust the display order of the test results.

[0094] The testing unit can perform tests while considering the geographical distribution of the technology. For example, the testing unit can perform tests considering region-specific market needs based on the geographical distribution of the technology. The testing unit can also perform tests considering logistics costs based on the geographical distribution. Furthermore, the testing unit can perform tests considering the competitive situation in the region based on the geographical distribution. For example, the testing unit can perform tests considering region-specific market needs based on the geographical distribution of the technology. It can perform tests considering logistics costs based on the geographical distribution. It can perform tests considering the competitive situation in the region based on the geographical distribution. This makes it possible to perform tests that reflect region-specific market needs and logistics costs by considering geographical distribution. Some or all of the above processing in the testing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the testing unit can input geographical distribution data into a generative AI, and the generative AI can perform tests considering region-specific market needs and logistics costs.

[0095] The testing unit can improve the accuracy of tests by referring to relevant technical documentation during testing. For example, the testing unit can improve the accuracy of tests by referring to relevant technical documentation. The testing unit can also adjust the test priorities based on the relevant documentation. Furthermore, the testing unit can improve the accuracy of tests by referring to relevant documentation. For example, the testing unit can improve the accuracy of tests by referring to relevant technical documentation. It adjusts the test priorities based on the relevant documentation. It improves the accuracy of tests by referring to relevant documentation. Thus, the accuracy of tests can be improved by referring to relevant documentation. Some or all of the above processing in the testing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the testing unit can input relevant documentation data into a generative AI, and the generative AI can improve the accuracy of the tests.

[0096] The development department can estimate the user's emotions and adjust the development method based on the estimated emotions. For example, if the user is relaxed, the development department can provide a detailed development method. If the user is in a hurry, the development department can also provide a concise development method. Furthermore, if the user is excited, the development department can provide a visually stimulating development method. For example, if the user is relaxed, the development department can provide a detailed development method. If the user is in a hurry, it can provide a concise development method. If the user is excited, it can provide a visually stimulating development method. This allows for more effective development by adjusting the development method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input user emotion data into a generative AI, and the generative AI can adjust the development method.

[0097] The development department can select the optimal development method by analyzing the past development history of the technology during development. For example, the development department can select the optimal development method based on past development history. The development department can also analyze the development history, extract common factors, and reflect them in the development method. Furthermore, the development department can select the optimal development method based on successful cases in the development history. For example, the development department can select the optimal development method based on past development history. They analyze the development history, extract common factors, and reflect them in the development method. They select the optimal development method based on successful cases in the development history. In this way, by analyzing past development history, the optimal development method can be selected and the accuracy of development can be improved. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input past development history data into a generative AI, and the generative AI can select the optimal development method.

[0098] The development department can customize the means of development during development based on the current market conditions of the technology. For example, the development department can customize the means of development based on the current market conditions. The development department can also adjust the means of development considering market demand. Furthermore, the development department can customize the means of development based on the competitive situation in the market. For example, the development department can customize the means of development based on the current market conditions. It can adjust the means of development considering market demand. It can customize the means of development based on the competitive situation in the market. By customizing the means of development based on the current market conditions, more effective development becomes possible. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input market condition data into a generative AI, and the generative AI can customize the means of development.

[0099] The development department can estimate the user's emotions and determine development priorities based on those estimated emotions. For example, if the user is relaxed, the development department might prioritize detailed development. If the user is in a hurry, the development department might prioritize concise development. Furthermore, if the user is excited, the development department might prioritize visually stimulating development. For example, if the user is relaxed, the development department might prioritize detailed development. If the user is in a hurry, it might prioritize concise development. If the user is excited, it might prioritize visually stimulating development. This allows for more effective development by determining development priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the development department may be performed using, for example, generative AI, or not using generative AI. For example, the development department can input user emotion data into a generative AI, which can then determine development priorities.

[0100] The development department can select the optimal development method during development by considering the geographical location of the technology. For example, the development department can select a development method based on the geographical location of the technology, taking into account the specific market needs of the region. The development department can also select a development method that takes logistics costs into account, based on the geographical location of the technology. Furthermore, the development department can select a development method that takes regional competitive conditions into account, based on the geographical location of the technology. For example, the development department can select a development method based on the geographical location of the technology, taking into account the specific market needs of the region. For example, the development department can select a development method that takes logistics costs into account, based on the geographical location of the technology. For example, the development department can select a development method that takes regional competitive conditions into account, based on the geographical location of the technology. This makes it possible to develop products that reflect regional market needs and logistics costs by considering geographical location. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not. For example, the development department can input geographical location information into a generative AI, and the generative AI can select a development method that takes regional market needs and logistics costs into account.

[0101] The development department can improve the accuracy of development by referring to relevant technical literature during development. For example, the development department can improve the accuracy of development by referring to relevant technical literature. The development department can also adjust development priorities based on the relevant literature. Furthermore, the development department can improve the accuracy of development by referring to relevant literature. For example, the development department can improve the accuracy of development by referring to relevant technical literature. Based on the relevant literature, it adjusts development priorities. By referring to relevant literature, it improves the accuracy of development. Thus, by referring to relevant literature, the accuracy of development can be improved. Some or all of the above processes in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input relevant literature data into a generative AI, and the generative AI can improve the accuracy of development.

[0102] The release unit can estimate the user's emotions and adjust the release method based on the estimated emotions. For example, if the user is relaxed, the release unit can provide a detailed release method. If the user is in a hurry, the release unit can also provide a concise release method. Furthermore, if the user is excited, the release unit can provide a visually stimulating release method. For example, if the user is relaxed, the release unit provides a detailed release method. If the user is in a hurry, it provides a concise release method. If the user is excited, it provides a visually stimulating release method. This allows for a more effective release by adjusting the release method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the release unit may be performed using a generative AI, for example, or not using a generative AI. For example, the release unit can input user emotion data into a generative AI, which can then adjust the release method.

[0103] The release unit can analyze the technology's past release history to select the optimal release method at the time of release. For example, the release unit can select the optimal release method based on past release history. The release unit can also analyze the release history, extract common factors, and reflect them in the release method. Furthermore, the release unit can select the optimal release method based on successful release history cases. For example, the release unit can select the optimal release method based on past release history. It analyzes the release history, extracts common factors, and reflects them in the release method. It selects the optimal release method based on successful release history cases. In this way, by analyzing past release history, the optimal release method can be selected and the accuracy of the release can be improved. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or without a generative AI. For example, the release unit can input past release history data into a generative AI, and the generative AI can select the optimal release method.

[0104] The release unit can customize the release method at the time of release based on the current market conditions of the technology. For example, the release unit customizes the release method based on the current market conditions. The release unit can also adjust the release method considering market demand. Furthermore, the release unit can customize the release method based on the competitive market conditions. For example, the release unit customizes the release method based on the current market conditions. It adjusts the release method considering market demand. It customizes the release method based on the competitive market conditions. This makes it possible to perform a more effective release by customizing the release method based on the current market conditions. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the release unit can input market conditions data into a generative AI, and the generative AI can customize the release method.

[0105] The release unit can estimate the user's emotions and determine the priority of releases based on the estimated emotions. For example, if the user is relaxed, the release unit may prioritize detailed releases. If the user is in a hurry, the release unit may also prioritize concise releases. Furthermore, if the user is excited, the release unit may prioritize visually stimulating releases. For example, if the user is relaxed, the release unit prioritizes detailed releases. If the user is in a hurry, it prioritizes concise releases. If the user is excited, it prioritizes visually stimulating releases. This allows for more effective releases by determining the priority of releases according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the release unit can input user emotion data into a generative AI, which can then determine the priority of releases.

[0106] The release unit can select the optimal release method at the time of release, taking into account the geographical location of the technology. For example, the release unit can select a release method based on the geographical location of the technology, taking into account the specific market needs of the region. The release unit can also select a release method that takes logistics costs into account, based on the geographical location of the technology. Furthermore, the release unit can select a release method that takes regional competitive conditions into account, based on the geographical location of the technology. For example, the release unit can select a release method based on the geographical location of the technology, taking into account the specific market needs of the region. For example, the release unit can select a release method that takes logistics costs into account, based on the geographical location of the technology. For example, the release unit can input geographical location information into a generating AI, which can then select a release method that takes regional market needs and logistics costs into account. This makes it possible to perform releases that reflect regional market needs and logistics costs by considering geographical location information. Some or all of the above processing in the release unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the release unit can input geographical location information into a generating AI, and the generating AI can select a release method that takes regional market needs and logistics costs into account.

[0107] The release unit can improve the accuracy of the release by referring to relevant technical documentation at the time of release. The release unit can, for example, refer to relevant technical documentation to improve the accuracy of the release. The release unit can also adjust the release priority based on the relevant documentation. Furthermore, the release unit can improve the accuracy of the release by referring to relevant documentation. For example, the release unit can improve the accuracy of the release by referring to relevant technical documentation. Based on the relevant documentation, the release priority is adjusted. By referring to relevant documentation, the accuracy of the release can be improved. In this way, the accuracy of the release can be improved by referring to relevant documentation. Some or all of the above processing in the release unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the release unit can input relevant documentation data into a generating AI, and the generating AI can improve the accuracy of the release.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis unit can evaluate the reliability of technical data and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. The analysis unit can also check the consistency of the data and select highly reliable data. Furthermore, the analysis unit can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. For example, the analysis unit can evaluate the source of the technical data and prioritize highly reliable data. It can check the consistency of the data and select highly reliable data. It can evaluate the frequency of data updates and prioritize the most recent and highly reliable data. By evaluating the reliability of the technical data, the analysis unit can prioritize the analysis of highly reliable data and improve the accuracy of the analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input technical data into a generative AI, which can evaluate the reliability and prioritize the analysis of highly reliable data.

[0110] The proposal department can propose optimal technology fusion scenarios that take into account technological compatibility and market needs. For example, the proposal department can propose optimal technology fusion scenarios using generative AI, taking into account technological compatibility and market needs. The proposal department can also evaluate combinations of technologies and examine their feasibility. Furthermore, the proposal department can identify application fields for the technology fusion scenarios and make specific proposals. For example, the proposal department evaluates technological compatibility and proposes optimal technology fusion scenarios using generative AI. It evaluates combinations of technologies and examines their feasibility. It identifies application fields for the technology fusion scenarios and makes specific proposals. By considering technological compatibility and market needs, it is possible to propose more feasible technology fusion scenarios. Some or all of the above processes in the proposal department may be performed using generative AI, for example, or without generative AI. For example, the proposal department can input technological data into generative AI, and the generative AI can propose optimal technology fusion scenarios.

[0111] The testing unit can test the market applicability of the proposed scenario, collect and analyze feedback data, and identify areas for improvement. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. The testing unit can also conduct consumer testing to evaluate the acceptability of the proposed scenario. Furthermore, the testing unit can conduct competitive analysis to evaluate the competitiveness of the proposed scenario. For example, the testing unit can conduct market research to evaluate the market applicability of the proposed scenario. Conduct consumer testing to evaluate the acceptability of the proposed scenario. Conduct competitive analysis to evaluate the competitiveness of the proposed scenario. By testing market applicability, areas for improvement of the scenario can be identified, enabling more appropriate technological integration. Some or all of the above processes in the testing unit may be performed using, for example, generative AI, or without generative AI. For example, the testing unit can input the proposed scenario into a generative AI, which can evaluate market applicability and collect and analyze feedback data.

[0112] The development department can support prototype development by performing optimization and risk prediction during the design phase. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. The development department can also minimize the development risks of the prototype by performing risk prediction. Furthermore, the development department can support efficient development by optimizing the prototype manufacturing process. For example, the development department can improve the performance of the prototype by performing optimization during the design phase. It can minimize the development risks of the prototype by performing risk prediction. It can support efficient development by optimizing the prototype manufacturing process. In this way, prototype development can be efficiently supported by performing optimization and risk prediction during the design phase. Some or all of the above processes performed by the development department may be performed using, for example, generative AI, or not using generative AI. For example, the development department can input design data into generative AI, which can perform optimization and risk prediction.

[0113] The release department can expand the market by releasing products. For example, the release department can formulate marketing strategies and support product launch. It can also expand sales channels and promote product sales. Furthermore, the release department can support international expansion and assist in the product's entry into global markets. For example, the release department can formulate marketing strategies and support product launch; expand sales channels and promote product sales; and support international expansion and assist in the product's entry into global markets. This allows for market expansion and improved profitability through product release. Some or all of the above processes in the release department may be performed using, for example, generative AI, or without generative AI. For example, the release department can input marketing data into generative AI, which can then propose an optimal marketing strategy.

[0114] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize the analysis of simple technical data. If the user is relaxed, the analysis unit may also prioritize the analysis of complex technical data. Furthermore, if the user is in a hurry, the analysis unit may prioritize the analysis of technical data that most directly relates to market needs. For example, if the user is stressed, the analysis unit may prioritize the analysis of simple technical data. If the user is relaxed, it may prioritize the analysis of complex technical data. If the user is in a hurry, it may prioritize the analysis of technical data that most directly relates to market needs. By adjusting the analysis priority according to the user's emotions, more effective analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then adjust the priority of the analysis.

[0115] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, it provides concise suggestions. If the user is excited, it provides visually stimulating suggestions. By adjusting the way it presents suggestions according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way it presents its suggestions.

[0116] The testing unit can estimate the user's emotions and adjust the test criteria based on the estimated emotions. For example, if the user is relaxed, the testing unit can conduct a detailed test. If the user is in a hurry, the testing unit can conduct a concise test. Furthermore, if the user is excited, the testing unit can conduct a visually stimulating test. For example, if the user is relaxed, the testing unit conducts a detailed test. If the user is in a hurry, it conducts a concise test. If the user is excited, it conducts a visually stimulating test. This allows for more effective testing by adjusting the test criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the testing unit can input user emotion data into a generative AI, which can then adjust the test criteria.

[0117] The development department can estimate the user's emotions and adjust the development method based on the estimated emotions. For example, if the user is relaxed, the development department can provide a detailed development method. If the user is in a hurry, the development department can also provide a concise development method. Furthermore, if the user is excited, the development department can provide a visually stimulating development method. For example, if the user is relaxed, the development department can provide a detailed development method. If the user is in a hurry, it can provide a concise development method. If the user is excited, it can provide a visually stimulating development method. This allows for more effective development by adjusting the development method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the development department may be performed using, for example, a generative AI, or not using a generative AI. For example, the development department can input user emotion data into a generative AI, and the generative AI can adjust the development method.

[0118] The release unit can estimate the user's emotions and adjust the release method based on the estimated emotions. For example, if the user is relaxed, the release unit can provide a detailed release method. If the user is in a hurry, the release unit can also provide a concise release method. Furthermore, if the user is excited, the release unit can provide a visually stimulating release method. For example, if the user is relaxed, the release unit provides a detailed release method. If the user is in a hurry, it provides a concise release method. If the user is excited, it provides a visually stimulating release method. This allows for a more effective release by adjusting the release method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the release unit may be performed using a generative AI, for example, or not using a generative AI. For example, the release unit can input user emotion data into a generative AI, which can then adjust the release method.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The analysis department analyzes the technical data. The analysis department collects technical data held by each company and analyzes it using data mining techniques. It also uses statistical analysis to understand trends in the technical data and machine learning algorithms to extract patterns in the technical data. For example, it collects product data, evaluates its suitability with market needs, identifies the strengths and weaknesses of the technology, and evaluates the compatibility of the technology. Step 2: The proposal department proposes technology fusion scenarios based on the data analyzed by the analysis department. The proposal department uses AI to generate optimal technology fusion scenarios, taking into account the compatibility of technologies and market needs, and evaluates the combination of technologies and examines their feasibility. It also identifies application fields for the technology fusion scenarios and makes specific proposals. Step 3: The testing department tests the market applicability of the scenarios proposed by the proposal department. The testing department conducts market research to evaluate the market applicability of the proposed scenarios, conducts consumer testing to evaluate the acceptability of the proposed scenarios, and also conducts competitive analysis to evaluate the competitiveness of the proposed scenarios. Step 4: The development department develops a prototype based on the feedback data obtained by the testing department. The development department optimizes the design phase to improve prototype performance, predicts risks, and minimizes development risks for the prototype. They also optimize the prototype manufacturing process to support efficient development. Step 5: The Release Department releases the prototype developed by the Development Department to expand the market. The Release Department formulates a marketing strategy, supports the product's market launch, expands sales channels, and promotes product sales. It also supports international expansion and assists the product's entry into the global market.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the analysis unit, proposal unit, test unit, development unit, and release unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The test unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The development unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The release unit is implemented by the control unit 46A of the smart device 14 or 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 examples described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the analysis unit, proposal unit, test unit, development unit, and release unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The test unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The development unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The release unit is implemented, for example, by the control unit 46A of the smart glasses 214 or 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 examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the analysis unit, proposal unit, test unit, development unit, and release unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The test unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The development unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The release unit is implemented by, for example, the control unit 46A of the headset terminal 314 or 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 examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the analysis unit, proposal unit, test unit, development unit, and release unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The test unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The development unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The release unit is implemented, for example, by the control unit 46A of the robot 414 or 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 examples described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The analysis unit analyzes the technical data, A proposal unit proposes a technology integration scenario based on the data analyzed by the aforementioned analysis unit, A test unit that tests the market applicability of the scenario proposed by the aforementioned proposal unit, The development unit develops a prototype based on the feedback data obtained by the aforementioned testing unit, The system includes a release unit that releases prototypes developed by the aforementioned development unit and expands the market. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We analyze the technical data held by each company and evaluate its suitability to market needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the optimal technology integration scenario, taking into account technological compatibility and market needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned test unit is Test the market applicability of the proposed scenarios, collect and analyze feedback data, and identify areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned development department, Supporting prototype development from optimization and risk prediction during the design phase. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned release section is Release products to expand the market. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We analyze past success stories of each company's technical data and optimize the analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the reliability of technical data is evaluated, and the most reliable data is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, the geographical location information of each company is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, we analyze each company's social media activities and prioritize the analysis of relevant technical data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of technology. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of technical submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the technologies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned test unit is We estimate the user's emotions and adjust the test criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned test unit is During testing, the system takes into account the attribute information of the technical submitter. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned test unit is It estimates the user's emotions and adjusts the display order of test results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned test unit is During testing, the geographical distribution of technologies should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned test unit is During testing, refer to relevant technical literature to improve test accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned development department, We estimate user emotions and adjust development methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned development department, During development, the optimal development method is selected by analyzing the technology's past development history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned development department, During development, customize the development methods based on the current market conditions of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned development department, We estimate user sentiment and determine development priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned development department, During development, the optimal development method is selected by considering the geographical location of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned development department, During development, we improve the accuracy of the development process by referring to relevant technical literature. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned release section is We estimate user sentiment and adjust the release method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned release section is At the time of release, we analyze the technology's past release history to select the optimal release method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned release section is At the time of release, customize the release method based on the current market status of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned release section is We estimate user sentiment and determine release priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned release section is When releasing a technology, the optimal release method is selected, taking into account its geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned release section is During release, we refer to relevant technical documentation to improve the accuracy of the release. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 analysis unit analyzes the technical data, A proposal unit proposes a technology integration scenario based on the data analyzed by the aforementioned analysis unit, A test unit that tests the market applicability of the scenario proposed by the aforementioned proposal unit, The development unit develops a prototype based on the feedback data obtained by the aforementioned testing unit, The system includes a release unit that releases prototypes developed by the aforementioned development unit and expands the market. A system characterized by the following features.

2. The aforementioned analysis unit, We analyze the technical data held by each company and evaluate its suitability to market needs. The system according to feature 1.

3. The aforementioned proposal section is, We propose the optimal technology integration scenario, taking into account technological compatibility and market needs. The system according to feature 1.

4. The aforementioned test unit is Test the market applicability of the proposed scenarios, collect and analyze feedback data, and identify areas for improvement. The system according to feature 1.

5. The aforementioned development department, Supporting prototype development from optimization and risk prediction during the design phase. The system according to feature 1.

6. The aforementioned release section is Release products to expand the market. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, We analyze past success stories of each company's technical data and optimize the analysis algorithm. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the reliability of technical data is evaluated, and the most reliable data is prioritized for analysis. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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