system

Abstract

The system according to this embodiment aims to efficiently acquire customer company information and business environment information and provide it to the sales representative in charge. [Solution] The system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires customer company information and business environment information. The analysis unit analyzes the information acquired by the acquisition unit. The provision unit provides the information analyzed by the analysis unit to the sales representative.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to efficiently acquire customer company information and business environment information and provide them to the person in charge of sales, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently acquire customer company information and business environment information and provide them to the person in charge of sales.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires customer company information and business environment information. The analysis unit analyzes the information acquired by the acquisition unit. The provision unit provides the information analyzed by the analysis unit to the person in charge of sales.

Effects of the Invention

[0007] The system according to this embodiment can efficiently acquire customer company information and business environment information and provide it to the sales representative. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 34434. 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 provides functions to mitigate or avoid the negative risks associated with lost deals. This system is a mechanism that uses an AI agent to acquire customer company information and business environment information and provides this information to the sales representative. For example, the system uses an AI agent to acquire customer company information and business environment information. Next, the system uses the AI ​​agent to analyze the acquired information and provides it to the sales representative. This mechanism allows the sales representative to understand the current situation and market environment of the customer company and take appropriate measures to mitigate or avoid the risk of lost deals. Furthermore, the system monitors the effectiveness of the proposals made by the sales representative and provides feedback as needed. This reduces or avoids the risk of lost deals and allows the sales representative to propose new value to the customer. As a result, the sales representative can enjoy achieving their goals and strengthen their relationship with the customer company. For example, the system uses an AI agent to collect information such as the customer company's financial situation, industry trends, and the actions of competitors. This allows the sales representative to understand the customer company's current situation and market environment. Next, the system uses the AI ​​agent to analyze the acquired information and provide it to the sales representative. For example, if the customer company's financial situation is deteriorating, the system notifies the sales representative of this information and proposes appropriate countermeasures. Furthermore, the system provides information to enable the creation of new value propositions based on industry trends and competitor activities. This mechanism allows sales representatives to understand the current situation and market environment of client companies and take appropriate measures to mitigate or avoid the risk of losing a deal. For example, if a client company's financial situation deteriorates, the sales representative can take countermeasures early to avoid losing the deal. In addition, by creating new value propositions based on industry trends and competitor activities, the quality of proposals to client companies can be improved. Moreover, the system monitors the effectiveness of the proposals made by sales representatives and provides feedback as needed. For example, it checks whether the proposed content was accepted by the client company, analyzes the reasons if it was not accepted, and provides information to be used for future proposals. In this way, by utilizing the AI ​​agent, the risk of losing a deal can be mitigated or avoided, and new value can be proposed to customers.This allows sales representatives to enjoy achieving their targets and strengthen relationships with client companies. The system, in turn, reduces or avoids the risk of lost deals and enables the proposal of new value to customers.

[0029] The system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires customer company information and business environment information. The acquisition unit collects information such as the customer company's financial status, industry trends, and the activities of competitors. For example, the acquisition unit acquires the customer company's financial statements to understand its financial status. The acquisition unit can also collect market research reports to understand industry trends. Furthermore, the acquisition unit can collect financial reports and marketing strategies of competitors to understand their activities. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit uses data mining technology to analyze the customer company's financial status. The analysis unit can also use statistical analysis technology to analyze industry trends. Furthermore, the analysis unit can use machine learning algorithms to analyze the activities of competitors. The provision unit provides the information analyzed by the analysis unit to the sales representative. For example, if the customer company's financial status is deteriorating, the provision unit notifies the sales representative of this information and proposes appropriate countermeasures. Furthermore, the service provider can provide information to make new value propositions based on industry trends and the actions of competitors. In addition, the service provider can monitor the effectiveness of the proposed content and provide feedback as needed. As a result, the system according to this embodiment can reduce or avoid the risk of losing a deal by acquiring, analyzing, and providing customer company information and business environment information.

[0030] The data acquisition unit acquires customer company information and business environment information. Specifically, it collects information such as the financial status of customer companies, industry trends, and the activities of competitors. To understand the financial status of customer companies, the data acquisition unit collects detailed financial data such as financial statements, cash flow statements, and income statements. This data is important for evaluating the management status and financial health of companies. In addition, the data acquisition unit collects market research reports and industry analysis reports to understand industry trends. This includes industry growth rates, market share of major players, and the adoption status of new technologies. Furthermore, to understand the activities of competitors, the data acquisition unit collects information such as the financial reports, marketing strategies, product lineups, and pricing strategies of competitors. This allows for a detailed understanding of the competitive environment faced by customer companies. The data acquisition unit can centrally manage this information and update it in real time as needed. For example, it can use web scraping technology to automatically collect publicly available information on the internet and store it in a database. It can also use APIs to obtain the latest market data and financial data from external data providers. This allows the data acquisition unit to always collect the latest information and improve the accuracy of the information throughout the system.

[0031] The Analysis Department analyzes the information acquired by the Acquisition Department. Specifically, it uses data mining techniques to analyze the financial status of client companies. Data mining techniques include clustering, association analysis, and regression analysis, which are used to extract useful patterns and trends from the client company's financial data. Statistical analysis techniques can also be used to analyze industry trends. For example, time series analysis is used to predict future market trends from historical data. Furthermore, machine learning algorithms can be used to analyze the activities of competitors. Machine learning algorithms include support vector machines, random forests, and neural networks, which are used to analyze the marketing strategies and product success factors of competitors. The Analysis Department utilizes these techniques to comprehensively analyze the acquired data and evaluate the current situation and future risks of client companies. Furthermore, the Analysis Department visualizes the analysis results so that sales representatives can easily understand them. For example, dashboards are used to display financial status, market trends, and competitor activities in graphs and charts. This allows the Analysis Department to provide sales representatives with information that enables them to make quick and accurate decisions.

[0032] The service provider provides the sales representative with information analyzed by the analytics department. Specifically, if a client company's financial situation deteriorates, the service provider notifies the sales representative and proposes appropriate countermeasures. For example, if a client company's cash flow deteriorates, the service provider will propose a review of payment terms or additional financing to the sales representative. The service provider can also provide information to create new value propositions based on industry trends and competitor activities. For example, in industries where new technologies are being adopted, the service provider may propose technology investments to help client companies maintain their competitiveness. Furthermore, the service provider can monitor the effectiveness of the proposals and provide feedback as needed. For example, after the proposed measures are implemented, the service provider quantitatively evaluates their effectiveness and identifies areas for improvement. This allows the service provider to support sales representatives in making optimal proposals to client companies and reducing or avoiding the risk of losing deals. In addition, the service provider provides tools and platforms to facilitate communication with sales representatives. For example, chatbots and AI assistants can be used to enable sales representatives to quickly obtain the information they need. This allows the service provider to support sales representatives in providing prompt and accurate responses to client companies, thereby maximizing the overall effectiveness of the system.

[0033] The service department can notify the sales representative of a client company's deteriorating financial condition and propose appropriate countermeasures. For example, the service department can notify the sales representative if a client company's financial indicators fall below a certain threshold. The service department can also notify the sales representative if the client company has a series of consecutive losses. Furthermore, the service department can notify the sales representative if a client company's cash flow is deteriorating. This allows for the rapid proposal of countermeasures when a client company's financial condition deteriorates, thereby reducing or avoiding the risk of losing a deal. Some or all of the above processes performed by the service department may be carried out using AI, for example, or not. For example, the service department can input the client company's financial data into a generating AI and have the generating AI perform a process to detect deterioration in the financial condition.

[0034] The service provider can provide information to create new value propositions based on industry trends and the actions of competitors. For example, the service provider can grasp industry trends based on market research reports and provide that information to the sales representative. The service provider can also collect industry news and provide the sales representative with the latest industry developments. Furthermore, the service provider can collect expert opinions and provide the sales representative with future industry forecasts. This allows the service provider to improve the quality of proposals to client companies by creating new value propositions based on industry trends and the actions of competitors. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input market research reports and industry news into a generating AI and have the generating AI perform the process of analyzing industry trends.

[0035] The service department can monitor the effectiveness of the proposal and provide feedback as needed. For example, the service department can verify whether the proposal was accepted by the client company. If the proposal was not accepted, the service department can also analyze the reasons and provide information to be used for future proposals. Furthermore, the service department can evaluate the impact the proposal had on the client company and provide feedback on the results to the sales representative. This allows for monitoring the effectiveness of the proposal and providing feedback to help improve the proposal. Some or all of the above processes in the service department may be performed using AI, for example, or not. For example, the service department can input the acceptance status of the proposal into a generating AI and have the generating AI perform a process to analyze the reasons why it was not accepted.

[0036] The data acquisition unit can collect information such as the financial status of client companies, industry trends, and the activities of competitors. For example, the data acquisition unit can collect financial statements of client companies to understand their financial status. It can also collect market research reports to understand industry trends. Furthermore, it can collect financial reports and marketing strategies of competitors to understand their activities. By collecting information such as the financial status of client companies, industry trends, and the activities of competitors, it is possible to understand the current situation and market environment of client companies. Some or all of the above processes in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can input the financial statements of client companies into a generating AI and have the generating AI perform the process of analyzing the financial status.

[0037] The analysis unit can analyze the acquired information to understand the current situation and market environment of client companies. For example, the analysis unit can use data mining techniques to analyze the financial situation of client companies. The analysis unit can also use statistical analysis techniques to analyze industry trends. Furthermore, the analysis unit can use machine learning algorithms to analyze the activities of competitors. In this way, by analyzing the acquired information, it is possible to understand the current situation and market environment of client companies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the acquired information into a generating AI and have the generating AI perform the process of analyzing the current situation and market environment of client companies.

[0038] The data acquisition unit can analyze the client company's past data and select the optimal method for acquiring information. For example, the data acquisition unit can analyze the client company's past purchase history and prioritize the acquisition of relevant information. It can also analyze the client company's past inquiry history and quickly acquire necessary information. Furthermore, the data acquisition unit can analyze the client company's past feedback and acquire information on areas for improvement. In this way, the optimal method for acquiring information can be selected by analyzing the client company's past data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the client company's past data into a generating AI and have the generating AI select the optimal method for acquiring information.

[0039] The data acquisition unit can filter information based on the client company's current projects and areas of interest when acquiring it. For example, the data acquisition unit can prioritize acquiring information related to the client company's current projects. The data acquisition unit can also acquire relevant news and trend information based on the client company's areas of interest. Furthermore, the data acquisition unit can filter and acquire information specific to the client company's industry. This allows for the acquisition of highly relevant information by filtering information based on the client company's current projects and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the client company's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0040] The information acquisition unit can prioritize acquiring highly relevant information based on the geographical location information of the client company. For example, the acquisition unit can prioritize acquiring local news related to the location of the client company. The acquisition unit can also acquire the activities of nearby competitors based on the geographical location of the client company. Furthermore, the acquisition unit can prioritize acquiring legal and regulatory information related to the location of the client company. This allows for the provision of more appropriate information by acquiring highly relevant information based on the geographical location information of the client company. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the geographical location information of the client company into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0041] The acquisition unit can analyze the client company's social media activities and obtain relevant information when acquiring data. For example, the acquisition unit can analyze the client company's social media posts and obtain relevant news and trend information. It can also analyze the client company's social media engagement and obtain information that attracts customer interest. Furthermore, the acquisition unit can analyze the behavior of the client company's social media followers and obtain relevant information. In this way, relevant information can be obtained by analyzing the client company's social media activities. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the client company's social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. It can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial information. It can also apply a trend analysis algorithm to market trend information. Furthermore, it can apply a competitive analysis algorithm to the activities of competitors. By applying different analysis algorithms depending on the category of information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit can prioritize the analysis of information with high urgency. It can also quickly analyze information with an approaching submission deadline. Furthermore, it can perform a detailed analysis of information with ample time before submission. By determining the priority of analysis based on the information submission timing, it is possible to prioritize the analysis of information with high urgency. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission timing into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the information. This allows for the prioritization of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit will provide detailed information for highly important information. It can also provide simplified information for less important information. Furthermore, the delivery unit can determine the priority of delivery according to the importance of the information. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0047] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit can apply a financial delivery algorithm to financial information. It can also apply a trend delivery algorithm to market trend information. Furthermore, it can apply a competitor delivery algorithm to the activities of competitors. By applying different delivery algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate delivery algorithm.

[0048] The information provision department can determine the priority of information provision based on the submission timing at the time of provision. For example, the information provision department will provide information of high urgency first. The information provision department can also provide information with an approaching submission deadline quickly. Furthermore, the information provision department can provide detailed information for information with ample time before submission. In this way, by determining the priority of information provision based on the submission timing, information of high urgency can be provided first. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the submission timing of the information into a generating AI and have the generating AI perform the determination of the provision priority.

[0049] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit will prioritize the delivery of highly relevant information. It can also postpone the delivery of less relevant information. Furthermore, the delivery unit can determine the order of delivery according to the relevance of the information. By adjusting the order of delivery based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data acquisition unit can analyze the client company's past data and select the optimal method for acquiring information. For example, the data acquisition unit can analyze the client company's past purchase history and prioritize the acquisition of relevant information. It can also analyze the client company's past inquiry history and quickly acquire necessary information. Furthermore, the data acquisition unit can analyze the client company's past feedback and acquire information on areas for improvement. In this way, the optimal method for acquiring information can be selected by analyzing the client company's past data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the client company's past data into a generating AI and have the generating AI select the optimal method for acquiring information.

[0052] The information provider can filter information based on the client company's current projects and areas of interest when acquiring it. The information acquisition unit, for example, prioritizes acquiring information related to the client company's current projects. The information acquisition unit can also acquire relevant news and trend information based on the client company's areas of interest. Furthermore, the information acquisition unit can filter and acquire information specific to the client company's industry. This allows for the acquisition of highly relevant information by filtering information based on the client company's current projects and areas of interest. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without AI. For example, the information acquisition unit can input the client company's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0053] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. It can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0054] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial information. It can also apply a trend analysis algorithm to market trend information. Furthermore, it can apply a competitive analysis algorithm to the activities of competitors. By applying different analysis algorithms depending on the category of information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0055] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit will provide detailed information for highly important information. It can also provide simplified information for less important information. Furthermore, the delivery unit can determine the priority of delivery according to the importance of the information. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0056] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit can apply a financial delivery algorithm to financial information. It can also apply a trend delivery algorithm to market trend information. Furthermore, it can apply a competitor delivery algorithm to the activities of competitors. By applying different delivery algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate delivery algorithm.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires customer company information and business environment information. For example, it collects information such as the customer company's financial status, industry trends, and the activities of competitors. Specifically, it acquires the customer company's financial statements to understand its financial status. It also collects market research reports to understand industry trends and collects financial reports and marketing strategies of competitors to understand the activities of competitors. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, it uses data mining techniques to analyze the financial situation of client companies and statistical analysis techniques to analyze industry trends. Furthermore, it uses machine learning algorithms to analyze the activities of competitors. Step 3: The service department provides the information analyzed by the analysis department to the sales representative. For example, if a client company's financial situation is deteriorating, the service department will notify the sales representative of this information and propose appropriate countermeasures. They will also provide information to develop new value propositions based on industry trends and the actions of competitors. Furthermore, they will monitor the effectiveness of the proposed solutions and provide feedback as needed.

[0059] (Example of form 2) The system according to an embodiment of the present invention is a system that provides functions to mitigate or avoid the negative risks associated with lost deals. This system is a mechanism that uses an AI agent to acquire customer company information and business environment information and provides this information to the sales representative. For example, the system uses an AI agent to acquire customer company information and business environment information. Next, the system uses the AI ​​agent to analyze the acquired information and provides it to the sales representative. This mechanism allows the sales representative to understand the current situation and market environment of the customer company and take appropriate measures to mitigate or avoid the risk of lost deals. Furthermore, the system monitors the effectiveness of the proposals made by the sales representative and provides feedback as needed. This reduces or avoids the risk of lost deals and allows the sales representative to propose new value to the customer. As a result, the sales representative can enjoy achieving their goals and strengthen their relationship with the customer company. For example, the system uses an AI agent to collect information such as the customer company's financial situation, industry trends, and the actions of competitors. This allows the sales representative to understand the customer company's current situation and market environment. Next, the system uses the AI ​​agent to analyze the acquired information and provide it to the sales representative. For example, if the customer company's financial situation is deteriorating, the system notifies the sales representative of this information and proposes appropriate countermeasures. Furthermore, the system provides information to enable the creation of new value propositions based on industry trends and competitor activities. This mechanism allows sales representatives to understand the current situation and market environment of client companies and take appropriate measures to mitigate or avoid the risk of losing a deal. For example, if a client company's financial situation deteriorates, the sales representative can take countermeasures early to avoid losing the deal. In addition, by creating new value propositions based on industry trends and competitor activities, the quality of proposals to client companies can be improved. Moreover, the system monitors the effectiveness of the proposals made by sales representatives and provides feedback as needed. For example, it checks whether the proposed content was accepted by the client company, analyzes the reasons if it was not accepted, and provides information to be used for future proposals. In this way, by utilizing the AI ​​agent, the risk of losing a deal can be mitigated or avoided, and new value can be proposed to customers.This allows sales representatives to enjoy achieving their targets and strengthen relationships with client companies. The system, in turn, reduces or avoids the risk of lost deals and enables the proposal of new value to customers.

[0060] The system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires customer company information and business environment information. The acquisition unit collects information such as the customer company's financial status, industry trends, and the activities of competitors. For example, the acquisition unit acquires the customer company's financial statements to understand its financial status. The acquisition unit can also collect market research reports to understand industry trends. Furthermore, the acquisition unit can collect financial reports and marketing strategies of competitors to understand their activities. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit uses data mining technology to analyze the customer company's financial status. The analysis unit can also use statistical analysis technology to analyze industry trends. Furthermore, the analysis unit can use machine learning algorithms to analyze the activities of competitors. The provision unit provides the information analyzed by the analysis unit to the sales representative. For example, if the customer company's financial status is deteriorating, the provision unit notifies the sales representative of this information and proposes appropriate countermeasures. Furthermore, the service provider can provide information to make new value propositions based on industry trends and the actions of competitors. In addition, the service provider can monitor the effectiveness of the proposed content and provide feedback as needed. As a result, the system according to this embodiment can reduce or avoid the risk of losing a deal by acquiring, analyzing, and providing customer company information and business environment information.

[0061] The data acquisition unit acquires customer company information and business environment information. Specifically, it collects information such as the financial status of customer companies, industry trends, and the activities of competitors. To understand the financial status of customer companies, the data acquisition unit collects detailed financial data such as financial statements, cash flow statements, and income statements. This data is important for evaluating the management status and financial health of companies. In addition, the data acquisition unit collects market research reports and industry analysis reports to understand industry trends. This includes industry growth rates, market share of major players, and the adoption status of new technologies. Furthermore, to understand the activities of competitors, the data acquisition unit collects information such as the financial reports, marketing strategies, product lineups, and pricing strategies of competitors. This allows for a detailed understanding of the competitive environment faced by customer companies. The data acquisition unit can centrally manage this information and update it in real time as needed. For example, it can use web scraping technology to automatically collect publicly available information on the internet and store it in a database. It can also use APIs to obtain the latest market data and financial data from external data providers. This allows the data acquisition unit to always collect the latest information and improve the accuracy of the information throughout the system.

[0062] The Analysis Department analyzes the information acquired by the Acquisition Department. Specifically, it uses data mining techniques to analyze the financial status of client companies. Data mining techniques include clustering, association analysis, and regression analysis, which are used to extract useful patterns and trends from the client company's financial data. Statistical analysis techniques can also be used to analyze industry trends. For example, time series analysis is used to predict future market trends from historical data. Furthermore, machine learning algorithms can be used to analyze the activities of competitors. Machine learning algorithms include support vector machines, random forests, and neural networks, which are used to analyze the marketing strategies and product success factors of competitors. The Analysis Department utilizes these techniques to comprehensively analyze the acquired data and evaluate the current situation and future risks of client companies. Furthermore, the Analysis Department visualizes the analysis results so that sales representatives can easily understand them. For example, dashboards are used to display financial status, market trends, and competitor activities in graphs and charts. This allows the Analysis Department to provide sales representatives with information that enables them to make quick and accurate decisions.

[0063] The service provider provides the sales representative with information analyzed by the analytics department. Specifically, if a client company's financial situation deteriorates, the service provider notifies the sales representative and proposes appropriate countermeasures. For example, if a client company's cash flow deteriorates, the service provider will propose a review of payment terms or additional financing to the sales representative. The service provider can also provide information to create new value propositions based on industry trends and competitor activities. For example, in industries where new technologies are being adopted, the service provider may propose technology investments to help client companies maintain their competitiveness. Furthermore, the service provider can monitor the effectiveness of the proposals and provide feedback as needed. For example, after the proposed measures are implemented, the service provider quantitatively evaluates their effectiveness and identifies areas for improvement. This allows the service provider to support sales representatives in making optimal proposals to client companies and reducing or avoiding the risk of losing deals. In addition, the service provider provides tools and platforms to facilitate communication with sales representatives. For example, chatbots and AI assistants can be used to enable sales representatives to quickly obtain the information they need. This allows the service provider to support sales representatives in providing prompt and accurate responses to client companies, thereby maximizing the overall effectiveness of the system.

[0064] The service department can notify the sales representative of a client company's deteriorating financial condition and propose appropriate countermeasures. For example, the service department can notify the sales representative if a client company's financial indicators fall below a certain threshold. The service department can also notify the sales representative if the client company has a series of consecutive losses. Furthermore, the service department can notify the sales representative if a client company's cash flow is deteriorating. This allows for the rapid proposal of countermeasures when a client company's financial condition deteriorates, thereby reducing or avoiding the risk of losing a deal. Some or all of the above processes performed by the service department may be carried out using AI, for example, or not. For example, the service department can input the client company's financial data into a generating AI and have the generating AI perform a process to detect deterioration in the financial condition.

[0065] The service provider can provide information to create new value propositions based on industry trends and the actions of competitors. For example, the service provider can grasp industry trends based on market research reports and provide that information to the sales representative. The service provider can also collect industry news and provide the sales representative with the latest industry developments. Furthermore, the service provider can collect expert opinions and provide the sales representative with future industry forecasts. This allows the service provider to improve the quality of proposals to client companies by creating new value propositions based on industry trends and the actions of competitors. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input market research reports and industry news into a generating AI and have the generating AI perform the process of analyzing industry trends.

[0066] The service department can monitor the effectiveness of the proposal and provide feedback as needed. For example, the service department can verify whether the proposal was accepted by the client company. If the proposal was not accepted, the service department can also analyze the reasons and provide information to be used for future proposals. Furthermore, the service department can evaluate the impact the proposal had on the client company and provide feedback on the results to the sales representative. This allows for monitoring the effectiveness of the proposal and providing feedback to help improve the proposal. Some or all of the above processes in the service department may be performed using AI, for example, or not. For example, the service department can input the acceptance status of the proposal into a generating AI and have the generating AI perform a process to analyze the reasons why it was not accepted.

[0067] The data acquisition unit can collect information such as the financial status of client companies, industry trends, and the activities of competitors. For example, the data acquisition unit can collect financial statements of client companies to understand their financial status. It can also collect market research reports to understand industry trends. Furthermore, it can collect financial reports and marketing strategies of competitors to understand their activities. By collecting information such as the financial status of client companies, industry trends, and the activities of competitors, it is possible to understand the current situation and market environment of client companies. Some or all of the above processes in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can input the financial statements of client companies into a generating AI and have the generating AI perform the process of analyzing the financial status.

[0068] The analysis unit can analyze the acquired information to understand the current situation and market environment of client companies. For example, the analysis unit can use data mining techniques to analyze the financial situation of client companies. The analysis unit can also use statistical analysis techniques to analyze industry trends. Furthermore, the analysis unit can use machine learning algorithms to analyze the activities of competitors. In this way, by analyzing the acquired information, it is possible to understand the current situation and market environment of client companies. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the acquired information into a generating AI and have the generating AI perform the process of analyzing the current situation and market environment of client companies.

[0069] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of information acquisition and acquire only important information. Conversely, if the user is relaxed, the acquisition unit can increase the frequency of information acquisition and acquire more detailed information. Furthermore, if the user is in a hurry, the acquisition unit can acquire information in real time and provide it quickly. This allows for information to be acquired at a more appropriate time by adjusting the timing of information acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of information acquisition timing based on emotions.

[0070] The data acquisition unit can analyze the client company's past data and select the optimal method for acquiring information. For example, the data acquisition unit can analyze the client company's past purchase history and prioritize the acquisition of relevant information. It can also analyze the client company's past inquiry history and quickly acquire necessary information. Furthermore, the data acquisition unit can analyze the client company's past feedback and acquire information on areas for improvement. In this way, the optimal method for acquiring information can be selected by analyzing the client company's past data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the client company's past data into a generating AI and have the generating AI select the optimal method for acquiring information.

[0071] The data acquisition unit can filter information based on the client company's current projects and areas of interest when acquiring it. For example, the data acquisition unit can prioritize acquiring information related to the client company's current projects. The data acquisition unit can also acquire relevant news and trend information based on the client company's areas of interest. Furthermore, the data acquisition unit can filter and acquire information specific to the client company's industry. This allows for the acquisition of highly relevant information by filtering information based on the client company's current projects and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the client company's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0072] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring information of high importance. If the user is relaxed, the data acquisition unit can also prioritize acquiring detailed information. Furthermore, if the user is in a hurry, the data acquisition unit can prioritize acquiring information that can be retrieved quickly. This allows for the priority acquisition of important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0073] The information acquisition unit can prioritize acquiring highly relevant information based on the geographical location information of the client company. For example, the acquisition unit can prioritize acquiring local news related to the location of the client company. The acquisition unit can also acquire the activities of nearby competitors based on the geographical location of the client company. Furthermore, the acquisition unit can prioritize acquiring legal and regulatory information related to the location of the client company. This allows for the provision of more appropriate information by acquiring highly relevant information based on the geographical location information of the client company. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the geographical location information of the client company into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0074] The acquisition unit can analyze the client company's social media activities and obtain relevant information when acquiring data. For example, the acquisition unit can analyze the client company's social media posts and obtain relevant news and trend information. It can also analyze the client company's social media engagement and obtain information that attracts customer interest. Furthermore, the acquisition unit can analyze the behavior of the client company's social media followers and obtain relevant information. In this way, relevant information can be obtained by analyzing the client company's social media activities. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the client company's social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. It can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial information. It can also apply a trend analysis algorithm to market trend information. Furthermore, it can apply a competitive analysis algorithm to the activities of competitors. By applying different analysis algorithms depending on the category of information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0079] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit can prioritize the analysis of information with high urgency. It can also quickly analyze information with an approaching submission deadline. Furthermore, it can perform a detailed analysis of information with ample time before submission. By determining the priority of analysis based on the information submission timing, it is possible to prioritize the analysis of information with high urgency. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission timing into a generating AI and have the generating AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the information. This allows for the prioritization of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The service provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a presentation that gets straight to the point. By adjusting the presentation of the information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the presentation of the information.

[0082] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit will provide detailed information for highly important information. It can also provide simplified information for less important information. Furthermore, the delivery unit can determine the priority of delivery according to the importance of the information. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0083] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit can apply a financial delivery algorithm to financial information. It can also apply a trend delivery algorithm to market trend information. Furthermore, it can apply a competitor delivery algorithm to the activities of competitors. By applying different delivery algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate delivery algorithm.

[0084] The delivery unit can estimate the user's emotions and adjust the length of the delivery based on the estimated emotions. For example, if the user is in a hurry, the delivery unit can provide a short, concise delivery. If the user is relaxed, the delivery unit can provide a detailed delivery. Furthermore, if the user is excited, the delivery unit can provide a visually stimulating delivery. By adjusting the length of the delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the length of the delivery.

[0085] The information provision department can determine the priority of information provision based on the submission timing at the time of provision. For example, the information provision department will provide information of high urgency first. The information provision department can also provide information with an approaching submission deadline quickly. Furthermore, the information provision department can provide detailed information for information with ample time before submission. In this way, by determining the priority of information provision based on the submission timing, information of high urgency can be provided first. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the submission timing of the information into a generating AI and have the generating AI perform the determination of the provision priority.

[0086] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit will prioritize the delivery of highly relevant information. It can also postpone the delivery of less relevant information. Furthermore, the delivery unit can determine the order of delivery according to the relevance of the information. By adjusting the order of delivery based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data acquisition unit can analyze the client company's past data and select the optimal method for acquiring information. For example, the data acquisition unit can analyze the client company's past purchase history and prioritize the acquisition of relevant information. It can also analyze the client company's past inquiry history and quickly acquire necessary information. Furthermore, the data acquisition unit can analyze the client company's past feedback and acquire information on areas for improvement. In this way, the optimal method for acquiring information can be selected by analyzing the client company's past data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the client company's past data into a generating AI and have the generating AI select the optimal method for acquiring information.

[0089] The information provider can filter information based on the client company's current projects and areas of interest when acquiring it. The information acquisition unit, for example, prioritizes acquiring information related to the client company's current projects. The information acquisition unit can also acquire relevant news and trend information based on the client company's areas of interest. Furthermore, the information acquisition unit can filter and acquire information specific to the client company's industry. This allows for the acquisition of highly relevant information by filtering information based on the client company's current projects and areas of interest. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without AI. For example, the information acquisition unit can input the client company's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0090] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of information acquisition and acquire only important information. Conversely, if the user is relaxed, the acquisition unit can increase the frequency of information acquisition and acquire more detailed information. Furthermore, if the user is in a hurry, the acquisition unit can acquire information in real time and provide it quickly. This allows for information to be acquired at a more appropriate time by adjusting the timing of information acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of information acquisition timing based on emotions.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. It can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for detailed analysis of important information by adjusting the level of detail based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial information. It can also apply a trend analysis algorithm to market trend information. Furthermore, it can apply a competitive analysis algorithm to the activities of competitors. By applying different analysis algorithms depending on the category of information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0094] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0095] The service provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a presentation that gets straight to the point. By adjusting the presentation of the information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the presentation of the information.

[0096] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit will provide detailed information for highly important information. It can also provide simplified information for less important information. Furthermore, the delivery unit can determine the priority of delivery according to the importance of the information. This allows important information to be provided in detail by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0097] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit can apply a financial delivery algorithm to financial information. It can also apply a trend delivery algorithm to market trend information. Furthermore, it can apply a competitor delivery algorithm to the activities of competitors. By applying different delivery algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI and have the generating AI execute the application of an appropriate delivery algorithm.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The acquisition unit acquires customer company information and business environment information. For example, it collects information such as the customer company's financial status, industry trends, and the activities of competitors. Specifically, it acquires the customer company's financial statements to understand its financial status. It also collects market research reports to understand industry trends and collects financial reports and marketing strategies of competitors to understand the activities of competitors. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, it uses data mining techniques to analyze the financial situation of client companies and statistical analysis techniques to analyze industry trends. Furthermore, it uses machine learning algorithms to analyze the activities of competitors. Step 3: The service department provides the information analyzed by the analysis department to the sales representative. For example, if a client company's financial situation is deteriorating, the service department will notify the sales representative of this information and propose appropriate countermeasures. They will also provide information to develop new value propositions based on industry trends and the actions of competitors. Furthermore, they will monitor the effectiveness of the proposed solutions and provide feedback as needed.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 38B of the smart device 14 to collect customer company information and business environment information, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and notifies the sales representative of the analysis results and proposes appropriate countermeasures. The provision unit may be implemented in the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect customer company information and business environment information, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the acquired information. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and notifies the sales representative of the analysis results and proposes appropriate countermeasures. The provision unit may be implemented, for example, in the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect customer company information and business environment information, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and notifies the sales representative of the analysis results and proposes appropriate countermeasures. The provision unit may be implemented in the control unit 46A of the headset terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and microphone 238 of the robot 414 to collect customer company information and business environment information, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and notifies the sales representative of the analysis results and proposes appropriate countermeasures. The provision unit may be implemented in the control unit 46A of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) The acquisition unit acquires customer company information and business environment information, An analysis unit analyzes the information acquired by the acquisition unit, The system includes a provisioning unit that provides the information analyzed by the aforementioned analysis unit to the sales representative. A system characterized by the following features. (Note 2) The aforementioned supply unit is, If a client company's financial situation deteriorates, we will notify the sales representative of this information and propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide information to create new value propositions based on industry trends and the actions of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Monitor the effectiveness of the proposed solutions and provide feedback as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, We collect information such as the financial status of client companies, industry trends, and the activities of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The acquired information is analyzed to understand the current situation and market environment of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, We analyze past data from client companies and select the most suitable method for obtaining information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring information, filtering is performed based on the client company's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring information, the system prioritizes acquiring highly relevant information based on the geographical location of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring information, we analyze the social media activities of client companies and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated 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 information. 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 information. 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 the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. 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 information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 acquisition unit acquires customer company information and business environment information, An analysis unit analyzes the information acquired by the acquisition unit, The system includes a provisioning unit that provides the information analyzed by the aforementioned analysis unit to the sales representative. A system characterized by the following features.

2. The aforementioned supply unit is, If a client company's financial situation deteriorates, we will notify the sales representative of this information and propose appropriate countermeasures. The system according to feature 1.

3. The aforementioned supply unit is, We provide information to create new value propositions based on industry trends and the actions of competitors. The system according to feature 1.

4. The aforementioned supply unit is, Monitor the effectiveness of the proposed solutions and provide feedback as needed. The system according to feature 1.

5. The acquisition unit is, We collect information such as the financial status of client companies, industry trends, and the activities of competitors. The system according to feature 1.

6. The aforementioned analysis unit, The acquired information is analyzed to understand the current situation and market environment of the client company. The system according to feature 1.

7. The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, We analyze past data from client companies and select the most suitable method for obtaining information. The system according to feature 1.

9. The acquisition unit is, When acquiring information, filtering is performed based on the client company's current projects and areas of interest. The system according to feature 1.

10. The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system according to feature 1.

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