system

Abstract

The system according to this embodiment aims to enable small and medium-sized enterprises to achieve effective collaboration. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a matching unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The matching unit creates a matching list based on the analysis results obtained by the analysis unit. The proposal unit makes collaboration proposals based on the matching list created by the matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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 prior art, there was a problem that it was difficult for small and medium-sized enterprises to come up with and execute collaboration ideas and there was no progress.

[0005] The system according to the embodiment aims to enable small and medium-sized enterprises to achieve effective collaboration.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The matching unit creates a matching list based on the analysis results obtained by the analysis unit. The proposal unit makes collaboration proposals based on the matching list created by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment enables small and medium-sized enterprises to achieve effective collaboration. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The support system according to an embodiment of the present invention is a system for small and medium-sized enterprises to solve problems through product and service collaboration. This support system uses generative AI to match candidate companies and products and provides comprehensive support from collaboration proposals to product and service development. First, the generative AI takes in publicly available information such as business overviews, service and product overviews, geography, and locations for all industries and sectors, and creates a matching list sorted by usefulness, synergy effect, feasibility, and potential interest. Next, the generative AI proposes collaborations to the candidate companies. These proposals include information such as how each problem will be solved, social contribution, synergy effect, feasibility, and potential interest. Furthermore, the generative AI takes in complaints, concerns, and problems received by call centers and combines them with publicly available product, service, and company information, production location data, etc., to find products and companies that can solve the problems. Based on this information, the generative AI proposes collaborations to the candidate companies and products and provides comprehensive support from matching to product and service development. This system enables small and medium-sized enterprises (SMEs) to overcome challenges such as personnel shortages, idea generation, research, and uncertainty about projected effects, and to create new products and services through collaboration. Furthermore, by utilizing generative AI, it becomes possible to materialize collaborative ideas that humans might not conceive, and release products and services that benefit people. For example, the generative AI can match tourism and agricultural companies and propose tourist farms. This proposal would include effects such as attracting tourists and promoting the sale of agricultural products. The generative AI can also match medical device manufacturers and IT companies and propose telemedicine systems. This proposal would include effects such as improving access to medical care and reducing costs. In this way, the present invention contributes to the development of society as a whole by supporting collaboration among SMEs using generative AI and creating new products and services. As a result, the support system can efficiently support collaboration among SMEs and create new products and services.

[0029] The support system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit can collect information such as business overviews, service / product overviews, geography, and locations of all publicly available industries and sectors. The data collection unit can also collect data on complaints, concerns, and problems received by call centers. For example, the data collection unit stores complaints received by call centers in a database and provides it to the analysis unit. Furthermore, the data collection unit can also collect feedback from social media and online forums. For example, the data collection unit analyzes social media posts and collects user opinions. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. Furthermore, the analysis unit can analyze the collected data along a time axis to identify trends and patterns. For example, the analysis unit analyzes past data to predict future trends. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, the analysis unit analyzes data from different industries to identify new business opportunities. The matching unit creates a matching list based on the analysis results obtained by the analysis unit. For example, the matching unit can create matching lists based on the analysis results, sorted by usefulness, synergy effect, feasibility, or potential interest. The matching unit can also select the most suitable partner by considering a company's past collaboration history. For example, it can analyze past collaboration history and select partners with a high success rate. Furthermore, the matching unit can simulate matching results and evaluate the expected effects in advance. For example, it can perform simulations to evaluate the effectiveness of the matching. The proposal unit makes collaboration proposals based on the matching list created by the matching unit. For example, the proposal unit can propose collaborations to specific products or companies. The proposal unit can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results.For example, the proposal department evaluates the effectiveness of a proposal and makes the optimal proposal. Furthermore, the proposal department can customize the proposal content and make specific proposals tailored to the characteristics and needs of a company. For example, the proposal department provides proposal content tailored to the characteristics of a company. In this way, the support system according to the embodiment can efficiently support collaboration among small and medium-sized enterprises. Some or all of the above-described processing in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can use generative AI to make collaboration proposals to specific products or companies.

[0030] The data collection unit collects data. For example, the data collection unit can collect information such as business overviews, service and product overviews, geography, and locations of all publicly available industry and sector information. Specifically, it collects information from publicly available databases on the internet, official company websites, and industry reports. This allows the data collection unit to comprehensively understand the business content, services, product details, and geographical location information of each company. The data collection unit can also collect data on complaints, concerns, and problems received by call centers. For example, complaints and inquiries received by call center operators are recorded in a database and retrieved by the data collection unit. This allows for a detailed understanding of the problems and dissatisfactions customers have. Furthermore, the data collection unit can also collect feedback from social media and online forums. For example, it analyzes social media posts and collects user opinions and impressions. This includes using natural language processing technology to analyze the content of posts and classify positive and negative opinions. This allows the data collection unit to obtain real-time customer feedback and understand the evaluation of a company's services and products. The data collection unit can gather information from these diverse data sources and manage it centrally, enabling a comprehensive understanding of the company's current situation and market trends.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. Specifically, it can use statistical analysis to reveal the distribution and correlation of data, and machine learning algorithms to extract patterns and trends from the data. For example, it can analyze collected call center complaint data to identify trends in dissatisfaction with specific products or services. The analysis unit can also analyze the collected data along a time axis to identify trends and patterns. For example, it can analyze data from the past few years to understand seasonal sales fluctuations and changes in customer interest at specific times. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, it can compare data from different industries to identify common challenges and success stories and discover new business opportunities. Based on these analysis results, the analysis unit can provide useful information to support corporate strategy planning and decision-making.

[0032] The matching department creates matching lists based on the analysis results obtained by the analysis department. For example, the matching department can create matching lists in order of usefulness, synergy effect, feasibility, or potential interest based on the analysis results. Specifically, based on the data provided by the analysis department, it selects the most suitable partners considering the characteristics and needs of each company. For example, if a company needs technical support when developing a new product, it will prioritize including companies with high technical capabilities in the matching list. The matching department can also select the most suitable partners by considering a company's past collaboration history. For example, it can analyze the history of successful past collaborations and select partners who are likely to achieve similar success. Furthermore, the matching department can simulate matching results and evaluate the expected effects in advance. For example, it can conduct simulations to evaluate the economic effects and market impact of a particular partnership. This allows the matching department to propose the most effective partnerships for companies.

[0033] The Proposal Department makes collaboration proposals based on the matching list created by the Matching Department. For example, the Proposal Department can propose collaborations to specific products or companies. Specifically, it creates proposals tailored to the characteristics and needs of the companies, and presents specific collaboration methods and expected effects. The Proposal Department can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, it can simulate the effects of the proposals and select the optimal proposal. Furthermore, the Proposal Department can customize the proposals and make specific proposals tailored to the characteristics and needs of the companies. For example, it can provide proposals tailored to the characteristics of the companies. This allows the Proposal Department to propose the most effective collaborations for companies and increase their feasibility. Some or all of the above processes in the Proposal Department may be performed using, for example, generative AI, or not. For example, the Proposal Department can use generative AI to propose collaborations to specific products or companies. The generative AI generates optimal proposals based on past data and trends and provides them to companies. This allows the Proposal Department to make proposals quickly and effectively.

[0034] The data collection unit can collect data on complaints, concerns, and problems received by the call center. For example, the data collection unit can store complaints received by the call center in a database and provide it to the analysis unit. The data collection unit can also collect concerns received by the call center and provide appropriate solutions. For example, the data collection unit can collect problems received by the call center in real time and respond quickly. Furthermore, the data collection unit can analyze the data received by the call center and quickly understand the user's problems. This allows the data collection unit to quickly understand and respond to the user's problems. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to collect data on complaints and concerns received by the call center.

[0035] The analysis unit can analyze the collected data and identify products and companies that can solve the problem. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. The analysis unit can also analyze the collected data along a time axis to identify trends and patterns. For example, the analysis unit can analyze past data to predict future trends. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, the analysis unit can analyze data from different industries to identify new business opportunities. This allows the analysis unit to identify appropriate products and companies and support problem solving. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not. For example, the analysis unit can use generative AI to analyze the collected data and identify products and companies that can solve the problem.

[0036] The proposal department can propose collaborations to specific products or companies. For example, the proposal department can propose collaborations to specific products or companies. The proposal department can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, the proposal department can evaluate the effectiveness of the proposals and make the optimal proposals. Furthermore, the proposal department can customize the proposals and make specific proposals tailored to the characteristics and needs of the companies. For example, the proposal department can provide proposals tailored to the characteristics of the companies. This enables the proposal department to achieve effective collaborations. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to propose collaborations to specific products or companies.

[0037] The proposal department can evaluate the effectiveness of the proposed collaboration. For example, the proposal department can evaluate the effectiveness of the proposed collaboration and confirm the validity of the proposal. The proposal department can also make proposals based on the evaluation results. For example, the proposal department can make the optimal proposal based on the evaluation results. Furthermore, the proposal department can customize the proposal content and make specific proposals tailored to the characteristics and needs of the company. For example, the proposal department can provide proposal content tailored to the characteristics of the company. This allows the proposal department to confirm the validity of the proposal and realize effective collaboration. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to evaluate the effectiveness of the proposed collaboration.

[0038] The proposal department can make collaboration proposals based on evaluation results. For example, the proposal department can make optimal collaboration proposals based on evaluation results. The proposal department can also customize the proposals and make specific proposals tailored to the characteristics and needs of the company. For example, the proposal department can provide proposals tailored to the characteristics of the company. Furthermore, the proposal department can evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, the proposal department can evaluate the effectiveness of the proposals and make optimal proposals. This allows the proposal department to achieve more effective collaborations based on evaluation results. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to make collaboration proposals based on evaluation results.

[0039] The matching unit can match tourism and agricultural companies and propose tourist farms. For example, the matching unit can match tourism and agricultural companies and propose new business models. The matching unit can also propose tourist farms and demonstrate their effectiveness in attracting tourists and promoting agricultural product sales. For example, the matching unit can propose tourist farms and evaluate their effectiveness in attracting tourists. Furthermore, the matching unit can propose tourist farms and evaluate their effectiveness in promoting agricultural product sales. For example, the matching unit can propose tourist farms and evaluate their effectiveness in promoting agricultural product sales. In this way, the matching unit can match tourism and agricultural companies and propose new business models. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or not. For example, the matching unit can use generative AI to match tourism and agricultural companies and propose tourist farms.

[0040] The matching unit can match medical device manufacturers with IT companies and propose telemedicine systems. For example, the matching unit can match medical device manufacturers with IT companies and propose telemedicine systems. Furthermore, the matching unit can propose telemedicine systems and demonstrate improvements in access to medical care and cost reductions. For example, the matching unit can propose telemedicine systems and evaluate their effectiveness in improving access to medical care. Additionally, the matching unit can propose telemedicine systems and evaluate their cost reduction effects. Thus, the matching unit can match medical device manufacturers with IT companies and propose telemedicine systems. Some or all of the above-described processes in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to match medical device manufacturers with IT companies and propose telemedicine systems.

[0041] The data collection unit can collect data on complaints and concerns received by the call center in real time, enabling immediate response. For example, the data collection unit can collect complaints received by the call center in real time and immediately propose countermeasures. The data collection unit can also collect concerns received by the call center in real time and provide appropriate solutions. For example, the data collection unit can collect problems received by the call center in real time and respond quickly. This enables the data collection unit to collect data in real time and respond quickly. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can use generative AI to collect data on complaints and concerns received by the call center in real time, enabling immediate response.

[0042] The data collection unit can expand the types of data it collects and also collect feedback from social media and online forums. For example, the data collection unit can collect feedback from social media and reflect user opinions. It can also collect feedback from online forums and understand user needs. For example, the data collection unit can collect feedback from social media and online forums and use it to improve products and services. This allows the data collection unit to collect feedback from social media and online forums and gain a broader understanding of user opinions and needs. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can use generative AI to expand the types of data it collects and also collect feedback from social media and online forums.

[0043] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, the data collection unit can prioritize the collection of data related to problems occurring in a particular region. The data collection unit can also prioritize the collection of data from geographically close regions. For example, the data collection unit can prioritize the collection of data from geographically important regions. This allows the data collection unit to prioritize the collection of data from specific regions and respond quickly to region-specific problems. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to prioritize the collection of data from specific regions, taking geographical information into consideration.

[0044] The data collection unit can diversify the format of the data it collects and collect multimedia data such as text, audio, and images. For example, the data collection unit can collect text data and analyze user opinions. It can also collect audio data and reflect user voices. For example, the data collection unit can collect image data and obtain visual feedback from users. In this way, the data collection unit can collect multimedia data and understand user opinions and feedback from multiple perspectives. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to diversify the format of the data it collects and collect multimedia data such as text, audio, and images.

[0045] The analysis unit can analyze collected data along a time axis and identify trends and patterns. For example, the analysis unit can analyze past data along a time axis and identify trends. The analysis unit can also analyze data along a time axis and identify patterns. For example, the analysis unit can analyze data along a time axis and predict future trends. In this way, the analysis unit can identify trends and patterns through data analysis along a time axis and use this information to make future predictions. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to analyze collected data along a time axis and identify trends and patterns.

[0046] The analysis unit can visualize the analysis results so that users can understand them intuitively. For example, the analysis unit can visualize the analysis results in graphs and charts so that users can understand them intuitively. The analysis unit can also display the analysis results on a dashboard so that users can easily access them. For example, the analysis unit can display the analysis results in an interactive format so that users can access detailed information. In this way, the analysis unit makes it easier for users to understand the analysis results intuitively by visualizing them. 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 use generative AI to visualize the analysis results so that users can understand them intuitively.

[0047] The analysis unit can cross-reference data from different industries during analysis to gain new insights. For example, the analysis unit can cross-reference data from different industries to identify new trends. The analysis unit can also cross-reference data from different industries to identify new patterns. For example, the analysis unit can cross-reference data from different industries to gain new insights. Thus, the analysis unit can gain new insights by cross-referencing data from different industries. 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 use generative AI to cross-reference data from different industries during analysis to gain new insights.

[0048] The analysis unit can integrate analysis results with other systems and provide real-time feedback. For example, the analysis unit can integrate analysis results with other systems and provide real-time feedback. Furthermore, the analysis unit can integrate analysis results with other systems and provide real-time alerts. For example, the analysis unit can integrate analysis results with other systems and provide real-time reports. This enables the analysis unit to respond quickly by providing real-time feedback. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to integrate analysis results with other systems and provide real-time feedback.

[0049] The matching unit can select the most suitable partner by considering a company's past collaboration history. For example, the matching unit can analyze a company's past collaboration history to select the most suitable partner. The matching unit can also select a partner with a high success rate by considering a company's past collaboration history. For example, the matching unit can select a partner with high synergy effects based on a company's past collaboration history. In this way, the matching unit can select a partner with a high success rate by considering past collaboration history. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to select the most suitable partner by considering a company's past collaboration history.

[0050] The matching unit can simulate the matching results and evaluate the predicted effects in advance. For example, the matching unit can simulate the matching results and evaluate the predicted effects in advance. The matching unit can also simulate the matching results and evaluate the risks in advance. For example, the matching unit can simulate the matching results and evaluate the success rate in advance. In this way, the matching unit can evaluate the effects of matching in advance through simulation. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the matching unit can use a generative AI to simulate the matching results and evaluate the predicted effects in advance.

[0051] The matching unit can prioritize matching nearby companies, taking geographical factors into consideration. For example, the matching unit can prioritize matching companies that are geographically close. The matching unit can also prioritize matching companies in geographically important regions. For example, the matching unit considers geographical factors and matches the most suitable company. This allows the matching unit to respond quickly to region-specific issues by considering geographical factors. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to prioritize matching nearby companies, taking geographical factors into consideration.

[0052] The matching unit can integrate matching results with other systems and immediately propose collaborations. For example, the matching unit can integrate matching results with other systems and immediately propose collaborations. Furthermore, the matching unit can integrate matching results with other systems and immediately provide feedback. For example, the matching unit can integrate matching results with other systems and immediately provide reports. This enables the matching unit to make rapid collaboration proposals by integrating with other systems. Some or all of the above-described processes in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to integrate matching results with other systems and immediately propose collaborations.

[0053] The proposal department can optimize its proposals by referring to past success stories. For example, the proposal department can optimize its proposals by referring to past success stories. Furthermore, the proposal department can customize its proposals based on past success stories. For example, the proposal department can analyze past success stories and provide the most effective proposals. This improves the accuracy of the proposals by referring to past success stories. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can use generative AI to optimize its proposals by referring to past success stories.

[0054] The proposal department can customize proposals and provide specific proposals tailored to the characteristics and needs of each company. For example, the proposal department can provide proposals tailored to the characteristics of each company. Furthermore, the proposal department can customize proposals based on the needs of each company. For example, the proposal department can consider the characteristics and needs of each company and provide the most suitable proposal. This allows the proposal department to provide proposals tailored to the characteristics and needs of each company, thereby enabling more effective collaboration. Some or all of the above-described processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can use generative AI to customize proposals and provide specific proposals tailored to the characteristics and needs of each company.

[0055] The proposal department can make proposals multilingual and promote international collaboration. For example, the proposal department can make proposals multilingual and submit them to international companies. The proposal department can also make proposals multilingual and promote collaboration between companies that speak different languages. For example, the proposal department can make proposals multilingual and enhance competitiveness in the international market. In this way, the proposal department promotes international collaboration by making proposals multilingual. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to make proposals multilingual and promote international collaboration.

[0056] The proposal department can integrate the proposal content with other systems and receive feedback in real time. For example, the proposal department can integrate the proposal content with other systems and receive feedback in real time. The proposal department can also integrate the proposal content with other systems and receive alerts in real time. For example, the proposal department can integrate the proposal content with other systems and receive reports in real time. In this way, the proposal department can receive feedback in real time by integrating with other systems. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to integrate the proposal content with other systems and receive feedback in real time.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, the data collection unit can prioritize the collection of data related to problems occurring in a particular region. The data collection unit can also prioritize the collection of data from geographically close regions. For example, the data collection unit can prioritize the collection of data from geographically important regions. This allows the data collection unit to prioritize the collection of data from specific regions and respond quickly to region-specific problems. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to prioritize the collection of data from specific regions, taking geographical information into consideration.

[0059] The analysis unit can visualize the analysis results so that users can understand them intuitively. For example, the analysis unit can visualize the analysis results in graphs and charts so that users can understand them intuitively. The analysis unit can also display the analysis results on a dashboard so that users can easily access them. For example, the analysis unit can display the analysis results in an interactive format so that users can access detailed information. In this way, the analysis unit makes it easier for users to understand the analysis results intuitively by visualizing them. 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 use generative AI to visualize the analysis results so that users can understand them intuitively.

[0060] The matching unit can select the most suitable partner by considering a company's past collaboration history. For example, the matching unit can analyze a company's past collaboration history to select the most suitable partner. The matching unit can also select a partner with a high success rate by considering a company's past collaboration history. For example, the matching unit can select a partner with high synergy effects based on a company's past collaboration history. In this way, the matching unit can select a partner with a high success rate by considering past collaboration history. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to select the most suitable partner by considering a company's past collaboration history.

[0061] The proposal department can optimize its proposals by referring to past success stories. For example, the proposal department can optimize its proposals by referring to past success stories. Furthermore, the proposal department can customize its proposals based on past success stories. For example, the proposal department can analyze past success stories and provide the most effective proposals. This improves the accuracy of the proposals by referring to past success stories. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can use generative AI to optimize its proposals by referring to past success stories.

[0062] The proposal department can make proposals multilingual and promote international collaboration. For example, the proposal department can make proposals multilingual and submit them to international companies. The proposal department can also make proposals multilingual and promote collaboration between companies that speak different languages. For example, the proposal department can make proposals multilingual and enhance competitiveness in the international market. In this way, the proposal department promotes international collaboration by making proposals multilingual. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to make proposals multilingual and promote international collaboration.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects data. The data collection unit can collect information such as business overviews, service / product overviews, geography, and locations of all publicly available industries and sectors. The data collection unit can also collect data on complaints, concerns, and problems received by call centers. For example, the data collection unit stores complaints received by call centers in a database and provides it to the analysis unit. Furthermore, the data collection unit can also collect feedback from social media and online forums. For example, the data collection unit analyzes social media posts and collects user opinions. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the collected data along a time axis to identify trends and patterns. For example, the analysis unit can analyze historical data to predict future trends. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, the analysis unit can analyze data from different industries to identify new business opportunities. Step 3: The matching unit creates a matching list based on the analysis results obtained by the analysis unit. The matching unit can create matching lists based on the analysis results, for example, in order of usefulness, synergy effect, feasibility, or what would be interesting. The matching unit can also select the optimal partner by considering the past collaboration history of companies. For example, the matching unit can analyze past collaboration history and select partners with a high success rate. Furthermore, the matching unit can simulate the matching results and evaluate the expected effects in advance. For example, the matching unit can perform a simulation to evaluate the effectiveness of the matching. Step 4: The Proposal Department makes collaboration proposals based on the matching list created by the Matching Department. The Proposal Department can, for example, propose collaborations to specific products or companies. The Proposal Department can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, the Proposal Department evaluates the effectiveness of the proposals and makes the most suitable proposals. Furthermore, the Proposal Department can customize the proposals and make specific proposals tailored to the characteristics and needs of the companies. For example, the Proposal Department provides proposals tailored to the characteristics of the companies.

[0065] (Example of form 2) The support system according to an embodiment of the present invention is a system for small and medium-sized enterprises to solve problems through product and service collaboration. This support system uses generative AI to match candidate companies and products and provides comprehensive support from collaboration proposals to product and service development. First, the generative AI takes in publicly available information such as business overviews, service and product overviews, geography, and locations for all industries and sectors, and creates a matching list sorted by usefulness, synergy effect, feasibility, and potential interest. Next, the generative AI proposes collaborations to the candidate companies. These proposals include information such as how each problem will be solved, social contribution, synergy effect, feasibility, and potential interest. Furthermore, the generative AI takes in complaints, concerns, and problems received by call centers and combines them with publicly available product, service, and company information, production location data, etc., to find products and companies that can solve the problems. Based on this information, the generative AI proposes collaborations to the candidate companies and products and provides comprehensive support from matching to product and service development. This system enables small and medium-sized enterprises (SMEs) to overcome challenges such as personnel shortages, idea generation, research, and uncertainty about projected effects, and to create new products and services through collaboration. Furthermore, by utilizing generative AI, it becomes possible to materialize collaborative ideas that humans might not conceive, and release products and services that benefit people. For example, the generative AI can match tourism and agricultural companies and propose tourist farms. This proposal would include effects such as attracting tourists and promoting the sale of agricultural products. The generative AI can also match medical device manufacturers and IT companies and propose telemedicine systems. This proposal would include effects such as improving access to medical care and reducing costs. In this way, the present invention contributes to the development of society as a whole by supporting collaboration among SMEs using generative AI and creating new products and services. As a result, the support system can efficiently support collaboration among SMEs and create new products and services.

[0066] The support system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit can collect information such as business overviews, service / product overviews, geography, and locations of all publicly available industries and sectors. The data collection unit can also collect data on complaints, concerns, and problems received by call centers. For example, the data collection unit stores complaints received by call centers in a database and provides it to the analysis unit. Furthermore, the data collection unit can also collect feedback from social media and online forums. For example, the data collection unit analyzes social media posts and collects user opinions. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. Furthermore, the analysis unit can analyze the collected data along a time axis to identify trends and patterns. For example, the analysis unit analyzes past data to predict future trends. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, the analysis unit analyzes data from different industries to identify new business opportunities. The matching unit creates a matching list based on the analysis results obtained by the analysis unit. For example, the matching unit can create matching lists based on the analysis results, sorted by usefulness, synergy effect, feasibility, or potential interest. The matching unit can also select the most suitable partner by considering a company's past collaboration history. For example, it can analyze past collaboration history and select partners with a high success rate. Furthermore, the matching unit can simulate matching results and evaluate the expected effects in advance. For example, it can perform simulations to evaluate the effectiveness of the matching. The proposal unit makes collaboration proposals based on the matching list created by the matching unit. For example, the proposal unit can propose collaborations to specific products or companies. The proposal unit can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results.For example, the proposal department evaluates the effectiveness of a proposal and makes the optimal proposal. Furthermore, the proposal department can customize the proposal content and make specific proposals tailored to the characteristics and needs of a company. For example, the proposal department provides proposal content tailored to the characteristics of a company. In this way, the support system according to the embodiment can efficiently support collaboration among small and medium-sized enterprises. Some or all of the above-described processing in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can use generative AI to make collaboration proposals to specific products or companies.

[0067] The data collection unit collects data. For example, the data collection unit can collect information such as business overviews, service and product overviews, geography, and locations of all publicly available industry and sector information. Specifically, it collects information from publicly available databases on the internet, official company websites, and industry reports. This allows the data collection unit to comprehensively understand the business content, services, product details, and geographical location information of each company. The data collection unit can also collect data on complaints, concerns, and problems received by call centers. For example, complaints and inquiries received by call center operators are recorded in a database and retrieved by the data collection unit. This allows for a detailed understanding of the problems and dissatisfactions customers have. Furthermore, the data collection unit can also collect feedback from social media and online forums. For example, it analyzes social media posts and collects user opinions and impressions. This includes using natural language processing technology to analyze the content of posts and classify positive and negative opinions. This allows the data collection unit to obtain real-time customer feedback and understand the evaluation of a company's services and products. The data collection unit can gather information from these diverse data sources and manage it centrally, enabling a comprehensive understanding of the company's current situation and market trends.

[0068] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. Specifically, it can use statistical analysis to reveal the distribution and correlation of data, and machine learning algorithms to extract patterns and trends from the data. For example, it can analyze collected call center complaint data to identify trends in dissatisfaction with specific products or services. The analysis unit can also analyze the collected data along a time axis to identify trends and patterns. For example, it can analyze data from the past few years to understand seasonal sales fluctuations and changes in customer interest at specific times. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, it can compare data from different industries to identify common challenges and success stories and discover new business opportunities. Based on these analysis results, the analysis unit can provide useful information to support corporate strategy planning and decision-making.

[0069] The matching department creates matching lists based on the analysis results obtained by the analysis department. For example, the matching department can create matching lists in order of usefulness, synergy effect, feasibility, or potential interest based on the analysis results. Specifically, based on the data provided by the analysis department, it selects the most suitable partners considering the characteristics and needs of each company. For example, if a company needs technical support when developing a new product, it will prioritize including companies with high technical capabilities in the matching list. The matching department can also select the most suitable partners by considering a company's past collaboration history. For example, it can analyze the history of successful past collaborations and select partners who are likely to achieve similar success. Furthermore, the matching department can simulate matching results and evaluate the expected effects in advance. For example, it can conduct simulations to evaluate the economic effects and market impact of a particular partnership. This allows the matching department to propose the most effective partnerships for companies.

[0070] The Proposal Department makes collaboration proposals based on the matching list created by the Matching Department. For example, the Proposal Department can propose collaborations to specific products or companies. Specifically, it creates proposals tailored to the characteristics and needs of the companies, and presents specific collaboration methods and expected effects. The Proposal Department can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, it can simulate the effects of the proposals and select the optimal proposal. Furthermore, the Proposal Department can customize the proposals and make specific proposals tailored to the characteristics and needs of the companies. For example, it can provide proposals tailored to the characteristics of the companies. This allows the Proposal Department to propose the most effective collaborations for companies and increase their feasibility. Some or all of the above processes in the Proposal Department may be performed using, for example, generative AI, or not. For example, the Proposal Department can use generative AI to propose collaborations to specific products or companies. The generative AI generates optimal proposals based on past data and trends and provides them to companies. This allows the Proposal Department to make proposals quickly and effectively.

[0071] The data collection unit can collect data on complaints, concerns, and problems received by the call center. For example, the data collection unit can store complaints received by the call center in a database and provide it to the analysis unit. The data collection unit can also collect concerns received by the call center and provide appropriate solutions. For example, the data collection unit can collect problems received by the call center in real time and respond quickly. Furthermore, the data collection unit can analyze the data received by the call center and quickly understand the user's problems. This allows the data collection unit to quickly understand and respond to the user's problems. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to collect data on complaints and concerns received by the call center.

[0072] The analysis unit can analyze the collected data and identify products and companies that can solve the problem. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. The analysis unit can also analyze the collected data along a time axis to identify trends and patterns. For example, the analysis unit can analyze past data to predict future trends. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, the analysis unit can analyze data from different industries to identify new business opportunities. This allows the analysis unit to identify appropriate products and companies and support problem solving. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not. For example, the analysis unit can use generative AI to analyze the collected data and identify products and companies that can solve the problem.

[0073] The proposal department can propose collaborations to specific products or companies. For example, the proposal department can propose collaborations to specific products or companies. The proposal department can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, the proposal department can evaluate the effectiveness of the proposals and make the optimal proposals. Furthermore, the proposal department can customize the proposals and make specific proposals tailored to the characteristics and needs of the companies. For example, the proposal department can provide proposals tailored to the characteristics of the companies. This enables the proposal department to achieve effective collaborations. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to propose collaborations to specific products or companies.

[0074] The proposal department can evaluate the effectiveness of the proposed collaboration. For example, the proposal department can evaluate the effectiveness of the proposed collaboration and confirm the validity of the proposal. The proposal department can also make proposals based on the evaluation results. For example, the proposal department can make the optimal proposal based on the evaluation results. Furthermore, the proposal department can customize the proposal content and make specific proposals tailored to the characteristics and needs of the company. For example, the proposal department can provide proposal content tailored to the characteristics of the company. This allows the proposal department to confirm the validity of the proposal and realize effective collaboration. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to evaluate the effectiveness of the proposed collaboration.

[0075] The proposal department can make collaboration proposals based on evaluation results. For example, the proposal department can make optimal collaboration proposals based on evaluation results. The proposal department can also customize the proposals and make specific proposals tailored to the characteristics and needs of the company. For example, the proposal department can provide proposals tailored to the characteristics of the company. Furthermore, the proposal department can evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, the proposal department can evaluate the effectiveness of the proposals and make optimal proposals. This allows the proposal department to achieve more effective collaborations based on evaluation results. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to make collaboration proposals based on evaluation results.

[0076] The matching unit can match tourism and agricultural companies and propose tourist farms. For example, the matching unit can match tourism and agricultural companies and propose new business models. The matching unit can also propose tourist farms and demonstrate their effectiveness in attracting tourists and promoting agricultural product sales. For example, the matching unit can propose tourist farms and evaluate their effectiveness in attracting tourists. Furthermore, the matching unit can propose tourist farms and evaluate their effectiveness in promoting agricultural product sales. For example, the matching unit can propose tourist farms and evaluate their effectiveness in promoting agricultural product sales. In this way, the matching unit can match tourism and agricultural companies and propose new business models. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or not. For example, the matching unit can use generative AI to match tourism and agricultural companies and propose tourist farms.

[0077] The matching unit can match medical device manufacturers with IT companies and propose telemedicine systems. For example, the matching unit can match medical device manufacturers with IT companies and propose telemedicine systems. Furthermore, the matching unit can propose telemedicine systems and demonstrate improvements in access to medical care and cost reductions. For example, the matching unit can propose telemedicine systems and evaluate their effectiveness in improving access to medical care. Additionally, the matching unit can propose telemedicine systems and evaluate their cost reduction effects. Thus, the matching unit can match medical device manufacturers with IT companies and propose telemedicine systems. Some or all of the above-described processes in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to match medical device manufacturers with IT companies and propose telemedicine systems.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to gather more detailed information. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to quickly obtain information. This allows the data collection unit to adjust the timing of data collection based on the user's emotions, reducing the user's burden and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine 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 data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can use generative AI to estimate the user's emotions and adjust the timing of data collection based on the estimated emotions.

[0079] The data collection unit can collect data on complaints and concerns received by the call center in real time, enabling immediate response. For example, the data collection unit can collect complaints received by the call center in real time and immediately propose countermeasures. The data collection unit can also collect concerns received by the call center in real time and provide appropriate solutions. For example, the data collection unit can collect problems received by the call center in real time and respond quickly. This enables the data collection unit to collect data in real time and respond quickly. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can use generative AI to collect data on complaints and concerns received by the call center in real time, enabling immediate response.

[0080] The data collection unit can expand the types of data it collects and also collect feedback from social media and online forums. For example, the data collection unit can collect feedback from social media and reflect user opinions. It can also collect feedback from online forums and understand user needs. For example, the data collection unit can collect feedback from social media and online forums and use it to improve products and services. This allows the data collection unit to collect feedback from social media and online forums and gain a broader understanding of user opinions and needs. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can use generative AI to expand the types of data it collects and also collect feedback from social media and online forums.

[0081] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. Conversely, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This allows the data collection unit to prioritize data based on the user's emotions and prioritize the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can use generative AI to estimate the user's emotions and prioritize the data to collect based on the estimated emotions.

[0082] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, the data collection unit can prioritize the collection of data related to problems occurring in a particular region. The data collection unit can also prioritize the collection of data from geographically close regions. For example, the data collection unit can prioritize the collection of data from geographically important regions. This allows the data collection unit to prioritize the collection of data from specific regions and respond quickly to region-specific problems. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to prioritize the collection of data from specific regions, taking geographical information into consideration.

[0083] The data collection unit can diversify the format of the data it collects and collect multimedia data such as text, audio, and images. For example, the data collection unit can collect text data and analyze user opinions. It can also collect audio data and reflect user voices. For example, the data collection unit can collect image data and obtain visual feedback from users. In this way, the data collection unit can collect multimedia data and understand user opinions and feedback from multiple perspectives. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to diversify the format of the data it collects and collect multimedia data such as text, audio, and images.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. Conversely, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. For example, if the user is in a hurry, the analysis unit can speed up the analysis algorithm and provide results quickly. This allows the analysis unit to adjust the analysis algorithm based on the user's emotions and provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions.

[0085] The analysis unit can analyze collected data along a time axis and identify trends and patterns. For example, the analysis unit can analyze past data along a time axis and identify trends. The analysis unit can also analyze data along a time axis and identify patterns. For example, the analysis unit can analyze data along a time axis and predict future trends. In this way, the analysis unit can identify trends and patterns through data analysis along a time axis and use this information to make future predictions. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to analyze collected data along a time axis and identify trends and patterns.

[0086] The analysis unit can visualize the analysis results so that users can understand them intuitively. For example, the analysis unit can visualize the analysis results in graphs and charts so that users can understand them intuitively. The analysis unit can also display the analysis results on a dashboard so that users can easily access them. For example, the analysis unit can display the analysis results in an interactive format so that users can access detailed information. In this way, the analysis unit makes it easier for users to understand the analysis results intuitively by visualizing them. 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 use generative AI to visualize the analysis results so that users can understand them intuitively.

[0087] 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 can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the analysis unit to adjust the display method based on the user's emotions, enabling a display that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions.

[0088] The analysis unit can cross-reference data from different industries during analysis to gain new insights. For example, the analysis unit can cross-reference data from different industries to identify new trends. The analysis unit can also cross-reference data from different industries to identify new patterns. For example, the analysis unit can cross-reference data from different industries to gain new insights. Thus, the analysis unit can gain new insights by cross-referencing data from different industries. 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 use generative AI to cross-reference data from different industries during analysis to gain new insights.

[0089] The analysis unit can integrate analysis results with other systems and provide real-time feedback. For example, the analysis unit can integrate analysis results with other systems and provide real-time feedback. Furthermore, the analysis unit can integrate analysis results with other systems and provide real-time alerts. For example, the analysis unit can integrate analysis results with other systems and provide real-time reports. This enables the analysis unit to respond quickly by providing real-time feedback. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to integrate analysis results with other systems and provide real-time feedback.

[0090] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit may use simple matching criteria. Alternatively, if the user is relaxed, it may use more detailed matching criteria. For example, if the user is in a hurry, the matching unit may use criteria for quick matching. This allows the matching unit to adjust the matching criteria based on the user's emotions, enabling more appropriate matching. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or not. For example, the matching unit can use generative AI to estimate the user's emotions and adjust the matching criteria based on the estimated emotions.

[0091] The matching unit can select the most suitable partner by considering a company's past collaboration history. For example, the matching unit can analyze a company's past collaboration history to select the most suitable partner. The matching unit can also select a partner with a high success rate by considering a company's past collaboration history. For example, the matching unit can select a partner with high synergy effects based on a company's past collaboration history. In this way, the matching unit can select a partner with a high success rate by considering past collaboration history. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to select the most suitable partner by considering a company's past collaboration history.

[0092] The matching unit can simulate the matching results and evaluate the predicted effects in advance. For example, the matching unit can simulate the matching results and evaluate the predicted effects in advance. The matching unit can also simulate the matching results and evaluate the risks in advance. For example, the matching unit can simulate the matching results and evaluate the success rate in advance. In this way, the matching unit can evaluate the effects of matching in advance through simulation. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the matching unit can use a generative AI to simulate the matching results and evaluate the predicted effects in advance.

[0093] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated emotions. For example, if the user is nervous, the matching unit can provide a simple and highly visible display order. Alternatively, if the user is relaxed, it can provide a display order that includes detailed information. For example, if the user is in a hurry, the matching unit can provide a concise display order. This allows the matching unit to adjust the display order based on the user's emotions, enabling a display that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to estimate the user's emotions and adjust the display order of the matching results based on the estimated emotions.

[0094] The matching unit can prioritize matching nearby companies, taking geographical factors into consideration. For example, the matching unit can prioritize matching companies that are geographically close. The matching unit can also prioritize matching companies in geographically important regions. For example, the matching unit considers geographical factors and matches the most suitable company. This allows the matching unit to respond quickly to region-specific issues by considering geographical factors. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to prioritize matching nearby companies, taking geographical factors into consideration.

[0095] The matching unit can integrate matching results with other systems and immediately propose collaborations. For example, the matching unit can integrate matching results with other systems and immediately propose collaborations. Furthermore, the matching unit can integrate matching results with other systems and immediately provide feedback. For example, the matching unit can integrate matching results with other systems and immediately provide reports. This enables the matching unit to make rapid collaboration proposals by integrating with other systems. Some or all of the above-described processes in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to integrate matching results with other systems and immediately propose collaborations.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to adjust the way it presents suggestions based on the user's emotions, enabling suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine 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 processing described above in the suggestion unit may be performed using, for example, generative AI, or not. For example, the suggestion unit can use generative AI to estimate the user's emotions and adjust the way it presents suggestions based on those estimated emotions.

[0097] The proposal department can optimize its proposals by referring to past success stories. For example, the proposal department can optimize its proposals by referring to past success stories. Furthermore, the proposal department can customize its proposals based on past success stories. For example, the proposal department can analyze past success stories and provide the most effective proposals. This improves the accuracy of the proposals by referring to past success stories. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can use generative AI to optimize its proposals by referring to past success stories.

[0098] The proposal department can customize proposals and provide specific proposals tailored to the characteristics and needs of each company. For example, the proposal department can provide proposals tailored to the characteristics of each company. Furthermore, the proposal department can customize proposals based on the needs of each company. For example, the proposal department can consider the characteristics and needs of each company and provide the most suitable proposal. This allows the proposal department to provide proposals tailored to the characteristics and needs of each company, thereby enabling more effective collaboration. Some or all of the above-described processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can use generative AI to customize proposals and provide specific proposals tailored to the characteristics and needs of each company.

[0099] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize providing high-priority suggestions. Conversely, if the user is relaxed, the suggestion unit may prioritize providing detailed suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize providing suggestions that can be delivered quickly. This allows the suggestion unit to prioritize suggestions based on the user's emotions and prioritize important suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or not. For example, the suggestion unit can use generative AI to estimate the user's emotions and prioritize suggestions based on those estimated emotions.

[0100] The proposal department can make proposals multilingual and promote international collaboration. For example, the proposal department can make proposals multilingual and submit them to international companies. The proposal department can also make proposals multilingual and promote collaboration between companies that speak different languages. For example, the proposal department can make proposals multilingual and enhance competitiveness in the international market. In this way, the proposal department promotes international collaboration by making proposals multilingual. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to make proposals multilingual and promote international collaboration.

[0101] The proposal department can integrate the proposal content with other systems and receive feedback in real time. For example, the proposal department can integrate the proposal content with other systems and receive feedback in real time. The proposal department can also integrate the proposal content with other systems and receive alerts in real time. For example, the proposal department can integrate the proposal content with other systems and receive reports in real time. In this way, the proposal department can receive feedback in real time by integrating with other systems. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to integrate the proposal content with other systems and receive feedback in real time.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to gather more detailed information. For example, if the user is in a hurry, the data collection unit can shorten the timing of data collection to quickly obtain information. This allows the data collection unit to adjust the timing of data collection based on the user's emotions, reducing the user's burden and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine 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 data collection unit may be performed using, for example, generative AI, or not. For example, the data collection unit can use generative AI to estimate the user's emotions and adjust the timing of data collection based on the estimated emotions.

[0104] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. Conversely, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. For example, if the user is in a hurry, the analysis unit can speed up the analysis algorithm and provide results quickly. This allows the analysis unit to adjust the analysis algorithm based on the user's emotions and provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions.

[0105] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit may use simple matching criteria. Alternatively, if the user is relaxed, it may use more detailed matching criteria. For example, if the user is in a hurry, the matching unit may use criteria for quick matching. This allows the matching unit to adjust the matching criteria based on the user's emotions, enabling more appropriate matching. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or not. For example, the matching unit can use generative AI to estimate the user's emotions and adjust the matching criteria based on the estimated emotions.

[0106] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to adjust the way it presents suggestions based on the user's emotions, enabling suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine 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 processing described above in the suggestion unit may be performed using, for example, generative AI, or not. For example, the suggestion unit can use generative AI to estimate the user's emotions and adjust the way it presents suggestions based on those estimated emotions.

[0107] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize providing high-priority suggestions. Conversely, if the user is relaxed, the suggestion unit may prioritize providing detailed suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize providing suggestions that can be delivered quickly. This allows the suggestion unit to prioritize suggestions based on the user's emotions and prioritize important suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or not. For example, the suggestion unit can use generative AI to estimate the user's emotions and prioritize suggestions based on those estimated emotions.

[0108] The data collection unit can prioritize the collection of data from specific regions, taking geographical information into consideration. For example, the data collection unit can prioritize the collection of data related to problems occurring in a particular region. The data collection unit can also prioritize the collection of data from geographically close regions. For example, the data collection unit can prioritize the collection of data from geographically important regions. This allows the data collection unit to prioritize the collection of data from specific regions and respond quickly to region-specific problems. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to prioritize the collection of data from specific regions, taking geographical information into consideration.

[0109] The analysis unit can visualize the analysis results so that users can understand them intuitively. For example, the analysis unit can visualize the analysis results in graphs and charts so that users can understand them intuitively. The analysis unit can also display the analysis results on a dashboard so that users can easily access them. For example, the analysis unit can display the analysis results in an interactive format so that users can access detailed information. In this way, the analysis unit makes it easier for users to understand the analysis results intuitively by visualizing them. 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 use generative AI to visualize the analysis results so that users can understand them intuitively.

[0110] The matching unit can select the most suitable partner by considering a company's past collaboration history. For example, the matching unit can analyze a company's past collaboration history to select the most suitable partner. The matching unit can also select a partner with a high success rate by considering a company's past collaboration history. For example, the matching unit can select a partner with high synergy effects based on a company's past collaboration history. In this way, the matching unit can select a partner with a high success rate by considering past collaboration history. Some or all of the above processing in the matching unit may be performed using, for example, generative AI, or without generative AI. For example, the matching unit can use generative AI to select the most suitable partner by considering a company's past collaboration history.

[0111] The proposal department can optimize its proposals by referring to past success stories. For example, the proposal department can optimize its proposals by referring to past success stories. Furthermore, the proposal department can customize its proposals based on past success stories. For example, the proposal department can analyze past success stories and provide the most effective proposals. This improves the accuracy of the proposals by referring to past success stories. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can use generative AI to optimize its proposals by referring to past success stories.

[0112] The proposal department can make proposals multilingual and promote international collaboration. For example, the proposal department can make proposals multilingual and submit them to international companies. The proposal department can also make proposals multilingual and promote collaboration between companies that speak different languages. For example, the proposal department can make proposals multilingual and enhance competitiveness in the international market. In this way, the proposal department promotes international collaboration by making proposals multilingual. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can use generative AI to make proposals multilingual and promote international collaboration.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects data. The data collection unit can collect information such as business overviews, service / product overviews, geography, and locations of all publicly available industries and sectors. The data collection unit can also collect data on complaints, concerns, and problems received by call centers. For example, the data collection unit stores complaints received by call centers in a database and provides it to the analysis unit. Furthermore, the data collection unit can also collect feedback from social media and online forums. For example, the data collection unit analyzes social media posts and collects user opinions. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the collected data along a time axis to identify trends and patterns. For example, the analysis unit can analyze historical data to predict future trends. Furthermore, the analysis unit can cross-reference data from different industries to gain new insights. For example, the analysis unit can analyze data from different industries to identify new business opportunities. Step 3: The matching unit creates a matching list based on the analysis results obtained by the analysis unit. The matching unit can create matching lists based on the analysis results, for example, in order of usefulness, synergy effect, feasibility, or what would be interesting. The matching unit can also select the optimal partner by considering the past collaboration history of companies. For example, the matching unit can analyze past collaboration history and select partners with a high success rate. Furthermore, the matching unit can simulate the matching results and evaluate the expected effects in advance. For example, the matching unit can perform a simulation to evaluate the effectiveness of the matching. Step 4: The Proposal Department makes collaboration proposals based on the matching list created by the Matching Department. The Proposal Department can, for example, propose collaborations to specific products or companies. The Proposal Department can also evaluate the effectiveness of the proposed collaborations and make proposals based on the evaluation results. For example, the Proposal Department evaluates the effectiveness of the proposals and makes the most suitable proposals. Furthermore, the Proposal Department can customize the proposals and make specific proposals tailored to the characteristics and needs of the companies. For example, the Proposal Department provides proposals tailored to the characteristics of the companies.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates a matching list based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes collaboration proposals based on the matching list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The matching unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and creates a matching list based on the analysis results. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and makes collaboration proposals based on the matching list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates a matching list based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes collaboration proposals based on the matching list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates a matching list based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes collaboration proposals based on the matching list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A matching unit creates a matching list based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that makes collaboration proposals based on the matching list created by the matching unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on complaints, concerns, and problems received by the call center. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data to identify products and companies that can solve the problem. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose collaborations to specific products and companies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Evaluate the effectiveness of the proposed collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We will propose collaborations based on the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The matching unit is We match tourism and agricultural businesses and propose tourist farms. The system described in Appendix 1, characterized by the features described herein. (Note 8) The matching unit is We match medical device manufacturers with IT companies and propose telemedicine systems. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is By collecting data on complaints and concerns received by the call center in real time, immediate responses become possible. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Expand the types of data collected, including feedback from social media and online forums. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Considering geographical information, prioritize the collection of data from specific regions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Diversifying the formats of data collected, including multimedia data such as text, audio, and images. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The collected data is analyzed along a time axis to identify trends and patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Visualize the analysis results so that users can understand them intuitively. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, During analysis, cross-reference data from different industries to gain new insights. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The analysis results are integrated with other systems to provide real-time feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is We select the optimal partner by considering the company's past collaboration history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is The matching results are simulated, and the expected effects are evaluated in advance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is It estimates the user's emotions and adjusts the display order of matching results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is Taking geographical factors into consideration, we prioritize matching with nearby companies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is The matching results are integrated with other systems to immediately propose collaborations. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The aforementioned proposal section is, When making a proposal, we optimize the proposal content by referring to past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We customize our proposals and provide specific suggestions tailored to the characteristics and needs of each company. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, Make the proposal multilingual and promote international collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, The proposed content is integrated with other systems, and feedback is received in real time. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A matching unit creates a matching list based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that makes collaboration proposals based on the matching list created by the matching unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on complaints, concerns, and problems received by the call center. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data to identify products and companies that can solve the problem. The system according to feature 1.

4. The aforementioned proposal section is, We propose collaborations to specific products and companies. The system according to feature 1.

5. The aforementioned proposal section is, Evaluate the effectiveness of the proposed collaboration. The system according to feature 1.

6. The aforementioned proposal section is, We will propose collaborations based on the evaluation results. The system according to feature 1.

7. The matching unit is We match tourism and agricultural businesses and propose tourist farms. The system according to feature 1.

8. The matching unit is We match medical device manufacturers with IT companies and propose telemedicine systems. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is By collecting data on complaints and concerns received by the call center in real time, immediate responses become possible. The system according to feature 1.

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