system

Abstract

The system according to this embodiment aims to comprehensively solve the problems of labor shortages, declining profits, and stagnation in technology transfer among small and medium-sized enterprises. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a sales unit, and a management unit. The data collection unit collects technical data from companies. The analysis unit analyzes the technical data collected by the data collection unit and identifies technological synergies. The proposal unit makes market proposals based on the technological synergies identified by the analysis unit. The sales unit conducts automated sales based on the proposals made by the proposal unit. The management unit manages technology transfer based on the results obtained by the sales unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there are problems such as labor shortages, low profits, and stagnation of technology inheritance faced by small and medium-sized enterprises, and there is a lack of means to solve these problems collectively.

[0005] The system according to the embodiment aims to solve collectively the labor shortage, low profits, and stagnation of technology inheritance of small and medium-sized enterprises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a sales unit, and a management unit. The data collection unit collects technical data from companies. The analysis unit analyzes the technical data collected by the data collection unit and identifies technological synergies. The proposal unit makes market proposals based on the technological synergies identified by the analysis unit. The sales unit conducts automated sales based on the proposals made by the proposal unit. The management unit manages technology transfer based on the results obtained by the sales unit. [Effects of the Invention]

[0007] The system according to this embodiment can simultaneously solve the problems of labor shortages, declining profits, and stagnation in technology transfer faced by small and medium-sized enterprises. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes an AI agent to solve the three major challenges faced by small and medium-sized enterprises (SMEs): "labor shortage," "stagnant profits," and "stagnation of technology transfer." This system improves profitability and sustainability by collecting corporate technology data, discovering technological synergies, making market proposals, conducting automated sales, and managing technology transfer. For example, the system analyzes a company's technologies and discovers technological synergies. Next, it makes market proposals based on the discovered technological synergies and creates efficient revenue opportunities through automated sales functions. It also manages technology transfer, quantifies technological capabilities by introducing a transfer index, and improves corporate value. Furthermore, by visualizing corporate value, it promotes investment and provides a base for new partnerships. This has a high ripple effect on the regional economy and is an initiative to rebuild the competitiveness of domestic companies. For example, the system collects and analyzes corporate technology data. For example, by fusing the technologies of companies with different technologies, it can make new market proposals. This allows companies to gain new business opportunities. Next, it makes market proposals based on the discovered technological synergies and creates efficient revenue opportunities through automated sales functions. The system supports a company's sales activities by automatically creating proposal materials and acting as a sales representative. For example, the system automatically selects target companies, creates proposal materials, and conducts sales activities, thereby streamlining the company's sales process. Furthermore, it implements technology transfer management and quantifies technological capabilities by introducing a transfer index, thereby improving corporate value. The system digitizes technology data and manages the progress of transfer, making the technology transfer process visible. For example, digitizing technology data prevents technology from becoming dependent on individuals and allows for real-time monitoring of the transfer progress. In addition, by visualizing corporate value, it provides a platform for promoting investment and new partnerships. The system quantifies a company's technological capabilities and profitability, making corporate value visible. This allows companies to appeal to investors and partner companies, and is expected to promote investment and partnerships. In this way, by utilizing the system, the three major challenges faced by small and medium-sized enterprises—"labor shortage," "stagnation of profits," and "stagnation of technology transfer"—can be solved all at once, improving the profitability and sustainability of companies.Furthermore, it will have a significant ripple effect on the regional economy and will contribute to rebuilding the competitiveness of domestic companies. As a result, the system can comprehensively address the challenges faced by small and medium-sized enterprises, improving their profitability and sustainability.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a sales unit, and a management unit. The data collection unit collects corporate technical data. The data collection unit can collect, for example, corporate patent information, research papers, and technical reports. The data collection unit can, for example, build a system that automatically collects corporate technical data. The data collection unit can also manually collect corporate technical data. For example, the data collection unit can provide a dedicated interface for collecting corporate technical data. The analysis unit analyzes the technical data collected by the data collection unit and discovers technological synergies. The analysis unit can, for example, use AI to analyze the technical data and discover technological synergies. The analysis unit can, for example, analyze the interactions of technical data and discover new technological synergies. The analysis unit can also analyze the integrated effects of technical data and discover technological synergies. For example, the analysis unit can use a dedicated algorithm for analyzing technical data. The proposal unit makes market proposals based on the technological synergies discovered by the analysis unit. The proposal unit can, for example, use AI to make market proposals. The proposal department can, for example, create proposals based on discovered technological synergies. The proposal department can also create presentation materials based on discovered technological synergies. For example, the proposal department can use specialized tools for making market proposals. The sales department conducts automated sales based on the proposals made by the proposal department. The sales department can, for example, use AI for automated sales. The sales department can, for example, automatically create proposal materials. The sales department can also perform sales outsourcing. For example, the sales department can build a specialized system for automated sales. The management department manages technology transfer based on the results obtained by the sales department. The management department can, for example, use AI for technology transfer management. The management department can, for example, digitize technology data. The management department can also manage the progress of technology transfer. For example, the management department can use specialized tools to visualize the technology transfer process.As a result, the system according to this embodiment can collect, analyze, propose, automate sales, and manage technology transfer for a company's technical data all in one place.

[0030] The data collection unit collects technical data from companies. For example, the unit can collect technical data such as patent information, research papers, and technical reports. Specifically, patent information is automatically retrieved from the Japan Patent Office database, research papers are collected from academic databases and publicly available company documents, and technical reports are collected from internal company documents and publicly available technical documents. The data collection unit can, for example, build a system to automatically collect company technical data. This system uses web crawlers and APIs to periodically collect publicly available information from the internet and store it in a database. The data collection unit can also manually collect company technical data. For example, the unit can provide a dedicated interface for collecting company technical data. This interface includes forms for manual data entry by personnel and functions for uploading existing data. Furthermore, the data collection unit can verify and clean the collected data to ensure data quality. For example, it can remove duplicate data, correct erroneous data, and standardize formats. This allows the data collection unit to efficiently and accurately collect company technical data and provide high-quality data that can be used by the analysis and proposal units.

[0031] The analysis unit analyzes the technical data collected by the collection unit to uncover technological synergies. For example, the analysis unit can use AI to analyze technical data and uncover technological synergies. Specifically, it can use natural language processing technology to analyze the content of patent information and research papers, extracting relevant technological elements. This allows it to identify interactions and commonalities between different technological fields, thereby uncovering new technological synergies. The analysis unit can also analyze the interactions of technical data to uncover new technological synergies. For example, it can identify combinations where combining a specific technology with another technology enables new applications. Furthermore, the analysis unit can analyze the integrated effects of technical data to uncover technological synergies. For example, it can evaluate the integrated effects when integrating multiple technologies achieves performance or functionality that cannot be achieved individually. The analysis unit can utilize dedicated algorithms for analyzing technical data. For example, it can use machine learning algorithms to analyze patterns and trends in technical data and predict future technological synergies. This allows the analysis unit to perform advanced analysis of the collected technical data and uncover valuable technological synergies for the company.

[0032] The proposal department makes market proposals based on the technological synergies discovered by the analysis department. For example, the proposal department can use AI to make market proposals. Specifically, it analyzes market needs based on the discovered technological synergies and creates scenarios for making optimal proposals. The proposal department can, for example, create proposal documents based on the discovered technological synergies. These documents include detailed explanations of the technological synergies, the impact of the technology on the market, and expected benefits. The proposal department can also create presentation materials based on the discovered technological synergies. For example, the proposal department can use a dedicated tool for making market proposals. This tool has an automatic generation function for proposal documents and presentation materials, enabling the efficient creation of high-quality materials. Furthermore, the proposal department can evaluate the effectiveness of the proposal and revise it as needed. For example, it can monitor market reactions and competitor activities to optimize the proposal. This allows the proposal department to make optimal market proposals for companies based on the technological synergies discovered by the analysis department, maximizing business opportunities.

[0033] The sales department conducts automated sales activities based on proposals made by the proposal department. For example, the sales department can use AI to automate sales. Specifically, it can identify target customers based on the proposals and formulate optimal sales strategies. The sales department can also automate the creation of proposal materials. This automated creation system generates customized proposal materials for each customer based on information provided by the proposal department. Furthermore, the sales department can act as a sales representative. For example, the sales department can build a dedicated system for automated sales. This system uses AI to automate communication with customers, conducting sales activities via email, chat, and telephone. In addition, the sales department can monitor the effectiveness of sales activities in real time and modify sales strategies as needed. For example, it can analyze customer reactions and market fluctuations and follow up at the optimal time. This allows the sales department to conduct sales activities efficiently and effectively based on proposals made by the proposal department, maximizing the company's sales.

[0034] The management department manages technology transfer based on the results obtained by the sales department. The management department can, for example, use AI for technology transfer management. Specifically, it analyzes data provided by the sales department and digitizes the technology. This ensures efficient technology transfer and maintains the company's technological capabilities. The management department can digitize technology, including the electronic conversion of technical documents and the creation of databases of technical know-how. Furthermore, the management department can manage the progress of technology transfer. For example, it can use a dedicated tool to visualize the technology transfer process. This tool visualizes each step of technology transfer and allows for real-time monitoring of progress. In addition, the management department can evaluate the effectiveness of technology transfer and improve the methods as needed. For example, it can analyze the results of technology transfer and establish best practices for effective technology transfer. This allows the management department to maintain the company's technological capabilities and efficiently manage future technology transfer based on the results obtained by the sales department.

[0035] The data collection unit collects technical data from companies, and the analysis unit analyzes the collected technical data to uncover technological synergies. The data collection unit can collect, for example, technical data such as company patent information, research papers, and technical reports. The data collection unit can, for example, build a system to automatically collect company technical data. Alternatively, the data collection unit can manually collect company technical data. For example, the data collection unit can provide a dedicated interface for collecting company technical data. The analysis unit analyzes the technical data collected by the data collection unit to uncover technological synergies. For example, the analysis unit can use AI to analyze the technical data and uncover technological synergies. For example, the analysis unit can analyze the interactions of technical data to uncover new technological synergies. Furthermore, the analysis unit can analyze the integrated effects of technical data to uncover technological synergies. For example, the analysis unit can use a dedicated algorithm for analyzing technical data. This allows for the collection and analysis of company technical data to uncover technological synergies.

[0036] The proposal department can make market proposals based on discovered technological synergies. For example, the proposal department can use AI to make market proposals. For example, the proposal department can create proposal documents based on discovered technological synergies. Furthermore, the proposal department can create presentation materials based on discovered technological synergies. For example, the proposal department can use dedicated tools for making market proposals. This allows them to make market proposals based on discovered technological synergies.

[0037] The sales department can automate the creation of proposal materials and perform sales outsourcing. For example, the sales department can use AI to automatically create proposal materials. For example, the sales department can build a system that automatically creates proposal materials. Furthermore, the sales department can also perform sales outsourcing. For example, the sales department can use AI to perform sales outsourcing. For example, the sales department can build a system that automates sales activities. This allows for the automatic creation of proposal materials and sales outsourcing.

[0038] The management department can manage the progress of digitizing and transferring technology data, making the technology transfer process visible. For example, the management department can use AI to digitize technology data. For example, the management department can build a system that automatically digitizes technology data. Furthermore, the management department can manage the progress of technology transfer. For example, the management department can use AI to manage the progress of technology transfer. For example, the management department can build a system that provides real-time information on the progress of technology transfer. This allows for the management of the digitization and transfer of technology data, making the technology transfer process visible.

[0039] The management department can improve corporate value by introducing an inheritance index and quantifying technological capabilities. For example, the management department can introduce the inheritance index using AI. The management department can use, for example, a dedicated algorithm for quantifying technological capabilities. Furthermore, the management department can use a dedicated tool for quantifying technological capabilities. This allows for the introduction of an inheritance index, quantification of technological capabilities, and improvement of corporate value.

[0040] The data collection unit can analyze a company's past technical data collection history and select the optimal collection method. For example, it can select the most efficient collection method based on past collection history. It can also analyze past collection history to identify areas for improvement in the collection method. Furthermore, the data collection unit can customize the collection method based on past collection history. This allows for efficient data collection by analyzing past technical data collection history and selecting the optimal method.

[0041] The data collection unit can filter technical data based on a company's current projects and areas of interest. For example, it can prioritize the collection of technical data relevant to current projects. It can also filter the collected technical data based on areas of interest. Furthermore, the data collection unit can adjust the collected technical data according to the project's progress. This allows for the collection of highly relevant data by filtering technical data based on current projects and areas of interest.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of companies when collecting technical data. For example, the data collection unit can prioritize the collection of technical data located near the company's location. For example, the data collection unit can filter and collect data that is highly relevant geographically. Furthermore, the data collection unit can adjust the technical data to be collected according to the characteristics of the region. This allows for efficient data collection by prioritizing the collection of highly relevant data while considering the geographical location of companies.

[0043] The data collection unit can analyze a company's social media activities and collect relevant data when collecting technical data. For example, the unit can collect relevant technical data based on the content of social media activities. The unit can also analyze social media trends and adjust the technical data to be collected. Furthermore, the unit can select the technical data to be collected based on the interests of social media followers. This allows for efficient data collection by analyzing a company's social media activities and collecting relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the technical data during the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance, and a simplified analysis on data of low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the technical data.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the technical data during analysis. For example, the analysis unit can select the optimal analysis algorithm for each category. For example, the analysis unit can customize the analysis algorithm depending on the category. Furthermore, the analysis unit can apply an analysis algorithm considering the characteristics of each category. This allows for efficient analysis by applying different analysis algorithms depending on the category of the technical data.

[0046] The analysis unit can determine the priority of analysis based on the submission date of the technical data. For example, the analysis unit can prioritize the analysis of data submitted earlier. For example, the analysis unit can adjust the priority of analysis according to the submission date. Furthermore, the analysis unit can postpone the analysis of data submitted later. In this way, by determining the priority of analysis based on the submission date of the technical data, analysis can be performed efficiently.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the technical data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can adjust the order of analysis according to relevance. Furthermore, the analysis unit can postpone the analysis of less relevant data. In this way, by adjusting the order of analysis based on the relevance of the technical data, analysis can be performed efficiently.

[0048] The proposal department can adjust the level of detail in its proposals based on the importance of the technological synergies when submitting market proposals. For example, it can provide detailed proposals for highly important technological synergies, and simplified proposals for less important ones. The proposal department can also prioritize proposals according to their importance. This allows for efficient market proposals by adjusting the level of detail based on the importance of the technological synergies.

[0049] The proposal department can apply different proposal algorithms depending on the category of technology synergy when making market proposals. For example, the proposal department can select the optimal proposal algorithm for each category. For example, the proposal department can customize the proposal algorithm depending on the category. Furthermore, the proposal department can apply a proposal algorithm considering the characteristics of each category. This allows for efficient market proposals by applying different proposal algorithms depending on the category of technology synergy.

[0050] The proposal department can prioritize proposals based on the timing of technology synergy submissions when submitting a market proposal. For example, the proposal department can prioritize proposals for technology synergies that have been submitted earlier. The proposal department can adjust the priority of proposals according to the submission timing. Furthermore, the proposal department can postpone proposals for technology synergies that have been submitted later. This allows for efficient market proposals by prioritizing proposals based on the timing of technology synergy submissions.

[0051] The proposal department can adjust the order of proposals based on the relevance of technological synergies when making market proposals. For example, the proposal department can prioritize proposing highly relevant technological synergies. For example, the proposal department can adjust the order of proposals according to their relevance. Furthermore, the proposal department can postpone proposing less relevant technological synergies. This allows for efficient market proposals by adjusting the order of proposals based on the relevance of technological synergies.

[0052] The sales department can customize its sales methods by considering the attribute information of target companies during sales activities. For example, the sales department can customize its sales methods according to the industry of the target company. For example, the sales department can adjust its sales methods according to the size of the target company. Furthermore, the sales department can optimize its sales methods according to the location of the target company. In this way, by customizing sales methods while considering the attribute information of target companies, sales activities can be conducted efficiently.

[0053] The sales department can select the optimal sales strategy by referring to past sales data during sales activities. For example, the sales department can analyze past sales data and select the most effective sales strategy. For example, the sales department can identify areas for improvement in sales strategies based on past sales data. Furthermore, the sales department can customize the optimal sales strategy for each target company by referring to past sales data. This allows for efficient sales activities by selecting the optimal sales strategy by referring to past sales data.

[0054] The sales department can adjust its sales methods during sales activities, taking into account the geographical location of target companies. For example, the sales department can adjust the schedule of sales visits according to the location of the target company. For example, the sales department can adopt a sales method that prioritizes visits to geographically close companies. Furthermore, the sales department can customize its sales methods according to geographical characteristics. In this way, sales activities can be conducted efficiently by adjusting sales methods while taking into account the geographical location of target companies.

[0055] The sales department can analyze the target company's social media activity and propose sales strategies during sales activities. For example, the sales department can propose sales strategies that will attract the target company's attention based on their social media activity. For example, the sales department can analyze social media trends and adjust sales strategies accordingly. Furthermore, the sales department can select sales strategies based on the interests of social media followers. This allows for more efficient sales activities by analyzing the target company's social media activity and proposing sales strategies accordingly.

[0056] The management department can customize its management methods when managing the progress of technology transfer, taking into account the digitization status of technical data. For example, the management department can apply detailed management methods to technical data that is well digitized. For example, the management department can apply simplified management methods to technical data that is lagging behind in digitization. Furthermore, the management department can adjust management methods according to the digitization status. In this way, technology transfer can be carried out efficiently by customizing management methods to take into account the digitization status of technical data.

[0057] The management department can select the optimal management strategy by referring to past technology transfer data when managing the progress of technology transfer. For example, the management department can analyze past technology transfer data and select the most effective management strategy. For example, the management department can identify areas for improvement in the management strategy based on past technology transfer data. Furthermore, the management department can customize the progress management of technology transfer by referring to past technology transfer data. This allows for efficient technology transfer by selecting the optimal management strategy by referring to past technology transfer data.

[0058] The management department can adjust its management methods when managing the progress of technology transfer, taking into account the geographical distribution of technology data. For example, the management department can adjust its management methods according to the location of the technology data. For example, the management department can apply remote management methods to geographically dispersed technology data. Furthermore, the management department can customize management methods according to geographical characteristics. By adjusting management methods to take into account the geographical distribution of technology data, technology transfer can be carried out efficiently.

[0059] The management department can propose management methods by referring to relevant literature on technical data when managing the progress of technology transfer. For example, the management department can propose the optimal management method based on relevant literature. For example, the management department can identify areas for improvement in management methods by referring to relevant literature. Furthermore, the management department can customize the progress management of technology transfer based on relevant literature. In this way, technology transfer can be carried out efficiently by proposing management methods by referring to relevant literature on technical data.

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

[0061] The data collection unit can adjust its data collection methods to take into account the skill sets and experience of the company's employees when collecting technical data from a company. For example, the unit can prioritize the collection of data related to specific technical fields based on the employees' skill sets. It can also reuse previously successful technical data collection methods based on the employees' experience. Furthermore, the unit can provide data collection training programs that take into account the employees' skill sets and experience. This can improve the efficiency and accuracy of the company's technical data collection.

[0062] The analysis unit can evaluate the reliability of collected technical data and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate reliability based on the data source and collection method. It can also evaluate the consistency and accuracy of the data and exclude unreliable data. Furthermore, the analysis unit can adjust the analysis algorithm based on the reliability evaluation results. This allows the analysis unit to uncover technological synergies using highly reliable data.

[0063] The proposal department can simulate the effects of a proposal when making a market proposal and select the optimal proposal. For example, the proposal department can use a simulation model to predict the impact of a proposal on the market. Furthermore, the proposal department can modify and optimize the proposal based on the simulation results. In addition, the proposal department can visualize the simulation results and explain the effects of the proposal in an easy-to-understand manner. This enables the proposal department to make effective market proposals.

[0064] The sales department can analyze the purchase history of target companies and formulate optimal sales strategies when conducting sales activities. For example, the sales department can analyze the past purchase history of target companies to understand their purchasing trends. Furthermore, the sales department can predict the needs of target companies based on purchase history and make optimal proposals. In addition, the sales department can formulate customized sales strategies for each target company based on purchase history. This allows the sales department to conduct effective sales activities that meet the needs of target companies.

[0065] The management department can assess the risks of technology transfer and apply management methods appropriate to those risks when managing the progress of technology transfer. For example, the management department can use risk assessment tools to evaluate the risks of technology transfer. Furthermore, based on the risk assessment results, the management department can apply detailed management methods to high-risk technology transfers. In addition, the management department can apply simplified management methods to low-risk technology transfers. This allows the management department to efficiently manage the progress of technology transfers according to their risks.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects the company's technical data. The collection unit can collect technical data such as the company's patent information, research papers, and technical reports. The collection unit can build a system that automatically collects the company's technical data. Alternatively, the collection unit can collect the company's technical data manually. For example, the collection unit can provide a dedicated interface for collecting the company's technical data. Step 2: The analysis unit analyzes the technical data collected by the data collection unit and uncovers technological synergies. The analysis unit can, for example, use AI to analyze the technical data and uncover technological synergies. The analysis unit can analyze the interactions of the technical data and uncover new technological synergies. The analysis unit can also analyze the integrated effects of the technical data and uncover technological synergies. For example, the analysis unit can use a dedicated algorithm for analyzing the technical data. Step 3: The proposal department makes market proposals based on the technological synergies discovered by the analysis department. The proposal department can, for example, use AI to make market proposals. The proposal department can create proposal documents based on the discovered technological synergies. The proposal department can also create presentation materials based on the discovered technological synergies. For example, the proposal department can use dedicated tools for making market proposals. Step 4: The sales department conducts automated sales based on the proposals made by the proposal department. The sales department can, for example, use AI to conduct automated sales. The sales department can automatically create proposal materials. The sales department can also act as a sales representative. For example, the sales department can build a dedicated system for conducting automated sales. Step 5: The management department manages technology transfer based on the results obtained by the sales department. The management department can, for example, use AI to manage technology transfer. The management department can digitize technology data. The management department can also manage the progress of technology transfer. For example, the management department can use a dedicated tool to visualize the technology transfer process.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes an AI agent to solve the three major challenges faced by small and medium-sized enterprises (SMEs): "labor shortage," "stagnant profits," and "stagnation of technology transfer." This system improves profitability and sustainability by collecting corporate technology data, discovering technological synergies, making market proposals, conducting automated sales, and managing technology transfer. For example, the system analyzes a company's technologies and discovers technological synergies. Next, it makes market proposals based on the discovered technological synergies and creates efficient revenue opportunities through automated sales functions. It also manages technology transfer, quantifies technological capabilities by introducing a transfer index, and improves corporate value. Furthermore, by visualizing corporate value, it promotes investment and provides a base for new partnerships. This has a high ripple effect on the regional economy and is an initiative to rebuild the competitiveness of domestic companies. For example, the system collects and analyzes corporate technology data. For example, by fusing the technologies of companies with different technologies, it can make new market proposals. This allows companies to gain new business opportunities. Next, it makes market proposals based on the discovered technological synergies and creates efficient revenue opportunities through automated sales functions. The system supports a company's sales activities by automatically creating proposal materials and acting as a sales representative. For example, the system automatically selects target companies, creates proposal materials, and conducts sales activities, thereby streamlining the company's sales process. Furthermore, it implements technology transfer management and quantifies technological capabilities by introducing a transfer index, thereby improving corporate value. The system digitizes technology data and manages the progress of transfer, making the technology transfer process visible. For example, digitizing technology data prevents technology from becoming dependent on individuals and allows for real-time monitoring of the transfer progress. In addition, by visualizing corporate value, it provides a platform for promoting investment and new partnerships. The system quantifies a company's technological capabilities and profitability, making corporate value visible. This allows companies to appeal to investors and partner companies, and is expected to promote investment and partnerships. In this way, by utilizing the system, the three major challenges faced by small and medium-sized enterprises—"labor shortage," "stagnation of profits," and "stagnation of technology transfer"—can be solved all at once, improving the profitability and sustainability of companies.Furthermore, it will have a significant ripple effect on the regional economy and will contribute to rebuilding the competitiveness of domestic companies. As a result, the system can comprehensively address the challenges faced by small and medium-sized enterprises, improving their profitability and sustainability.

[0069] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a sales unit, and a management unit. The data collection unit collects corporate technical data. The data collection unit can collect, for example, corporate patent information, research papers, and technical reports. The data collection unit can, for example, build a system that automatically collects corporate technical data. The data collection unit can also manually collect corporate technical data. For example, the data collection unit can provide a dedicated interface for collecting corporate technical data. The analysis unit analyzes the technical data collected by the data collection unit and discovers technological synergies. The analysis unit can, for example, use AI to analyze the technical data and discover technological synergies. The analysis unit can, for example, analyze the interactions of technical data and discover new technological synergies. The analysis unit can also analyze the integrated effects of technical data and discover technological synergies. For example, the analysis unit can use a dedicated algorithm for analyzing technical data. The proposal unit makes market proposals based on the technological synergies discovered by the analysis unit. The proposal unit can, for example, use AI to make market proposals. The proposal department can, for example, create proposals based on discovered technological synergies. The proposal department can also create presentation materials based on discovered technological synergies. For example, the proposal department can use specialized tools for making market proposals. The sales department conducts automated sales based on the proposals made by the proposal department. The sales department can, for example, use AI for automated sales. The sales department can, for example, automatically create proposal materials. The sales department can also perform sales outsourcing. For example, the sales department can build a specialized system for automated sales. The management department manages technology transfer based on the results obtained by the sales department. The management department can, for example, use AI for technology transfer management. The management department can, for example, digitize technology data. The management department can also manage the progress of technology transfer. For example, the management department can use specialized tools to visualize the technology transfer process.As a result, the system according to this embodiment can collect, analyze, propose, automate sales, and manage technology transfer for a company's technical data all in one place.

[0070] The data collection unit collects technical data from companies. For example, the unit can collect technical data such as patent information, research papers, and technical reports. Specifically, patent information is automatically retrieved from the Japan Patent Office database, research papers are collected from academic databases and publicly available company documents, and technical reports are collected from internal company documents and publicly available technical documents. The data collection unit can, for example, build a system to automatically collect company technical data. This system uses web crawlers and APIs to periodically collect publicly available information from the internet and store it in a database. The data collection unit can also manually collect company technical data. For example, the unit can provide a dedicated interface for collecting company technical data. This interface includes forms for manual data entry by personnel and functions for uploading existing data. Furthermore, the data collection unit can verify and clean the collected data to ensure data quality. For example, it can remove duplicate data, correct erroneous data, and standardize formats. This allows the data collection unit to efficiently and accurately collect company technical data and provide high-quality data that can be used by the analysis and proposal units.

[0071] The analysis unit analyzes the technical data collected by the collection unit to uncover technological synergies. For example, the analysis unit can use AI to analyze technical data and uncover technological synergies. Specifically, it can use natural language processing technology to analyze the content of patent information and research papers, extracting relevant technological elements. This allows it to identify interactions and commonalities between different technological fields, thereby uncovering new technological synergies. The analysis unit can also analyze the interactions of technical data to uncover new technological synergies. For example, it can identify combinations where combining a specific technology with another technology enables new applications. Furthermore, the analysis unit can analyze the integrated effects of technical data to uncover technological synergies. For example, it can evaluate the integrated effects when integrating multiple technologies achieves performance or functionality that cannot be achieved individually. The analysis unit can utilize dedicated algorithms for analyzing technical data. For example, it can use machine learning algorithms to analyze patterns and trends in technical data and predict future technological synergies. This allows the analysis unit to perform advanced analysis of the collected technical data and uncover valuable technological synergies for the company.

[0072] The proposal department makes market proposals based on the technological synergies discovered by the analysis department. For example, the proposal department can use AI to make market proposals. Specifically, it analyzes market needs based on the discovered technological synergies and creates scenarios for making optimal proposals. The proposal department can, for example, create proposal documents based on the discovered technological synergies. These documents include detailed explanations of the technological synergies, the impact of the technology on the market, and expected benefits. The proposal department can also create presentation materials based on the discovered technological synergies. For example, the proposal department can use a dedicated tool for making market proposals. This tool has an automatic generation function for proposal documents and presentation materials, enabling the efficient creation of high-quality materials. Furthermore, the proposal department can evaluate the effectiveness of the proposal and revise it as needed. For example, it can monitor market reactions and competitor activities to optimize the proposal. This allows the proposal department to make optimal market proposals for companies based on the technological synergies discovered by the analysis department, maximizing business opportunities.

[0073] The sales department conducts automated sales activities based on proposals made by the proposal department. For example, the sales department can use AI to automate sales. Specifically, it can identify target customers based on the proposals and formulate optimal sales strategies. The sales department can also automate the creation of proposal materials. This automated creation system generates customized proposal materials for each customer based on information provided by the proposal department. Furthermore, the sales department can act as a sales representative. For example, the sales department can build a dedicated system for automated sales. This system uses AI to automate communication with customers, conducting sales activities via email, chat, and telephone. In addition, the sales department can monitor the effectiveness of sales activities in real time and modify sales strategies as needed. For example, it can analyze customer reactions and market fluctuations and follow up at the optimal time. This allows the sales department to conduct sales activities efficiently and effectively based on proposals made by the proposal department, maximizing the company's sales.

[0074] The management department manages technology transfer based on the results obtained by the sales department. The management department can, for example, use AI for technology transfer management. Specifically, it analyzes data provided by the sales department and digitizes the technology. This ensures efficient technology transfer and maintains the company's technological capabilities. The management department can digitize technology, including the electronic conversion of technical documents and the creation of databases of technical know-how. Furthermore, the management department can manage the progress of technology transfer. For example, it can use a dedicated tool to visualize the technology transfer process. This tool visualizes each step of technology transfer and allows for real-time monitoring of progress. In addition, the management department can evaluate the effectiveness of technology transfer and improve the methods as needed. For example, it can analyze the results of technology transfer and establish best practices for effective technology transfer. This allows the management department to maintain the company's technological capabilities and efficiently manage future technology transfer based on the results obtained by the sales department.

[0075] The data collection unit collects technical data from companies, and the analysis unit analyzes the collected technical data to uncover technological synergies. The data collection unit can collect, for example, technical data such as company patent information, research papers, and technical reports. The data collection unit can, for example, build a system to automatically collect company technical data. Alternatively, the data collection unit can manually collect company technical data. For example, the data collection unit can provide a dedicated interface for collecting company technical data. The analysis unit analyzes the technical data collected by the data collection unit to uncover technological synergies. For example, the analysis unit can use AI to analyze the technical data and uncover technological synergies. For example, the analysis unit can analyze the interactions of technical data to uncover new technological synergies. Furthermore, the analysis unit can analyze the integrated effects of technical data to uncover technological synergies. For example, the analysis unit can use a dedicated algorithm for analyzing technical data. This allows for the collection and analysis of company technical data to uncover technological synergies.

[0076] The proposal department can make market proposals based on discovered technological synergies. For example, the proposal department can use AI to make market proposals. For example, the proposal department can create proposal documents based on discovered technological synergies. Furthermore, the proposal department can create presentation materials based on discovered technological synergies. For example, the proposal department can use dedicated tools for making market proposals. This allows them to make market proposals based on discovered technological synergies.

[0077] The sales department can automate the creation of proposal materials and perform sales outsourcing. For example, the sales department can use AI to automatically create proposal materials. For example, the sales department can build a system that automatically creates proposal materials. Furthermore, the sales department can also perform sales outsourcing. For example, the sales department can use AI to perform sales outsourcing. For example, the sales department can build a system that automates sales activities. This allows for the automatic creation of proposal materials and sales outsourcing.

[0078] The management department can manage the progress of digitizing and transferring technology data, making the technology transfer process visible. For example, the management department can use AI to digitize technology data. For example, the management department can build a system that automatically digitizes technology data. Furthermore, the management department can manage the progress of technology transfer. For example, the management department can use AI to manage the progress of technology transfer. For example, the management department can build a system that provides real-time information on the progress of technology transfer. This allows for the management of the digitization and transfer of technology data, making the technology transfer process visible.

[0079] The management department can improve corporate value by introducing an inheritance index and quantifying technological capabilities. For example, the management department can introduce the inheritance index using AI. The management department can use, for example, a dedicated algorithm for quantifying technological capabilities. Furthermore, the management department can use a dedicated tool for quantifying technological capabilities. This allows for the introduction of an inheritance index, quantification of technological capabilities, and improvement of corporate value.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of technical data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can accelerate the collection timing to collect data efficiently. Furthermore, if the user is in a hurry, the data collection unit can adjust the collection timing to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of technical data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can analyze a company's past technical data collection history and select the optimal collection method. For example, it can select the most efficient collection method based on past collection history. It can also analyze past collection history to identify areas for improvement in the collection method. Furthermore, the data collection unit can customize the collection method based on past collection history. This allows for efficient data collection by analyzing past technical data collection history and selecting the optimal method.

[0082] The data collection unit can filter technical data based on a company's current projects and areas of interest. For example, it can prioritize the collection of technical data relevant to current projects. It can also filter the collected technical data based on areas of interest. Furthermore, the data collection unit can adjust the collected technical data according to the project's progress. This allows for the collection of highly relevant data by filtering technical data based on current projects and areas of interest.

[0083] The data collection unit can estimate the user's emotions and prioritize the technical data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone collecting less important data. For example, if the user is relaxed, the data collection unit can prioritize collecting more important data. Also, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This allows for efficient data collection by prioritizing technical data based on the user's emotions. 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.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of companies when collecting technical data. For example, the data collection unit can prioritize the collection of technical data located near the company's location. For example, the data collection unit can filter and collect data that is highly relevant geographically. Furthermore, the data collection unit can adjust the technical data to be collected according to the characteristics of the region. This allows for efficient data collection by prioritizing the collection of highly relevant data while considering the geographical location of companies.

[0085] The data collection unit can analyze a company's social media activities and collect relevant data when collecting technical data. For example, the unit can collect relevant technical data based on the content of social media activities. The unit can also analyze social media trends and adjust the technical data to be collected. Furthermore, the unit can select the technical data to be collected based on the interests of social media followers. This allows for efficient data collection by analyzing a company's social media activities and collecting relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis method of technology synergies based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can perform a simplified analysis. Furthermore, if the user is excited, the analysis unit can provide visually easy-to-understand analysis results. This allows for efficient analysis by adjusting the analysis method of technology synergies based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the technical data during the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance, and a simplified analysis on data of low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the technical data.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the technical data during analysis. For example, the analysis unit can select the optimal analysis algorithm for each category. For example, the analysis unit can customize the analysis algorithm depending on the category. Furthermore, the analysis unit can apply an analysis algorithm considering the characteristics of each category. This allows for efficient analysis by applying different analysis algorithms depending on the category of the technical data.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, the analysis results can be displayed efficiently. 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.

[0090] The analysis unit can determine the priority of analysis based on the submission date of the technical data. For example, the analysis unit can prioritize the analysis of data submitted earlier. For example, the analysis unit can adjust the priority of analysis according to the submission date. Furthermore, the analysis unit can postpone the analysis of data submitted later. In this way, by determining the priority of analysis based on the submission date of the technical data, analysis can be performed efficiently.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the technical data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can adjust the order of analysis according to relevance. Furthermore, the analysis unit can postpone the analysis of less relevant data. In this way, by adjusting the order of analysis based on the relevance of the technical data, analysis can be performed efficiently.

[0092] The proposal unit can estimate the user's emotions and adjust the way market proposals are presented based on those emotions. For example, if the user is relaxed, the proposal unit can provide detailed proposals. If the user is in a hurry, the proposal unit can provide concise proposals. Furthermore, if the user is excited, the proposal unit can provide visually appealing proposals. This allows for efficient market proposal delivery by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The proposal department can adjust the level of detail in its proposals based on the importance of the technological synergies when submitting market proposals. For example, it can provide detailed proposals for highly important technological synergies, and simplified proposals for less important ones. The proposal department can also prioritize proposals according to their importance. This allows for efficient market proposals by adjusting the level of detail based on the importance of the technological synergies.

[0094] The proposal department can apply different proposal algorithms depending on the category of technology synergy when making market proposals. For example, the proposal department can select the optimal proposal algorithm for each category. For example, the proposal department can customize the proposal algorithm depending on the category. Furthermore, the proposal department can apply a proposal algorithm considering the characteristics of each category. This allows for efficient market proposals by applying different proposal algorithms depending on the category of technology synergy.

[0095] The proposal unit can estimate the user's emotions and adjust the length of the market proposal based on the estimated emotions. For example, if the user is in a hurry, the proposal unit can make a short, to-the-point proposal. If the user is relaxed, the proposal unit can make a longer proposal with detailed explanations. Furthermore, if the user is excited, the proposal unit can make a visually stimulating proposal. This allows for efficient market proposal delivery by adjusting the length of the market proposal based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The proposal department can prioritize proposals based on the timing of technology synergy submissions when submitting a market proposal. For example, the proposal department can prioritize proposals for technology synergies that have been submitted earlier. The proposal department can adjust the priority of proposals according to the submission timing. Furthermore, the proposal department can postpone proposals for technology synergies that have been submitted later. This allows for efficient market proposals by prioritizing proposals based on the timing of technology synergy submissions.

[0097] The proposal department can adjust the order of proposals based on the relevance of technological synergies when making market proposals. For example, the proposal department can prioritize proposing highly relevant technological synergies. For example, the proposal department can adjust the order of proposals according to their relevance. Furthermore, the proposal department can postpone proposing less relevant technological synergies. This allows for efficient market proposals by adjusting the order of proposals based on the relevance of technological synergies.

[0098] The sales department can estimate the user's emotions and adjust its sales activities based on those emotions. For example, if the user is relaxed, the sales department can conduct detailed sales activities. If the user is in a hurry, the sales department can conduct concise sales activities. Furthermore, if the user is excited, the sales department can conduct visually appealing sales activities. This allows for more efficient sales activities by adjusting sales activities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The sales department can customize its sales methods by considering the attribute information of target companies during sales activities. For example, the sales department can customize its sales methods according to the industry of the target company. For example, the sales department can adjust its sales methods according to the size of the target company. Furthermore, the sales department can optimize its sales methods according to the location of the target company. In this way, by customizing sales methods while considering the attribute information of target companies, sales activities can be conducted efficiently.

[0100] The sales department can select the optimal sales strategy by referring to past sales data during sales activities. For example, the sales department can analyze past sales data and select the most effective sales strategy. For example, the sales department can identify areas for improvement in sales strategies based on past sales data. Furthermore, the sales department can customize the optimal sales strategy for each target company by referring to past sales data. This allows for efficient sales activities by selecting the optimal sales strategy by referring to past sales data.

[0101] The sales department can estimate the user's emotions and prioritize sales activities based on those emotions. For example, if a user is stressed, the sales department can postpone less important sales activities. If a user is relaxed, the sales department can prioritize more important sales activities. Also, if a user is in a hurry, the sales department can prioritize sales activities that can be handled quickly. This allows for more efficient sales activities by prioritizing them based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The sales department can adjust its sales methods during sales activities, taking into account the geographical location of target companies. For example, the sales department can adjust the schedule of sales visits according to the location of the target company. For example, the sales department can adopt a sales method that prioritizes visits to geographically close companies. Furthermore, the sales department can customize its sales methods according to geographical characteristics. In this way, sales activities can be conducted efficiently by adjusting sales methods while taking into account the geographical location of target companies.

[0103] The sales department can analyze the target company's social media activity and propose sales strategies during sales activities. For example, the sales department can propose sales strategies that will attract the target company's attention based on their social media activity. For example, the sales department can analyze social media trends and adjust sales strategies accordingly. Furthermore, the sales department can select sales strategies based on the interests of social media followers. This allows for more efficient sales activities by analyzing the target company's social media activity and proposing sales strategies accordingly.

[0104] The management department can estimate the user's emotions and adjust the progress management method for technology transfer based on the estimated emotions. For example, if the user is relaxed, the management department can perform detailed progress management. For example, if the user is in a hurry, the management department can perform simplified progress management. Furthermore, if the user is excited, the management department can provide a visually easy-to-understand progress management method. This allows for efficient technology transfer by adjusting the progress management method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The management department can customize its management methods when managing the progress of technology transfer, taking into account the digitization status of technical data. For example, the management department can apply detailed management methods to technical data that is well digitized. For example, the management department can apply simplified management methods to technical data that is lagging behind in digitization. Furthermore, the management department can adjust management methods according to the digitization status. In this way, technology transfer can be carried out efficiently by customizing management methods to take into account the digitization status of technical data.

[0106] The management department can select the optimal management strategy by referring to past technology transfer data when managing the progress of technology transfer. For example, the management department can analyze past technology transfer data and select the most effective management strategy. For example, the management department can identify areas for improvement in the management strategy based on past technology transfer data. Furthermore, the management department can customize the progress management of technology transfer by referring to past technology transfer data. This allows for efficient technology transfer by selecting the optimal management strategy by referring to past technology transfer data.

[0107] The management department can estimate user emotions and determine the priority of technology transfer based on those estimated emotions. For example, if a user is stressed, the management department can postpone less important technology transfers. For example, if a user is relaxed, the management department can prioritize more important technology transfers. Furthermore, if a user is in a hurry, the management department can prioritize technology transfers that can be handled quickly. This allows for efficient technology transfer by determining the priority of technology transfers based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The management department can adjust its management methods when managing the progress of technology transfer, taking into account the geographical distribution of technology data. For example, the management department can adjust its management methods according to the location of the technology data. For example, the management department can apply remote management methods to geographically dispersed technology data. Furthermore, the management department can customize management methods according to geographical characteristics. By adjusting management methods to take into account the geographical distribution of technology data, technology transfer can be carried out efficiently.

[0109] The management department can propose management methods by referring to relevant literature on technical data when managing the progress of technology transfer. For example, the management department can propose the optimal management method based on relevant literature. For example, the management department can identify areas for improvement in management methods by referring to relevant literature. Furthermore, the management department can customize the progress management of technology transfer based on relevant literature. In this way, technology transfer can be carried out efficiently by proposing management methods by referring to relevant literature on technical data.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can adjust its data collection methods to take into account the skill sets and experience of the company's employees when collecting technical data from a company. For example, the unit can prioritize the collection of data related to specific technical fields based on the employees' skill sets. It can also reuse previously successful technical data collection methods based on the employees' experience. Furthermore, the unit can provide data collection training programs that take into account the employees' skill sets and experience. This can improve the efficiency and accuracy of the company's technical data collection.

[0112] The analysis unit can evaluate the reliability of collected technical data and prioritize the analysis of highly reliable data. For example, the analysis unit can evaluate reliability based on the data source and collection method. It can also evaluate the consistency and accuracy of the data and exclude unreliable data. Furthermore, the analysis unit can adjust the analysis algorithm based on the reliability evaluation results. This allows the analysis unit to uncover technological synergies using highly reliable data.

[0113] The proposal department can simulate the effects of a proposal when making a market proposal and select the optimal proposal. For example, the proposal department can use a simulation model to predict the impact of a proposal on the market. Furthermore, the proposal department can modify and optimize the proposal based on the simulation results. In addition, the proposal department can visualize the simulation results and explain the effects of the proposal in an easy-to-understand manner. This enables the proposal department to make effective market proposals.

[0114] The sales department can analyze the purchase history of target companies and formulate optimal sales strategies when conducting sales activities. For example, the sales department can analyze the past purchase history of target companies to understand their purchasing trends. Furthermore, the sales department can predict the needs of target companies based on purchase history and make optimal proposals. In addition, the sales department can formulate customized sales strategies for each target company based on purchase history. This allows the sales department to conduct effective sales activities that meet the needs of target companies.

[0115] The management department can assess the risks of technology transfer and apply management methods appropriate to those risks when managing the progress of technology transfer. For example, the management department can use risk assessment tools to evaluate the risks of technology transfer. Furthermore, based on the risk assessment results, the management department can apply detailed management methods to high-risk technology transfers. In addition, the management department can apply simplified management methods to low-risk technology transfers. This allows the management department to efficiently manage the progress of technology transfers according to their risks.

[0116] The data collection unit can estimate the user's emotions and customize the method of collecting technical data based on those emotions. For example, if the user is stressed, the data collection unit can simplify the collection method to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can apply a more detailed collection method to improve data accuracy. Furthermore, if the user is in a hurry, the data collection unit can select a method for quickly collecting data. This allows the data collection unit to perform flexible data collection in response to the user's emotions.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can present simple and visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can present concise analysis results that get straight to the point. In this way, the analysis unit can present analysis results effectively according to the user's emotions.

[0118] The suggestion function can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is in a hurry, it can provide concise and to-the-point suggestions. Furthermore, if the user is excited, it can provide visually appealing suggestions. This allows the suggestion function to make effective suggestions tailored to the user's emotions.

[0119] The sales department can estimate the user's emotions and adjust the timing of sales activities based on those estimates. For example, if the user is stressed, the sales department can delay sales activities to reduce the user's burden. Conversely, if the user is relaxed, the sales department can accelerate sales activities to conduct sales more efficiently. Furthermore, if the user is in a hurry, the sales department can conduct sales activities to respond quickly. In this way, the sales department can conduct flexible sales activities that respond to the user's emotions.

[0120] The management department can estimate the user's emotions and adjust the frequency of progress management for technology transfer based on those estimated emotions. For example, if the user is stressed, the management department can reduce the frequency of progress management to lessen the user's burden. Conversely, if the user is relaxed, the management department can increase the frequency of progress management to provide more detailed control. Furthermore, if the user is in a hurry, the management department can provide progress management to ensure a quick response. This allows the management department to provide flexible progress management that responds to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects the company's technical data. The collection unit can collect technical data such as the company's patent information, research papers, and technical reports. The collection unit can build a system that automatically collects the company's technical data. Alternatively, the collection unit can collect the company's technical data manually. For example, the collection unit can provide a dedicated interface for collecting the company's technical data. Step 2: The analysis unit analyzes the technical data collected by the data collection unit and uncovers technological synergies. The analysis unit can, for example, use AI to analyze the technical data and uncover technological synergies. The analysis unit can analyze the interactions of the technical data and uncover new technological synergies. The analysis unit can also analyze the integrated effects of the technical data and uncover technological synergies. For example, the analysis unit can use a dedicated algorithm for analyzing the technical data. Step 3: The proposal department makes market proposals based on the technological synergies discovered by the analysis department. The proposal department can, for example, use AI to make market proposals. The proposal department can create proposal documents based on the discovered technological synergies. The proposal department can also create presentation materials based on the discovered technological synergies. For example, the proposal department can use dedicated tools for making market proposals. Step 4: The sales department conducts automated sales based on the proposals made by the proposal department. The sales department can, for example, use AI to conduct automated sales. The sales department can automatically create proposal materials. The sales department can also act as a sales representative. For example, the sales department can build a dedicated system for conducting automated sales. Step 5: The management department manages technology transfer based on the results obtained by the sales department. The management department can, for example, use AI to manage technology transfer. The management department can digitize technology data. The management department can also manage the progress of technology transfer. For example, the management department can use a dedicated tool to visualize the technology transfer process.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, sales unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect a company's technical data by the control unit 46A of the smart device 14. The analysis unit can analyze the technical data and uncover technological synergies by the specific processing unit 290 of the data processing unit 12. The proposal unit can make market proposals by the specific processing unit 290 of the data processing unit 12. The sales unit can perform automated sales by the control unit 46A of the smart device 14. The management unit can perform technology transfer management by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the above-mentioned elements, including the data collection unit, analysis unit, proposal unit, sales unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect a company's technical data using the control unit 46A of the smart glasses 214. The analysis unit can analyze the technical data using the specific processing unit 290 of the data processing unit 12 to uncover technological synergies. The proposal unit can make market proposals using the specific processing unit 290 of the data processing unit 12. The sales unit can perform automated sales using the control unit 46A of the smart glasses 214. The management unit can perform technology transfer management using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices or control units is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, sales unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect a company's technical data using the control unit 46A of the headset terminal 314. The analysis unit can analyze the technical data using, for example, the specific processing unit 290 of the data processing unit 12 to uncover technological synergies. The proposal unit can make market proposals using, for example, the specific processing unit 290 of the data processing unit 12. The sales unit can conduct automated sales using, for example, the control unit 46A of the headset terminal 314. The management unit can perform technology transfer management using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the above-mentioned elements, including the data collection unit, analysis unit, proposal unit, sales unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect a company's technical data by the control unit 46A of the robot 414. The analysis unit can analyze the technical data and uncover technological synergies by, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit can make market proposals by, for example, the specific processing unit 290 of the data processing unit 12. The sales unit can perform automated sales by, for example, the control unit 46A of the robot 414. The management unit can perform technology transfer management by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The data collection department collects technical data from companies, The analysis unit analyzes the technical data collected by the aforementioned collection unit and discovers technological synergies, Based on the technological synergies discovered by the aforementioned analysis unit, the proposal unit makes market proposals. A sales department that conducts automated sales based on the content proposed by the aforementioned proposal department, The system includes a management department that performs technology transfer management based on the results obtained by the aforementioned sales department. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collecting technical data from companies, The aforementioned analysis unit, We analyze the collected technical data and uncover technological synergies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We will make market proposals based on the technological synergies that have been discovered. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned sales department, Automate the creation of proposal documents and provide sales outsourcing services. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, We will manage the progress of digitizing and transferring technology data, and make the technology transfer process visible. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, By introducing an inheritance index, we can quantify technological capabilities and improve corporate value. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of technical data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze a company's past technical data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting technical data, filter it based on the company's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of technical data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting technical data, the collection of highly relevant data is prioritized, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting technical data, analyze a company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate user emotions and adjust the analysis method of technology synergies based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the technical data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the technical data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the timing of the submission of technical data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the technical data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates user sentiment and adjusts the way market proposals are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a market proposal, adjust the level of detail in the proposal based on the importance of the technological synergies. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a market proposal, different proposal algorithms are applied depending on the category of technological synergy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates user sentiment and adjusts the length of the market proposal based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a market proposal, the priority of proposals will be determined based on the timing of the submission of technology synergies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making market proposals, adjust the order of proposals based on the relevance of technological synergies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned sales department, We estimate user emotions and adjust sales activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned sales department, During sales activities, customize sales methods by taking into account the attribute information of the target company. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned sales department, During sales activities, select the optimal sales strategy by referring to past sales data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned sales department, The system estimates user emotions and prioritizes sales activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned sales department, When conducting sales activities, adjust sales methods while taking into account the geographical location of the target company. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned sales department, During sales activities, we analyze the social media activities of target companies and propose sales methods based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, We estimate user sentiment and adjust the progress management method for technology transfer based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, When managing the progress of technology transfer, customize the management methodology to take into account the status of digitalization of technical data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When managing the progress of technology transfer, refer to past technology transfer data to select the optimal management strategy. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, It estimates user emotions and determines the priority of technology transfer based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, When managing the progress of technology transfer, adjust management methods to take into account the geographical distribution of technical data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, When managing the progress of technology transfer, we propose a management method by referring to relevant literature on technical data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects technical data from companies, The analysis unit analyzes the technical data collected by the aforementioned collection unit and discovers technological synergies, Based on the technological synergies discovered by the aforementioned analysis unit, the proposal unit makes market proposals. A sales department that conducts automated sales based on the content proposed by the aforementioned proposal department, The system includes a management department that performs technology transfer management based on the results obtained by the aforementioned sales department. A system characterized by the following features.

2. The aforementioned sales department, Automate the creation of proposal documents and provide sales outsourcing services. The system according to feature 1.

3. The aforementioned management department, We will manage the progress of digitizing and transferring technology data, and make the technology transfer process visible. The system according to feature 1.

4. The aforementioned management department, By introducing an inheritance index, we can quantify technological capabilities and improve corporate value. The system according to feature 1.

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

6. The aforementioned collection unit is We analyze a company's past technical data collection history and select the optimal collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting technical data, filter it based on the company's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates user sentiment and determines the priority of technical data to collect based on the estimated user sentiment. The system according to feature 1.

Images