system

Abstract

The system according to this embodiment aims to identify the risk of losing a deal based on negotiation information and to provide appropriate countermeasures. [Solution] The system according to the embodiment comprises an acquisition unit, a summary acquisition unit, and a notification unit. The acquisition unit acquires business negotiation information. The summary acquisition unit acquires summary information of the business negotiation company and business environment based on the business negotiation information acquired by the acquisition unit. The notification unit notifies the customer of the risk of losing the deal and proposed risk countermeasures based on the summary information acquired by the summary acquisition unit.

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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the grasp of the risk of losing customers based on negotiation information and the provision of countermeasures have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to grasp the risk of losing customers based on negotiation information and provide appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a summary acquisition unit, and a notification unit. The acquisition unit acquires business opportunity information. The summary acquisition unit acquires summary information of the business partner company and business environment based on the business opportunity information acquired by the acquisition unit. The notification unit notifies the business partner of the risk of losing the deal and proposed risk mitigation measures based on the summary information acquired by the summary acquisition unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the risk of losing a deal based on negotiation information and provide appropriate countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The sales opportunity information acquisition and provision system according to an embodiment of the present invention is a system that acquires and provides sales opportunity information using an AI agent. This system has the function of acquiring sales opportunity information from a sales opportunity management tool, acquiring summary information on the prospective client company and business environment, and notifying the risk of lost deals and proposed risk countermeasures. For example, if the sales opportunity management tool is Chatter, sales opportunity information is acquired via the data loader. Next, summary information on the prospective client company and business environment is acquired via the Perplexity API. This makes it possible to understand the situation of the prospective client company and its business environment. Furthermore, the sales representative is notified of the risk of lost deals and proposed risk countermeasures via the communication tool API. This mechanism makes it possible to detect the risk of lost deals early and take appropriate measures. First, sales opportunity information is acquired from a sales opportunity management tool. The sales opportunity management tool can be any service that has API functionality. For example, in the case of Chatter, sales opportunity information is acquired via the data loader. This information includes the progress of the deal and customer information. Next, summary information on the prospective client company and business environment is acquired via the Perplexity API. The Perplexity API provides information on the business environment of potential clients and the activities of competitors. Based on this information, it is possible to understand the situation of potential clients and assess the risk of losing a deal. Furthermore, it notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures via the communication tool API. This allows the sales representative to quickly understand the risk of losing the deal and take appropriate measures. This mechanism allows for early detection of the risk of losing a deal and the implementation of appropriate countermeasures, thereby increasing the closing rate. For example, by identifying risk factors such as a change in management or deterioration of performance at a potential client company early and taking appropriate measures, it is possible to avoid losing a deal. It also allows for quick responses to changes in the business environment, such as the activities of competitors and legal revisions and regulations. As a result, the deal information acquisition and provision system can efficiently acquire deal information, acquire summary information, and notify customers of the risk of losing a deal and proposed risk mitigation measures.

[0029] The business opportunity information acquisition and provision system according to the embodiment comprises an acquisition unit, a summary acquisition unit, and a notification unit. The acquisition unit acquires business opportunity information. The acquisition unit acquires business opportunity information from, for example, a business opportunity management tool. The business opportunity management tool includes, for example, a CRM system or a sales support tool. The acquisition unit can acquire business opportunity information using the API of the business opportunity management tool. For example, the acquisition unit acquires business opportunity information by calling the API of the business opportunity management tool. The acquisition unit can also acquire business opportunity information by importing data exported from the business opportunity management tool. For example, the acquisition unit acquires business opportunity information by reading a CSV file exported from the business opportunity management tool. Furthermore, the acquisition unit can also acquire business opportunity information by directly accessing the database of the business opportunity management tool. For example, the acquisition unit acquires business opportunity information by executing an SQL query on the database of the business opportunity management tool. The summary acquisition unit acquires summary information of the target company and business environment. The summary acquisition unit acquires summary information of the target company and business environment via, for example, the Perplexity API. The Perplexity API provides information such as the business environment and competitor activities of potential clients. The summary retrieval unit can call the Perplexity API to obtain summary information about potential clients and their business environment. For example, the summary retrieval unit can provide the name of a potential client as input to the Perplexity API and retrieve summary information about the company's business environment and competitor activities. The summary retrieval unit can also obtain information about the business environment from the potential client's website or publicly available reports. For example, the summary retrieval unit can crawl the potential client's website to collect information about the business environment. Furthermore, the summary retrieval unit can obtain information about the business environment from the potential client's industry reports and market research reports. For example, the summary retrieval unit can analyze industry reports to obtain information about the potential client's business environment. The notification unit notifies clients of the risk of losing a deal and proposed risk mitigation measures. The notification unit notifies the sales representative of the risk of losing a deal and proposed risk mitigation measures, for example, via a communication tool API. The notification unit can also notify clients of the risk of losing a deal and proposed risk mitigation measures via email.For example, the notification unit sends information about the risk of losing a deal and proposed risk mitigation measures to the sales representative via the mail server. Furthermore, the notification unit can also use a mobile app to notify the sales representative of the risk of losing a deal and proposed risk mitigation measures. For example, the notification unit notifies the sales representative of the risk of losing a deal and proposed risk mitigation measures via the mobile app. This allows the deal information acquisition and provision system to efficiently acquire deal information, acquire summary information, and notify the sales representative of the risk of losing a deal and proposed risk mitigation measures.

[0030] The acquisition unit acquires sales opportunity information. For example, the acquisition unit acquires sales opportunity information from a sales opportunity management tool. Sales opportunity management tools include, for example, CRM systems and sales support tools. The acquisition unit can acquire sales opportunity information using the sales opportunity management tool's API. Specifically, the acquisition unit acquires sales opportunity information by calling the sales opportunity management tool's API. For example, the acquisition unit acquires sales opportunity information by calling the sales opportunity management tool's API. Furthermore, the acquisition unit can acquire sales opportunity information by importing data exported from the sales opportunity management tool. For example, the acquisition unit acquires sales opportunity information by reading a CSV file exported from the sales opportunity management tool. In addition, the acquisition unit can acquire sales opportunity information by directly accessing the sales opportunity management tool's database. For example, the acquisition unit acquires sales opportunity information by executing an SQL query on the sales opportunity management tool's database. This allows the acquisition unit to acquire sales opportunity information using the sales opportunity management tool's API. For example, the acquisition unit acquires sales opportunity information by calling the sales opportunity management tool's API. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to obtain opportunity information. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can obtain opportunity information by reading a CSV file exported from the opportunity management tool. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool.For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can execute an SQL query on the database of the opportunity management tool to retrieve opportunity information. This allows the retrieval unit to retrieve opportunity information using the API of the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can execute an SQL query on the database of the opportunity management tool to retrieve opportunity information. This allows the retrieval unit to obtain opportunity information using the opportunity management tool's API. For example, the retrieval unit can retrieve opportunity information by calling the opportunity management tool's API. The retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool and retrieve opportunity information. Furthermore, the retrieval unit can also obtain opportunity information by directly accessing the opportunity management tool's database. For example, the retrieval unit can execute an SQL query on the opportunity management tool's database to retrieve opportunity information. This allows the retrieval unit to obtain opportunity information using the opportunity management tool's API. For example, the retrieval unit can retrieve opportunity information by calling the opportunity management tool's API. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool and retrieve opportunity information.Furthermore, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. The retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool and obtain opportunity information. Furthermore, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. The retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the sales opportunity management tool to obtain sales opportunity information. Furthermore, the retrieval unit can also directly access the sales opportunity management tool's database to obtain sales opportunity information. For example, the retrieval unit can execute an SQL query on the sales opportunity management tool's database to obtain sales opportunity information. This allows the retrieval unit to obtain sales opportunity information using the sales opportunity management tool's API. For example, the retrieval unit can call the sales opportunity management tool's API to obtain sales opportunity information. Additionally, the retrieval unit can import data exported from the sales opportunity management tool to obtain sales opportunity information. For example, the retrieval unit can read a CSV file exported from the sales opportunity management tool to obtain sales opportunity information. Furthermore, the retrieval unit can also directly access the sales opportunity management tool's database to obtain sales opportunity information. For example, the retrieval unit can execute an SQL query on the sales opportunity management tool's database to obtain sales opportunity information. This allows the retrieval unit to obtain sales opportunity information using the sales opportunity management tool's API. For example, the retrieval unit can call the sales opportunity management tool's API to obtain sales opportunity information.Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to obtain opportunity information. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can obtain opportunity information by reading a CSV file exported from the opportunity management tool. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can execute an SQL query on the database of the opportunity management tool to retrieve opportunity information. Thus, the retrieval unit can retrieve opportunity information using the API of the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool.For example, the retrieval unit can retrieve opportunity information by executing an SQL query into the opportunity management tool's database. This allows the retrieval unit to retrieve opportunity information using the opportunity management tool's API. For example, the retrieval unit can retrieve opportunity information by calling the opportunity management tool's API. The retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by reading a CSV file exported from the opportunity management tool. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the opportunity management tool's database. For example, the retrieval unit can retrieve opportunity information by executing an SQL query into the opportunity management tool's database. This allows the retrieval unit to retrieve opportunity information using the opportunity management tool's API.

[0031] The summary acquisition unit obtains summary information about potential business partners and their business environment. For example, the summary acquisition unit obtains this information via the Perplexity API. The Perplexity API provides information such as the business environment of potential business partners and the activities of their competitors. The summary acquisition unit can call the Perplexity API to obtain summary information about potential business partners and their business environment. For example, the summary acquisition unit provides the name of a potential business partner as input to the Perplexity API and obtains summary information about the business environment and the activities of its competitors. The summary acquisition unit can also obtain information about the business environment from the potential business partner's website or publicly available reports. For example, the summary acquisition unit crawls the potential business partner's website to collect information about the business environment. Furthermore, the summary acquisition unit can obtain information about the business environment from the potential business partner's industry reports and market research reports. For example, the summary acquisition unit analyzes industry reports to obtain information about the potential business partner's business environment. This allows the summary acquisition unit to efficiently obtain summary information on potential business partners and their business environment. Furthermore, the summary acquisition unit can analyze the acquired information using AI to identify the strengths and weaknesses, opportunities, and threats of potential business partners. For example, the summary acquisition unit uses AI to analyze the financial situation, market share, and competitor activities of potential business partners to identify their strengths and weaknesses. It also uses AI to analyze market trends and industry trends to identify opportunities and threats for potential business partners. As a result, the summary acquisition unit can efficiently acquire summary information on potential business partners and their business environment, providing information to formulate strategies for successful business negotiations.

[0032] The notification unit notifies users of the risk of lost sales and proposed risk mitigation measures. For example, the notification unit notifies the sales representative of the risk of lost sales and proposed risk mitigation measures via a communication tool API. The notification unit can also notify users of the risk of lost sales and proposed risk mitigation measures via email. For example, the notification unit sends the risk of lost sales and proposed risk mitigation measures to the sales representative via an email server. Furthermore, the notification unit can also notify users of the risk of lost sales and proposed risk mitigation measures using a mobile app. For example, the notification unit notifies the sales representative of the risk of lost sales and proposed risk mitigation measures via a mobile app. This allows the notification unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, based on feedback from users who received evacuation instructions, evacuation routes may be reviewed and instructions may be improved. The notification unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0033] The acquisition unit can acquire deal information from a deal management tool. For example, the acquisition unit can acquire deal information from a deal management tool. The acquisition unit can acquire deal information using the API of the deal management tool. For example, the acquisition unit can acquire deal information by calling the API of the deal management tool. The acquisition unit can also acquire deal information by importing data exported from the deal management tool. For example, the acquisition unit can acquire deal information by reading a CSV file exported from the deal management tool. Furthermore, the acquisition unit can acquire deal information by directly accessing the database of the deal management tool. For example, the acquisition unit can acquire deal information by executing an SQL query on the database of the deal management tool. This allows the acquisition unit to efficiently acquire deal information from the deal management tool. The deal management tool includes, for example, CRM systems and sales support tools. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to optimize the acquisition of deal information when calling the API of a deal management tool to obtain deal information.

[0034] The summary acquisition unit can obtain information such as the business environment and competitor trends of a potential business partner. For example, the summary acquisition unit can obtain summary information about the potential business partner and its business environment via the Perplexity API. The Perplexity API provides information such as the business environment and competitor trends of a potential business partner. The summary acquisition unit can call the Perplexity API to obtain summary information about the potential business partner and its business environment. For example, the summary acquisition unit provides the name of the potential business partner as input to the Perplexity API and obtains summary information regarding the business environment and competitor trends of the potential business partner. The summary acquisition unit can also obtain information about the business environment from the potential business partner's website or publicly available reports. For example, the summary acquisition unit crawls the potential business partner's website to collect information about the business environment. Furthermore, the summary acquisition unit can obtain information about the business environment from the potential business partner's industry reports and market research reports. For example, the summary acquisition unit analyzes industry reports to obtain information about the potential business partner's business environment. This allows the summary acquisition unit to understand the business environment of the prospective client company and the activities of its competitors. The business environment includes, for example, market trends, economic conditions, and industry trends. The activities of competitors include, for example, information on competitors' products and marketing strategies. Some or all of the above processing in the summary acquisition unit may be performed using AI, or not. For example, when the summary acquisition unit calls the Perplexity API to acquire summary information on the prospective client company and its business environment, it can use AI to optimize the information acquisition.

[0035] The notification unit can notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures. The notification unit can notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via a communication tool API, for example. The notification unit can also notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via email. For example, the notification unit can send the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via an email server. Furthermore, the notification unit can also notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via a mobile app. For example, the notification unit can notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via a mobile app. This allows the notification unit to enable the sales representative in charge to quickly grasp the risk of losing a deal and proposed risk mitigation measures. Sales representatives include, for example, sales representatives and account managers. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI.

[0036] The business negotiation information acquisition and provision system according to the embodiment includes a generation unit that generates risk mitigation proposals. The generation unit generates risk mitigation proposals. The generation unit generates risk mitigation proposals using, for example, AI. The generation unit can generate risk mitigation proposals by executing a risk mitigation proposal generation algorithm. For example, the generation unit analyzes past business negotiation data and generates risk mitigation proposals based on mitigation proposals for successful business negotiations. The generation unit can also analyze data from failed business negotiations and generate risk mitigation proposals that reflect areas for improvement. For example, the generation unit inputs past business negotiation data into AI, and the AI ​​analyzes the data to generate risk mitigation proposals. Furthermore, the generation unit can also customize risk mitigation proposals based on the current status of the business negotiation. For example, the generation unit generates risk mitigation proposals considering the progress of the business negotiation and current risk factors. This allows the generation unit to automatically generate risk mitigation proposals. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without using AI. For example, the generation unit can optimize the generation of risk mitigation proposals using AI.

[0037] The acquisition unit can adjust the level of detail of the information it acquires according to the progress of the negotiation. For example, the acquisition unit adjusts the level of detail of the information it acquires based on the progress of the negotiation. In the early stages of the negotiation, the acquisition unit can acquire only basic information. For example, in the early stages of the negotiation, the acquisition unit acquires basic information such as the customer's basic information and the purpose of the negotiation. In the middle stages of the negotiation, the acquisition unit can acquire detailed customer information and competitor information. For example, in the middle stages of the negotiation, the acquisition unit acquires detailed information such as the customer's needs and information on competitors. In the final stages of the negotiation, the acquisition unit can acquire information on the contents of the contract and price negotiations. For example, in the final stages of the negotiation, the acquisition unit acquires information on the contents of the contract and price negotiations. This allows the acquisition unit to acquire appropriate information according to the progress of the negotiation. The progress of the negotiation includes, for example, the phase of the negotiation and progress reports. The level of detail of the information includes, for example, detailed data and summary information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the information acquisition unit can use AI to optimize information acquisition when adjusting the level of detail of the information acquired based on the progress of the business negotiation.

[0038] The information acquisition unit can determine the priority of information to acquire based on the importance of the business deal. For example, the information acquisition unit can prioritize the acquisition of detailed information for high-priority business deals. For example, the information acquisition unit prioritizes acquiring detailed customer information and competitor information for high-priority business deals. For low-priority business deals, the information acquisition unit can acquire only basic information. For example, the information acquisition unit acquires basic customer information and the purpose of the business deal for low-priority business deals. For medium-priority business deals, the information acquisition unit can acquire detailed information as needed. For example, the information acquisition unit acquires customer needs and competitor information as needed for medium-priority business deals. This allows the information acquisition unit to prioritize the acquisition of appropriate information according to the importance of the business deal. The importance of a business deal includes, for example, the size of the deal and the impact of the deal. The priority of information includes, for example, a prioritization method based on importance. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use AI to optimize information acquisition when determining the priority of information to acquire based on the importance of the business opportunity.

[0039] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the geographical location of the business negotiation. For example, the data acquisition unit prioritizes the acquisition of highly relevant information by considering the geographical location of the business negotiation. The data acquisition unit can prioritize the acquisition of region-specific information based on the location of the business negotiation company. For example, the data acquisition unit prioritizes the acquisition of region-specific information such as local market trends and competitor information based on the location of the business negotiation company. The data acquisition unit can prioritize the acquisition of competitor information in the vicinity of the business negotiation company. For example, the data acquisition unit prioritizes the acquisition of competitor information such as product information and marketing strategies of competitors in the vicinity of the business negotiation company. The data acquisition unit can prioritize the acquisition of legal and regulatory information related to the location of the business negotiation company. For example, the data acquisition unit prioritizes the acquisition of legal and regulatory information and industry regulations related to the location of the business negotiation company. This allows the data acquisition unit to acquire highly relevant information based on the geographical location of the business negotiation. Geographic location information includes, for example, GPS data and geographic information systems. Highly relevant information includes, for example, geographically relevant data and region-specific information. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use AI to optimize information acquisition when acquiring highly relevant information while considering the geographical location information of business negotiations.

[0040] The acquisition unit can analyze the past history of a business deal and acquire relevant information. For example, the acquisition unit can analyze the past history of a business deal and acquire relevant information. The acquisition unit can prioritize acquiring information on successful business deals from the past business deal history. For example, the acquisition unit can analyze the past business deal history and acquire relevant information based on the information of successful business deals. The acquisition unit can analyze information on failed business deals from the past business deal history and acquire areas for improvement. For example, the acquisition unit can analyze the past business deal history and acquire areas for improvement based on the information of failed business deals. The acquisition unit can acquire information on similar business deals from the past business deal history. For example, the acquisition unit can analyze the past business deal history and acquire relevant information based on the information of similar business deals. In this way, the acquisition unit can acquire relevant information based on the past history of a business deal. Past history includes, for example, past business deal data and history databases. Relevant information includes, for example, data related to past business deals and information based on history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the data acquisition unit can use AI to optimize information acquisition when analyzing the past history of business negotiations to obtain relevant information.

[0041] The summary acquisition unit can adjust the level of detail of the information it acquires based on the performance and market trends of the client company. For example, if the client company's performance is good, the summary acquisition unit can acquire detailed market trend information. For example, if the client company's performance is good, the summary acquisition unit can acquire detailed market trend information. If the client company's performance is poor, the summary acquisition unit can acquire information on risk factors. For example, if the client company's performance is poor, the summary acquisition unit can acquire information on risk factors. If the client company's performance is stable, the summary acquisition unit can acquire information on the activities of competitors. For example, if the client company's performance is stable, the summary acquisition unit can acquire information on the activities of competitors. This allows the summary acquisition unit to acquire appropriate information according to the client company's performance and market trends. Performance includes, for example, sales data and financial reports. Market trends include, for example, market reports and industry analysis data. The level of detail of the information includes, for example, detailed data and summary information. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or not using AI. For example, the summary acquisition unit can use AI to optimize information acquisition when adjusting the level of detail of the information acquired based on the performance and market trends of the business partner company.

[0042] The summary acquisition unit can apply different acquisition algorithms depending on the category of the client company. For example, if the client company is a manufacturer, the summary acquisition unit can acquire information on product lineup and production capacity. For example, if the client company is a service company, the summary acquisition unit can acquire information on customer satisfaction and service quality. For example, if the client company is an IT company, the summary acquisition unit can acquire information on technology trends and competing technologies. This allows the summary acquisition unit to acquire appropriate information depending on the category of the client company. Categories include, for example, industry categories and product categories. Acquisition algorithms include, for example, machine learning algorithms and data mining algorithms. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or without AI. For example, the summary acquisition unit can use AI to optimize information acquisition when applying different acquisition algorithms depending on the category of the business partner company.

[0043] The summary acquisition unit can prioritize the acquisition of highly relevant information by considering the geographical location information of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of highly relevant information by considering the geographical location information of the business partner company. The summary acquisition unit can prioritize the acquisition of region-specific market trend information based on the location of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of region-specific information such as regional market trends and competitor information based on the location of the business partner company. The summary acquisition unit can prioritize the acquisition of competitor information in the vicinity of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of competitor information such as product information and marketing strategies of competitors in the vicinity of the business partner company. The summary acquisition unit can prioritize the acquisition of legal and regulatory information related to the location of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of legal and industry regulatory information related to the location of the business partner company. As a result, the summary acquisition unit can acquire highly relevant information based on the geographical location information of the business partner company. Geographic location information includes, for example, GPS data and geographic information systems. Highly relevant information includes, for example, geographically relevant data and region-specific information. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or not. For example, when the summary acquisition unit acquires highly relevant information while considering the geographical location information of the business partner company, it can use AI to optimize information acquisition.

[0044] The summary acquisition unit can improve the accuracy of its acquisitions by referring to relevant literature of the company being considered for business. For example, the summary acquisition unit can improve the accuracy of its acquisitions by referring to relevant literature of the company being considered for business. The summary acquisition unit can obtain accurate summary information by referring to past performance reports of the company being considered for business. For example, the summary acquisition unit can obtain accurate summary information by referring to past performance reports of the company being considered for business. The summary acquisition unit can obtain technical summary information by referring to relevant research papers of the company being considered for business. For example, the summary acquisition unit can obtain technical summary information by referring to relevant research papers of the company being considered for business. The summary acquisition unit can grasp overall industry trends by referring to industry reports of the company being considered for business. For example, the summary acquisition unit grasps overall industry trends by referring to industry reports of the company being considered for business. This allows the summary acquisition unit to improve the accuracy of its acquisitions by referring to relevant literature of the company being considered for business. Relevant literature includes, for example, academic papers and industry reports. Accuracy of acquisition includes, for example, data accuracy and the accuracy of the acquisition algorithm. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or without AI. For example, the summary acquisition unit can use AI to optimize information acquisition when improving the accuracy of acquisition by referring to relevant literature of the business partner company.

[0045] The notification unit can adjust the level of detail in notifications based on the importance of the risk of losing a deal. For example, the notification unit adjusts the level of detail in notifications based on the importance of the risk of losing a deal. If the risk of losing a deal is high, the notification unit can provide detailed risk information. For example, if the risk of losing a deal is high, the notification unit will provide detailed risk information. If the risk of losing a deal is moderate, the notification unit can provide basic risk information. For example, if the risk of losing a deal is moderate, the notification unit will provide basic risk information. If the risk of losing a deal is low, the notification unit can provide concise risk information. For example, if the risk of losing a deal is low, the notification unit will provide concise risk information. This allows the notification unit to provide appropriate notifications according to the importance of the risk of losing a deal. The importance of the risk of losing a deal includes, for example, past deal data and the activities of competitors. The level of detail in notifications includes, for example, methods for adjusting the level of detail in notifications based on importance. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to optimize notification content when adjusting the level of detail in notifications based on the importance of the risk of losing a deal.

[0046] The notification unit can apply different notification algorithms depending on the category of the risk mitigation proposal. For example, the notification unit can apply different notification algorithms depending on the category of the risk mitigation proposal. If the risk mitigation proposal is technical, the notification unit can provide notifications that include technical details. For example, if the risk mitigation proposal is technical, the notification unit can provide notifications that include technical details. If the risk mitigation proposal is business-related, the notification unit can provide notifications that include business strategies. For example, if the risk mitigation proposal is business-related, the notification unit can provide notifications that include business strategies. If the risk mitigation proposal is legal, the notification unit can provide notifications that include legal details. For example, if the risk mitigation proposal is legal, the notification unit can provide notifications that include legal details. This allows the notification unit to provide appropriate notifications depending on the category of the risk mitigation proposal. Categories of risk mitigation proposals include, for example, technical measures and business measures. Notification algorithms include, for example, machine learning algorithms and data mining algorithms. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to optimize notification content when applying different notification algorithms depending on the category of the risk mitigation plan.

[0047] The notification department can prioritize notifications based on the progress of a deal. For example, the notification department can prioritize notifications based on the progress of a deal. In the early stages of a deal, the notification department can prioritize notifying basic information. For example, in the early stages of a deal, the notification department prioritizes notifying basic information such as the customer's basic information and the objectives of the deal. In the middle stages of a deal, the notification department can prioritize notifying detailed information. For example, in the middle stages of a deal, the notification department prioritizes notifying detailed information such as the customer's needs and competitor information. In the final stages of a deal, the notification department can prioritize notifying information such as the contents of the contract and price negotiations. For example, in the final stages of a deal, the notification department prioritizes notifying information such as the contents of the contract and price negotiations. This allows the notification department to provide appropriate notifications according to the progress of the deal. The progress of a deal includes, for example, the phase of the deal and progress reports. The priority of notifications includes, for example, methods for prioritizing based on progress. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to optimize notification content when determining notification priorities based on the progress of a business deal.

[0048] The notification unit can improve the accuracy of notifications by referring to relevant market data for the deal. For example, the notification unit can improve the accuracy of notifications by referring to relevant market data for the deal. The notification unit can provide accurate notifications based on relevant market data for the deal. For example, the notification unit can provide accurate notifications based on relevant market data for the deal. The notification unit can provide notifications that include competitive information by referring to relevant market data for the deal. For example, the notification unit can provide notifications that include competitive information by referring to relevant market data for the deal. The notification unit can analyze relevant market data for the deal and provide notifications that include risk factors. For example, the notification unit analyzes relevant market data for the deal and provides notifications that include risk factors. This allows the notification unit to improve the accuracy of notifications by referring to relevant market data for the deal. Relevant market data includes, for example, market reports and industry analysis data. Notification accuracy includes, for example, data accuracy and the accuracy of the notification algorithm. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to optimize information acquisition when improving the accuracy of notifications by referring to relevant market data for business negotiations.

[0049] The generation unit can analyze the past history of a sales opportunity and generate the optimal solution. For example, the generation unit can analyze the past history of a sales opportunity and generate the optimal solution. The generation unit can generate the optimal solution based on successful solutions from the past sales opportunity history. For example, the generation unit can analyze the past sales opportunity history and generate the optimal solution based on successful solutions. The generation unit can analyze failed solutions from the past sales opportunity history and generate solutions that reflect improvements. For example, the generation unit can analyze the past sales opportunity history and generate solutions that reflect improvements based on failed solutions. The generation unit can analyze the past sales opportunity history and generate solutions that can be applied to similar sales opportunities. For example, the generation unit can analyze the past sales opportunity history and generate solutions that can be applied to similar sales opportunities. In this way, the generation unit can generate the optimal solution based on the past history of a sales opportunity. Past history includes, for example, past sales opportunity data and history databases. Optimal solutions include, for example, solutions based on past history and optimization algorithms. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, when the generation unit analyzes the past history of business negotiations to generate the optimal countermeasures, it can use AI to optimize information acquisition.

[0050] The generation unit can customize proposed solutions based on the current status of the business deal. For example, the generation unit customizes proposed solutions based on the current status of the business deal. The generation unit can customize the optimal proposed solutions based on the progress of the business deal. For example, the generation unit customizes the optimal proposed solutions based on the progress of the business deal. The generation unit can adjust the level of detail of the proposed solutions based on the importance of the business deal. For example, the generation unit adjusts the level of detail of the proposed solutions based on the importance of the business deal. The generation unit can customize proposed solutions based on the current risk factors of the business deal. For example, the generation unit customizes proposed solutions based on the current risk factors of the business deal. This allows the generation unit to customize proposed solutions according to the current status of the business deal. Current status includes, for example, the progress of the business deal and current market trends. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, when the generation unit customizes proposed solutions based on the current status of the business deal, it may use AI to optimize the acquisition of information.

[0051] The generation unit can generate optimal countermeasures by considering the geographical location information of the business negotiation. The generation unit can generate optimal countermeasures by considering the geographical location information of the business negotiation. The generation unit can generate region-specific risk countermeasures based on the location of the business negotiation company. For example, the generation unit can generate region-specific risk countermeasures based on the location of the business negotiation company. The generation unit can generate countermeasures by considering information on competitors in the vicinity of the business negotiation company. For example, the generation unit can generate countermeasures by considering information on competitors in the vicinity of the business negotiation company. The generation unit can generate countermeasures by considering legal and regulatory information related to the location of the business negotiation company. For example, the generation unit can generate countermeasures by considering legal and regulatory information related to the location of the business negotiation company. As a result, the generation unit can generate optimal countermeasures based on the geographical location information of the business negotiation. Geographic location information includes, for example, GPS data and geographic information systems. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can use AI to optimize information acquisition when generating optimal countermeasures while considering the geographical location information of business negotiations.

[0052] The generation unit can improve the accuracy of proposed solutions by referring to relevant literature on the business deal. For example, the generation unit can improve the accuracy of proposed solutions by referring to relevant literature on the business deal. The generation unit can generate accurate proposed solutions by referring to past performance reports of the company being dealt with. For example, the generation unit can generate accurate proposed solutions by referring to past performance reports of the company being dealt with. The generation unit can generate technical proposed solutions by referring to relevant research papers of the company being dealt with. For example, the generation unit can generate technical proposed solutions by referring to relevant research papers of the company being dealt with. The generation unit can generate proposed solutions that reflect overall industry trends by referring to industry reports of the company being dealt with. For example, the generation unit can generate proposed solutions that reflect overall industry trends by referring to industry reports of the company being dealt with. In this way, the generation unit can improve the accuracy of proposed solutions by referring to relevant literature on the business deal. Relevant literature includes, for example, academic papers and industry reports. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can use AI to optimize information acquisition when improving the accuracy of proposed solutions by referring to relevant literature on business negotiations.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] In addition to acquiring business opportunity information, the acquisition unit can also analyze the financial status of potential business partners. For example, it can acquire financial reports and stock price data from potential business partners to assess their financial health. This allows for a proactive understanding of potential business partner financial risks and the identification of factors that could affect the progress of a business deal. Furthermore, the acquisition unit can adjust the priority of business deals based on the financial status of potential business partners. For example, prioritizing deals with companies in good financial condition can increase the success rate of closing deals.

[0055] In addition to acquiring business negotiation information, the acquisition unit can also evaluate employee satisfaction at prospective business partners. For example, the acquisition unit collects employee review sites and survey results from prospective business partners and analyzes employee satisfaction. This allows the unit to understand the internal environment of prospective business partners and evaluate risk factors for the negotiation. Furthermore, the acquisition unit can adjust the negotiation process based on employee satisfaction. For example, in negotiations with companies where employee satisfaction is low, the risk of losing the deal can be reduced by strengthening risk countermeasures.

[0056] The Summary Acquisition Department can also acquire information on the corporate social responsibility (CSR) activities of prospective clients. For example, it can collect information on prospective clients' CSR reports and environmental protection activities to evaluate their commitment to corporate social responsibility. This allows for an understanding of the prospective client's social standing and the identification of factors that may influence the progress of the negotiation. Furthermore, the Summary Acquisition Department can adjust the negotiation process based on the client's CSR activities. For instance, in negotiations with companies that are actively engaged in CSR activities, strengthening proposals regarding corporate social responsibility can increase the chances of a successful negotiation.

[0057] The notification system can customize notification content based on the culture and values ​​of the target company. For example, the notification system can collect information on the target company's corporate culture and values ​​and adjust the notification content accordingly. This allows for more appropriate communication with the target company. Furthermore, the notification system can also adjust the timing of notifications based on corporate culture. For example, sending notifications at a time that aligns with the values ​​that the target company prioritizes can increase the success rate of the deal.

[0058] The generation unit can generate risk mitigation plans based on the brand image of the client company. For example, the generation unit collects information on the client company's brand image and customizes risk mitigation plans based on that information. This allows it to propose risk mitigation measures that are suitable for the client company's brand strategy. Furthermore, the generation unit can also determine the priority of risk mitigation plans based on brand image. For example, for companies where brand image is important, prioritizing risk mitigation plans that focus on brand protection can increase the success rate of the deal.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The retrieval unit retrieves opportunity information. The retrieval unit retrieves opportunity information from, for example, an opportunity management tool. Opportunity management tools include CRM systems and sales support tools. The retrieval unit can retrieve opportunity information using the API of the opportunity management tool. For example, the retrieval unit retrieves opportunity information by calling the API of the opportunity management tool. The retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit retrieves opportunity information by reading a CSV file exported from the opportunity management tool. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit retrieves opportunity information by executing an SQL query on the database of the opportunity management tool. Step 2: The summary acquisition unit obtains summary information about the target company and its business environment. The summary acquisition unit obtains summary information about the target company and its business environment, for example, via the Perplexity API. The Perplexity API provides information such as the target company's business environment and the trends of its competitors. The summary acquisition unit can call the Perplexity API to obtain summary information about the target company and its business environment. For example, the summary acquisition unit provides the name of the target company as input to the Perplexity API and obtains summary information about the target company's business environment and the trends of its competitors. The summary acquisition unit can also obtain information about the business environment from the target company's website or publicly available reports. For example, the summary acquisition unit crawls the target company's website to collect information about the business environment. Furthermore, the summary acquisition unit can obtain information about the business environment from the target company's industry reports and market research reports. For example, the summary acquisition unit analyzes industry reports to obtain information about the target company's business environment. Step 3: The notification unit notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures. The notification unit notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures, for example, via a communication tool API. The notification unit can also notify the sales representative of the risk of losing the deal and proposed risk mitigation measures using email. For example, the notification unit sends the sales representative of the risk of losing the deal and proposed risk mitigation measures via an email server. Furthermore, the notification unit can also notify the sales representative of the risk of losing the deal and proposed risk mitigation measures using a mobile app. For example, the notification unit notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures via a mobile app.

[0061] (Example of form 2) The sales opportunity information acquisition and provision system according to an embodiment of the present invention is a system that acquires and provides sales opportunity information using an AI agent. This system has the function of acquiring sales opportunity information from a sales opportunity management tool, acquiring summary information on the prospective client company and business environment, and notifying the risk of lost deals and proposed risk countermeasures. For example, if the sales opportunity management tool is Chatter, sales opportunity information is acquired via the data loader. Next, summary information on the prospective client company and business environment is acquired via the Perplexity API. This makes it possible to understand the situation of the prospective client company and its business environment. Furthermore, the sales representative is notified of the risk of lost deals and proposed risk countermeasures via the communication tool API. This mechanism makes it possible to detect the risk of lost deals early and take appropriate measures. First, sales opportunity information is acquired from a sales opportunity management tool. The sales opportunity management tool can be any service that has API functionality. For example, in the case of Chatter, sales opportunity information is acquired via the data loader. This information includes the progress of the deal and customer information. Next, summary information on the prospective client company and business environment is acquired via the Perplexity API. The Perplexity API provides information on the business environment of potential clients and the activities of competitors. Based on this information, it is possible to understand the situation of potential clients and assess the risk of losing a deal. Furthermore, it notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures via the communication tool API. This allows the sales representative to quickly understand the risk of losing the deal and take appropriate measures. This mechanism allows for early detection of the risk of losing a deal and the implementation of appropriate countermeasures, thereby increasing the closing rate. For example, by identifying risk factors such as a change in management or deterioration of performance at a potential client company early and taking appropriate measures, it is possible to avoid losing a deal. It also allows for quick responses to changes in the business environment, such as the activities of competitors and legal revisions and regulations. As a result, the deal information acquisition and provision system can efficiently acquire deal information, acquire summary information, and notify customers of the risk of losing a deal and proposed risk mitigation measures.

[0062] The business opportunity information acquisition and provision system according to the embodiment comprises an acquisition unit, a summary acquisition unit, and a notification unit. The acquisition unit acquires business opportunity information. The acquisition unit acquires business opportunity information from, for example, a business opportunity management tool. The business opportunity management tool includes, for example, a CRM system or a sales support tool. The acquisition unit can acquire business opportunity information using the API of the business opportunity management tool. For example, the acquisition unit acquires business opportunity information by calling the API of the business opportunity management tool. The acquisition unit can also acquire business opportunity information by importing data exported from the business opportunity management tool. For example, the acquisition unit acquires business opportunity information by reading a CSV file exported from the business opportunity management tool. Furthermore, the acquisition unit can also acquire business opportunity information by directly accessing the database of the business opportunity management tool. For example, the acquisition unit acquires business opportunity information by executing an SQL query on the database of the business opportunity management tool. The summary acquisition unit acquires summary information of the target company and business environment. The summary acquisition unit acquires summary information of the target company and business environment via, for example, the Perplexity API. The Perplexity API provides information such as the business environment and competitor activities of potential clients. The summary retrieval unit can call the Perplexity API to obtain summary information about potential clients and their business environment. For example, the summary retrieval unit can provide the name of a potential client as input to the Perplexity API and retrieve summary information about the company's business environment and competitor activities. The summary retrieval unit can also obtain information about the business environment from the potential client's website or publicly available reports. For example, the summary retrieval unit can crawl the potential client's website to collect information about the business environment. Furthermore, the summary retrieval unit can obtain information about the business environment from the potential client's industry reports and market research reports. For example, the summary retrieval unit can analyze industry reports to obtain information about the potential client's business environment. The notification unit notifies clients of the risk of losing a deal and proposed risk mitigation measures. The notification unit notifies the sales representative of the risk of losing a deal and proposed risk mitigation measures, for example, via a communication tool API. The notification unit can also notify clients of the risk of losing a deal and proposed risk mitigation measures via email.For example, the notification unit sends information about the risk of losing a deal and proposed risk mitigation measures to the sales representative via the mail server. Furthermore, the notification unit can also use a mobile app to notify the sales representative of the risk of losing a deal and proposed risk mitigation measures. For example, the notification unit notifies the sales representative of the risk of losing a deal and proposed risk mitigation measures via the mobile app. This allows the deal information acquisition and provision system to efficiently acquire deal information, acquire summary information, and notify the sales representative of the risk of losing a deal and proposed risk mitigation measures.

[0063] The acquisition unit acquires sales opportunity information. For example, the acquisition unit acquires sales opportunity information from a sales opportunity management tool. Sales opportunity management tools include, for example, CRM systems and sales support tools. The acquisition unit can acquire sales opportunity information using the sales opportunity management tool's API. Specifically, the acquisition unit acquires sales opportunity information by calling the sales opportunity management tool's API. For example, the acquisition unit acquires sales opportunity information by calling the sales opportunity management tool's API. Furthermore, the acquisition unit can acquire sales opportunity information by importing data exported from the sales opportunity management tool. For example, the acquisition unit acquires sales opportunity information by reading a CSV file exported from the sales opportunity management tool. In addition, the acquisition unit can acquire sales opportunity information by directly accessing the sales opportunity management tool's database. For example, the acquisition unit acquires sales opportunity information by executing an SQL query on the sales opportunity management tool's database. This allows the acquisition unit to acquire sales opportunity information using the sales opportunity management tool's API. For example, the acquisition unit acquires sales opportunity information by calling the sales opportunity management tool's API. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to obtain opportunity information. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can obtain opportunity information by reading a CSV file exported from the opportunity management tool. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool.For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can execute an SQL query on the database of the opportunity management tool to retrieve opportunity information. This allows the retrieval unit to retrieve opportunity information using the API of the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can execute an SQL query on the database of the opportunity management tool to retrieve opportunity information. This allows the retrieval unit to obtain opportunity information using the opportunity management tool's API. For example, the retrieval unit can retrieve opportunity information by calling the opportunity management tool's API. The retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool and retrieve opportunity information. Furthermore, the retrieval unit can also obtain opportunity information by directly accessing the opportunity management tool's database. For example, the retrieval unit can execute an SQL query on the opportunity management tool's database to retrieve opportunity information. This allows the retrieval unit to obtain opportunity information using the opportunity management tool's API. For example, the retrieval unit can retrieve opportunity information by calling the opportunity management tool's API. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool and retrieve opportunity information.Furthermore, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. The retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool and obtain opportunity information. Furthermore, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. The retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the sales opportunity management tool to obtain sales opportunity information. Furthermore, the retrieval unit can also directly access the sales opportunity management tool's database to obtain sales opportunity information. For example, the retrieval unit can execute an SQL query on the sales opportunity management tool's database to obtain sales opportunity information. This allows the retrieval unit to obtain sales opportunity information using the sales opportunity management tool's API. For example, the retrieval unit can call the sales opportunity management tool's API to obtain sales opportunity information. Additionally, the retrieval unit can import data exported from the sales opportunity management tool to obtain sales opportunity information. For example, the retrieval unit can read a CSV file exported from the sales opportunity management tool to obtain sales opportunity information. Furthermore, the retrieval unit can also directly access the sales opportunity management tool's database to obtain sales opportunity information. For example, the retrieval unit can execute an SQL query on the sales opportunity management tool's database to obtain sales opportunity information. This allows the retrieval unit to obtain sales opportunity information using the sales opportunity management tool's API. For example, the retrieval unit can call the sales opportunity management tool's API to obtain sales opportunity information.Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to obtain opportunity information. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by calling the API of the opportunity management tool. Furthermore, the retrieval unit can also obtain opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can obtain opportunity information by reading a CSV file exported from the opportunity management tool. In addition, the retrieval unit can also obtain opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can obtain opportunity information by executing an SQL query on the database of the opportunity management tool. This allows the retrieval unit to obtain opportunity information using the API of the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit can execute an SQL query on the database of the opportunity management tool to retrieve opportunity information. Thus, the retrieval unit can retrieve opportunity information using the API of the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by calling the API of the opportunity management tool. Alternatively, the retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can read a CSV file exported from the opportunity management tool to retrieve opportunity information. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool.For example, the retrieval unit can retrieve opportunity information by executing an SQL query into the opportunity management tool's database. This allows the retrieval unit to retrieve opportunity information using the opportunity management tool's API. For example, the retrieval unit can retrieve opportunity information by calling the opportunity management tool's API. The retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit can retrieve opportunity information by reading a CSV file exported from the opportunity management tool. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the opportunity management tool's database. For example, the retrieval unit can retrieve opportunity information by executing an SQL query into the opportunity management tool's database. This allows the retrieval unit to retrieve opportunity information using the opportunity management tool's API.

[0064] The summary acquisition unit obtains summary information about potential business partners and their business environment. For example, the summary acquisition unit obtains this information via the Perplexity API. The Perplexity API provides information such as the business environment of potential business partners and the activities of their competitors. The summary acquisition unit can call the Perplexity API to obtain summary information about potential business partners and their business environment. For example, the summary acquisition unit provides the name of a potential business partner as input to the Perplexity API and obtains summary information about the business environment and the activities of its competitors. The summary acquisition unit can also obtain information about the business environment from the potential business partner's website or publicly available reports. For example, the summary acquisition unit crawls the potential business partner's website to collect information about the business environment. Furthermore, the summary acquisition unit can obtain information about the business environment from the potential business partner's industry reports and market research reports. For example, the summary acquisition unit analyzes industry reports to obtain information about the potential business partner's business environment. This allows the summary acquisition unit to efficiently obtain summary information on potential business partners and their business environment. Furthermore, the summary acquisition unit can analyze the acquired information using AI to identify the strengths and weaknesses, opportunities, and threats of potential business partners. For example, the summary acquisition unit uses AI to analyze the financial situation, market share, and competitor activities of potential business partners to identify their strengths and weaknesses. It also uses AI to analyze market trends and industry trends to identify opportunities and threats for potential business partners. As a result, the summary acquisition unit can efficiently acquire summary information on potential business partners and their business environment, providing information to formulate strategies for successful business negotiations.

[0065] The notification unit notifies users of the risk of lost sales and proposed risk mitigation measures. For example, the notification unit notifies the sales representative of the risk of lost sales and proposed risk mitigation measures via a communication tool API. The notification unit can also notify users of the risk of lost sales and proposed risk mitigation measures via email. For example, the notification unit sends the risk of lost sales and proposed risk mitigation measures to the sales representative via an email server. Furthermore, the notification unit can also notify users of the risk of lost sales and proposed risk mitigation measures using a mobile app. For example, the notification unit notifies the sales representative of the risk of lost sales and proposed risk mitigation measures via a mobile app. This allows the notification unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. In addition, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, based on feedback from users who received evacuation instructions, evacuation routes may be reviewed and instructions may be improved. The notification unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0066] The acquisition unit can acquire deal information from a deal management tool. For example, the acquisition unit can acquire deal information from a deal management tool. The acquisition unit can acquire deal information using the API of the deal management tool. For example, the acquisition unit can acquire deal information by calling the API of the deal management tool. The acquisition unit can also acquire deal information by importing data exported from the deal management tool. For example, the acquisition unit can acquire deal information by reading a CSV file exported from the deal management tool. Furthermore, the acquisition unit can acquire deal information by directly accessing the database of the deal management tool. For example, the acquisition unit can acquire deal information by executing an SQL query on the database of the deal management tool. This allows the acquisition unit to efficiently acquire deal information from the deal management tool. The deal management tool includes, for example, CRM systems and sales support tools. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to optimize the acquisition of deal information when calling the API of a deal management tool to obtain deal information.

[0067] The summary acquisition unit can obtain information such as the business environment and competitor trends of a potential business partner. For example, the summary acquisition unit can obtain summary information about the potential business partner and its business environment via the Perplexity API. The Perplexity API provides information such as the business environment and competitor trends of a potential business partner. The summary acquisition unit can call the Perplexity API to obtain summary information about the potential business partner and its business environment. For example, the summary acquisition unit provides the name of the potential business partner as input to the Perplexity API and obtains summary information regarding the business environment and competitor trends of the potential business partner. The summary acquisition unit can also obtain information about the business environment from the potential business partner's website or publicly available reports. For example, the summary acquisition unit crawls the potential business partner's website to collect information about the business environment. Furthermore, the summary acquisition unit can obtain information about the business environment from the potential business partner's industry reports and market research reports. For example, the summary acquisition unit analyzes industry reports to obtain information about the potential business partner's business environment. This allows the summary acquisition unit to understand the business environment of the prospective client company and the activities of its competitors. The business environment includes, for example, market trends, economic conditions, and industry trends. The activities of competitors include, for example, information on competitors' products and marketing strategies. Some or all of the above processing in the summary acquisition unit may be performed using AI, or not. For example, when the summary acquisition unit calls the Perplexity API to acquire summary information on the prospective client company and its business environment, it can use AI to optimize the information acquisition.

[0068] The notification unit can notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures. The notification unit can notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via a communication tool API, for example. The notification unit can also notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via email. For example, the notification unit can send the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via an email server. Furthermore, the notification unit can also notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via a mobile app. For example, the notification unit can notify the sales representative in charge of the risk of losing a deal and proposed risk mitigation measures via a mobile app. This allows the notification unit to enable the sales representative in charge to quickly grasp the risk of losing a deal and proposed risk mitigation measures. Sales representatives include, for example, sales representatives and account managers. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI.

[0069] The business negotiation information acquisition and provision system according to the embodiment includes a generation unit that generates risk mitigation proposals. The generation unit generates risk mitigation proposals. The generation unit generates risk mitigation proposals using, for example, AI. The generation unit can generate risk mitigation proposals by executing a risk mitigation proposal generation algorithm. For example, the generation unit analyzes past business negotiation data and generates risk mitigation proposals based on mitigation proposals for successful business negotiations. The generation unit can also analyze data from failed business negotiations and generate risk mitigation proposals that reflect areas for improvement. For example, the generation unit inputs past business negotiation data into AI, and the AI ​​analyzes the data to generate risk mitigation proposals. Furthermore, the generation unit can also customize risk mitigation proposals based on the current status of the business negotiation. For example, the generation unit generates risk mitigation proposals considering the progress of the business negotiation and current risk factors. This allows the generation unit to automatically generate risk mitigation proposals. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without using AI. For example, the generation unit can optimize the generation of risk mitigation proposals using AI.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring deal information based on the estimated user emotions. For example, the acquisition unit uses an emotion analysis algorithm to estimate the user's emotions. The acquisition unit can estimate the user's emotions by analyzing user behavior data. For example, the acquisition unit can estimate the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the acquisition unit can adjust the timing of acquiring deal information based on the user's emotions. For example, if the user is stressed, the acquisition unit will reduce the frequency of acquiring deal information and acquire only important information. If the user is relaxed, the acquisition unit can acquire detailed deal information frequently. In addition, if the user is in a hurry, the acquisition unit can quickly acquire the necessary deal information. In this way, the acquisition unit can adjust the timing of acquiring deal information 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 processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user behavior data into a generating AI, the generating AI can estimate the user's emotions, and adjust the timing of acquiring business opportunity information based on those emotions.

[0071] The acquisition unit can adjust the level of detail of the information it acquires according to the progress of the negotiation. For example, the acquisition unit adjusts the level of detail of the information it acquires based on the progress of the negotiation. In the early stages of the negotiation, the acquisition unit can acquire only basic information. For example, in the early stages of the negotiation, the acquisition unit acquires basic information such as the customer's basic information and the purpose of the negotiation. In the middle stages of the negotiation, the acquisition unit can acquire detailed customer information and competitor information. For example, in the middle stages of the negotiation, the acquisition unit acquires detailed information such as the customer's needs and information on competitors. In the final stages of the negotiation, the acquisition unit can acquire information on the contents of the contract and price negotiations. For example, in the final stages of the negotiation, the acquisition unit acquires information on the contents of the contract and price negotiations. This allows the acquisition unit to acquire appropriate information according to the progress of the negotiation. The progress of the negotiation includes, for example, the phase of the negotiation and progress reports. The level of detail of the information includes, for example, detailed data and summary information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the information acquisition unit can use AI to optimize information acquisition when adjusting the level of detail of the information acquired based on the progress of the business negotiation.

[0072] The information acquisition unit can determine the priority of information to acquire based on the importance of the business deal. For example, the information acquisition unit can prioritize the acquisition of detailed information for high-priority business deals. For example, the information acquisition unit prioritizes acquiring detailed customer information and competitor information for high-priority business deals. For low-priority business deals, the information acquisition unit can acquire only basic information. For example, the information acquisition unit acquires basic customer information and the purpose of the business deal for low-priority business deals. For medium-priority business deals, the information acquisition unit can acquire detailed information as needed. For example, the information acquisition unit acquires customer needs and competitor information as needed for medium-priority business deals. This allows the information acquisition unit to prioritize the acquisition of appropriate information according to the importance of the business deal. The importance of a business deal includes, for example, the size of the deal and the impact of the deal. The priority of information includes, for example, a prioritization method based on importance. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use AI to optimize information acquisition when determining the priority of information to acquire based on the importance of the business opportunity.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of the sales opportunity information to acquire based on the estimated user emotions. For example, the acquisition unit can use an emotion analysis algorithm to estimate the user's emotions. The acquisition unit can estimate the user's emotions by analyzing user behavior data. For example, the acquisition unit can estimate the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the acquisition unit can determine the priority of the sales opportunity information to acquire based on the user's emotions. For example, if the user is feeling stressed, the acquisition unit will prioritize acquiring only important information. If the user is relaxed, the acquisition unit can prioritize acquiring detailed information. In addition, if the user is in a hurry, the acquisition unit can prioritize acquiring information that is needed quickly. In this way, the acquisition unit can determine the priority of sales opportunity information 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 processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user behavior data into a generating AI, the generating AI can estimate the user's emotions, and based on those emotions, it can determine the priority of business opportunity information.

[0074] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the geographical location of the business negotiation. For example, the data acquisition unit prioritizes the acquisition of highly relevant information by considering the geographical location of the business negotiation. The data acquisition unit can prioritize the acquisition of region-specific information based on the location of the business negotiation company. For example, the data acquisition unit prioritizes the acquisition of region-specific information such as local market trends and competitor information based on the location of the business negotiation company. The data acquisition unit can prioritize the acquisition of competitor information in the vicinity of the business negotiation company. For example, the data acquisition unit prioritizes the acquisition of competitor information such as product information and marketing strategies of competitors in the vicinity of the business negotiation company. The data acquisition unit can prioritize the acquisition of legal and regulatory information related to the location of the business negotiation company. For example, the data acquisition unit prioritizes the acquisition of legal and regulatory information and industry regulations related to the location of the business negotiation company. This allows the data acquisition unit to acquire highly relevant information based on the geographical location of the business negotiation. Geographic location information includes, for example, GPS data and geographic information systems. Highly relevant information includes, for example, geographically relevant data and region-specific information. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use AI to optimize information acquisition when acquiring highly relevant information while considering the geographical location information of business negotiations.

[0075] The acquisition unit can analyze the past history of a business deal and acquire relevant information. For example, the acquisition unit can analyze the past history of a business deal and acquire relevant information. The acquisition unit can prioritize acquiring information on successful business deals from the past business deal history. For example, the acquisition unit can analyze the past business deal history and acquire relevant information based on the information of successful business deals. The acquisition unit can analyze information on failed business deals from the past business deal history and acquire areas for improvement. For example, the acquisition unit can analyze the past business deal history and acquire areas for improvement based on the information of failed business deals. The acquisition unit can acquire information on similar business deals from the past business deal history. For example, the acquisition unit can analyze the past business deal history and acquire relevant information based on the information of similar business deals. In this way, the acquisition unit can acquire relevant information based on the past history of a business deal. Past history includes, for example, past business deal data and history databases. Relevant information includes, for example, data related to past business deals and information based on history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the data acquisition unit can use AI to optimize information acquisition when analyzing the past history of business negotiations to obtain relevant information.

[0076] The summary acquisition unit can estimate the user's emotions and adjust the method of acquiring summary information based on the estimated user emotions. For example, the summary acquisition unit uses an emotion analysis algorithm to estimate the user's emotions. The summary acquisition unit can also estimate the user's emotions by analyzing user behavior data. For example, the summary acquisition unit can estimate the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the summary acquisition unit can adjust the method of acquiring summary information based on the user's emotions. For example, if the user is stressed, the summary acquisition unit can acquire concise summary information. If the user is relaxed, the summary acquisition unit can acquire detailed summary information. Additionally, if the user is in a hurry, the summary acquisition unit can quickly acquire the necessary summary information. Thus, the summary acquisition unit can adjust the method of acquiring summary information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or without AI. For example, the summary acquisition unit can input user behavior data into a generating AI, the generating AI can estimate the user's emotions, and adjust the method of acquiring summary information based on those emotions.

[0077] The summary acquisition unit can adjust the level of detail of the information it acquires based on the performance and market trends of the client company. For example, if the client company's performance is good, the summary acquisition unit can acquire detailed market trend information. For example, if the client company's performance is good, the summary acquisition unit can acquire detailed market trend information. If the client company's performance is poor, the summary acquisition unit can acquire information on risk factors. For example, if the client company's performance is poor, the summary acquisition unit can acquire information on risk factors. If the client company's performance is stable, the summary acquisition unit can acquire information on the activities of competitors. For example, if the client company's performance is stable, the summary acquisition unit can acquire information on the activities of competitors. This allows the summary acquisition unit to acquire appropriate information according to the client company's performance and market trends. Performance includes, for example, sales data and financial reports. Market trends include, for example, market reports and industry analysis data. The level of detail of the information includes, for example, detailed data and summary information. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or not using AI. For example, the summary acquisition unit can use AI to optimize information acquisition when adjusting the level of detail of the information acquired based on the performance and market trends of the business partner company.

[0078] The summary acquisition unit can apply different acquisition algorithms depending on the category of the client company. For example, if the client company is a manufacturer, the summary acquisition unit can acquire information on product lineup and production capacity. For example, if the client company is a service company, the summary acquisition unit can acquire information on customer satisfaction and service quality. For example, if the client company is an IT company, the summary acquisition unit can acquire information on technology trends and competing technologies. This allows the summary acquisition unit to acquire appropriate information depending on the category of the client company. Categories include, for example, industry categories and product categories. Acquisition algorithms include, for example, machine learning algorithms and data mining algorithms. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or without AI. For example, the summary acquisition unit can use AI to optimize information acquisition when applying different acquisition algorithms depending on the category of the business partner company.

[0079] The summary acquisition unit can estimate the user's emotions and determine the priority of summary information to acquire based on the estimated user emotions. For example, the summary acquisition unit uses an emotion analysis algorithm to estimate the user's emotions. The summary acquisition unit can also estimate the user's emotions by analyzing user behavior data. For example, the summary acquisition unit estimates the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the summary acquisition unit can determine the priority of summary information to acquire based on the user's emotions. For example, if the user is stressed, the summary acquisition unit prioritizes acquiring important summary information. If the user is relaxed, the summary acquisition unit can prioritize acquiring detailed summary information. Additionally, if the user is in a hurry, the summary acquisition unit can prioritize acquiring quickly needed summary information. Thus, the summary acquisition unit can determine the priority of summary information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or without AI. For example, the summary acquisition unit can input user behavior data into a generating AI, the generating AI can estimate the user's emotions, and based on those emotions, determine the priority of summary information to acquire.

[0080] The summary acquisition unit can prioritize the acquisition of highly relevant information by considering the geographical location information of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of highly relevant information by considering the geographical location information of the business partner company. The summary acquisition unit can prioritize the acquisition of region-specific market trend information based on the location of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of region-specific information such as regional market trends and competitor information based on the location of the business partner company. The summary acquisition unit can prioritize the acquisition of competitor information in the vicinity of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of competitor information such as product information and marketing strategies of competitors in the vicinity of the business partner company. The summary acquisition unit can prioritize the acquisition of legal and regulatory information related to the location of the business partner company. For example, the summary acquisition unit prioritizes the acquisition of legal and industry regulatory information related to the location of the business partner company. As a result, the summary acquisition unit can acquire highly relevant information based on the geographical location information of the business partner company. Geographic location information includes, for example, GPS data and geographic information systems. Highly relevant information includes, for example, geographically relevant data and region-specific information. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or not. For example, when the summary acquisition unit acquires highly relevant information while considering the geographical location information of the business partner company, it can use AI to optimize information acquisition.

[0081] The summary acquisition unit can improve the accuracy of its acquisitions by referring to relevant literature of the company being considered for business. For example, the summary acquisition unit can improve the accuracy of its acquisitions by referring to relevant literature of the company being considered for business. The summary acquisition unit can obtain accurate summary information by referring to past performance reports of the company being considered for business. For example, the summary acquisition unit can obtain accurate summary information by referring to past performance reports of the company being considered for business. The summary acquisition unit can obtain technical summary information by referring to relevant research papers of the company being considered for business. For example, the summary acquisition unit can obtain technical summary information by referring to relevant research papers of the company being considered for business. The summary acquisition unit can grasp overall industry trends by referring to industry reports of the company being considered for business. For example, the summary acquisition unit grasps overall industry trends by referring to industry reports of the company being considered for business. This allows the summary acquisition unit to improve the accuracy of its acquisitions by referring to relevant literature of the company being considered for business. Relevant literature includes, for example, academic papers and industry reports. Accuracy of acquisition includes, for example, data accuracy and the accuracy of the acquisition algorithm. Some or all of the processing described above in the summary acquisition unit may be performed using AI, for example, or without AI. For example, the summary acquisition unit can use AI to optimize information acquisition when improving the accuracy of acquisition by referring to relevant literature of the business partner company.

[0082] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, the notification unit may use an emotion analysis algorithm to estimate the user's emotions. The notification unit can also estimate the user's emotions by analyzing user behavior data. For example, the notification unit may estimate the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the notification unit can adjust the way notifications are presented based on the user's emotions. For example, if the user is stressed, the notification unit may provide a concise and clear notification. If the user is relaxed, the notification unit may provide a detailed notification. In addition, if the user is in a hurry, the notification unit may quickly provide the necessary information. In this way, the notification unit can adjust the way notifications are presented 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-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user behavior data into a generating AI, which can then estimate the user's emotions and adjust the way notifications are expressed based on those emotions.

[0083] The notification unit can adjust the level of detail in notifications based on the importance of the risk of losing a deal. For example, the notification unit adjusts the level of detail in notifications based on the importance of the risk of losing a deal. If the risk of losing a deal is high, the notification unit can provide detailed risk information. For example, if the risk of losing a deal is high, the notification unit will provide detailed risk information. If the risk of losing a deal is moderate, the notification unit can provide basic risk information. For example, if the risk of losing a deal is moderate, the notification unit will provide basic risk information. If the risk of losing a deal is low, the notification unit can provide concise risk information. For example, if the risk of losing a deal is low, the notification unit will provide concise risk information. This allows the notification unit to provide appropriate notifications according to the importance of the risk of losing a deal. The importance of the risk of losing a deal includes, for example, past deal data and the activities of competitors. The level of detail in notifications includes, for example, methods for adjusting the level of detail in notifications based on importance. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to optimize notification content when adjusting the level of detail in notifications based on the importance of the risk of losing a deal.

[0084] The notification unit can apply different notification algorithms depending on the category of the risk mitigation proposal. For example, the notification unit can apply different notification algorithms depending on the category of the risk mitigation proposal. If the risk mitigation proposal is technical, the notification unit can provide notifications that include technical details. For example, if the risk mitigation proposal is technical, the notification unit can provide notifications that include technical details. If the risk mitigation proposal is business-related, the notification unit can provide notifications that include business strategies. For example, if the risk mitigation proposal is business-related, the notification unit can provide notifications that include business strategies. If the risk mitigation proposal is legal, the notification unit can provide notifications that include legal details. For example, if the risk mitigation proposal is legal, the notification unit can provide notifications that include legal details. This allows the notification unit to provide appropriate notifications depending on the category of the risk mitigation proposal. Categories of risk mitigation proposals include, for example, technical measures and business measures. Notification algorithms include, for example, machine learning algorithms and data mining algorithms. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to optimize notification content when applying different notification algorithms depending on the category of the risk mitigation plan.

[0085] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, the notification unit may use an emotion analysis algorithm to estimate the user's emotions. The notification unit can also estimate the user's emotions by analyzing user behavior data. For example, the notification unit may estimate the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the notification unit can adjust the timing of notifications based on the user's emotions. For example, if the user is stressed, the notification unit will notify only important information at the appropriate time. If the user is relaxed, the notification unit may notify detailed information frequently. In addition, if the user is in a hurry, the notification unit may notify necessary information quickly. In this way, the notification unit can adjust the timing of notifications 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user behavior data into a generating AI, which can then estimate the user's emotions and adjust the timing of notifications based on those emotions.

[0086] The notification department can prioritize notifications based on the progress of a deal. For example, the notification department can prioritize notifications based on the progress of a deal. In the early stages of a deal, the notification department can prioritize notifying basic information. For example, in the early stages of a deal, the notification department prioritizes notifying basic information such as the customer's basic information and the objectives of the deal. In the middle stages of a deal, the notification department can prioritize notifying detailed information. For example, in the middle stages of a deal, the notification department prioritizes notifying detailed information such as the customer's needs and competitor information. In the final stages of a deal, the notification department can prioritize notifying information such as the contents of the contract and price negotiations. For example, in the final stages of a deal, the notification department prioritizes notifying information such as the contents of the contract and price negotiations. This allows the notification department to provide appropriate notifications according to the progress of the deal. The progress of a deal includes, for example, the phase of the deal and progress reports. The priority of notifications includes, for example, methods for prioritizing based on progress. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to optimize notification content when determining notification priorities based on the progress of a business deal.

[0087] The notification unit can improve the accuracy of notifications by referring to relevant market data for the deal. For example, the notification unit can improve the accuracy of notifications by referring to relevant market data for the deal. The notification unit can provide accurate notifications based on relevant market data for the deal. For example, the notification unit can provide accurate notifications based on relevant market data for the deal. The notification unit can provide notifications that include competitive information by referring to relevant market data for the deal. For example, the notification unit can provide notifications that include competitive information by referring to relevant market data for the deal. The notification unit can analyze relevant market data for the deal and provide notifications that include risk factors. For example, the notification unit analyzes relevant market data for the deal and provides notifications that include risk factors. This allows the notification unit to improve the accuracy of notifications by referring to relevant market data for the deal. Relevant market data includes, for example, market reports and industry analysis data. Notification accuracy includes, for example, data accuracy and the accuracy of the notification algorithm. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to optimize information acquisition when improving the accuracy of notifications by referring to relevant market data for business negotiations.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating risk mitigation plans based on the estimated user emotions. For example, the generation unit uses an emotion analysis algorithm to estimate the user's emotions. The generation unit can estimate the user's emotions by analyzing user behavior data. For example, the generation unit can estimate the user's emotions by analyzing behavior data such as the user's input speed and mouse movements. Furthermore, the generation unit can adjust the method of generating risk mitigation plans based on the user's emotions. For example, if the user is stressed, the generation unit can generate a concise and clear risk mitigation plan. If the user is relaxed, the generation unit can generate a detailed risk mitigation plan. Moreover, if the user is in a hurry, the generation unit can quickly generate the necessary risk mitigation plans. In this way, the generation unit can adjust the method of generating risk mitigation plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user behavior data into a generation AI, which can then estimate the user's emotions and adjust the method for generating risk mitigation plans based on those emotions.

[0089] The generation unit can analyze the past history of a sales opportunity and generate the optimal solution. For example, the generation unit can analyze the past history of a sales opportunity and generate the optimal solution. The generation unit can generate the optimal solution based on successful solutions from the past sales opportunity history. For example, the generation unit can analyze the past sales opportunity history and generate the optimal solution based on successful solutions. The generation unit can analyze failed solutions from the past sales opportunity history and generate solutions that reflect improvements. For example, the generation unit can analyze the past sales opportunity history and generate solutions that reflect improvements based on failed solutions. The generation unit can analyze the past sales opportunity history and generate solutions that can be applied to similar sales opportunities. For example, the generation unit can analyze the past sales opportunity history and generate solutions that can be applied to similar sales opportunities. In this way, the generation unit can generate the optimal solution based on the past history of a sales opportunity. Past history includes, for example, past sales opportunity data and history databases. Optimal solutions include, for example, solutions based on past history and optimization algorithms. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, when the generation unit analyzes the past history of business negotiations to generate the optimal countermeasures, it can use AI to optimize information acquisition.

[0090] The generation unit can customize proposed solutions based on the current status of the business deal. For example, the generation unit customizes proposed solutions based on the current status of the business deal. The generation unit can customize the optimal proposed solutions based on the progress of the business deal. For example, the generation unit customizes the optimal proposed solutions based on the progress of the business deal. The generation unit can adjust the level of detail of the proposed solutions based on the importance of the business deal. For example, the generation unit adjusts the level of detail of the proposed solutions based on the importance of the business deal. The generation unit can customize proposed solutions based on the current risk factors of the business deal. For example, the generation unit customizes proposed solutions based on the current risk factors of the business deal. This allows the generation unit to customize proposed solutions according to the current status of the business deal. Current status includes, for example, the progress of the business deal and current market trends. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, when the generation unit customizes proposed solutions based on the current status of the business deal, it may use AI to optimize the acquisition of information.

[0091] The generation unit can estimate the user's emotions and prioritize risk mitigation measures based on those estimated emotions. For example, the generation unit might use an emotion analysis algorithm to estimate the user's emotions. The generation unit can also estimate user emotions by analyzing user behavior data. For example, it might analyze behavioral data such as user input speed and mouse movements to estimate user emotions. Furthermore, the generation unit can prioritize risk mitigation measures based on the user's emotions. For example, if the user is stressed, the generation unit will prioritize generating important measures. If the user is relaxed, the generation unit can prioritize generating detailed measures. Additionally, if the user is in a hurry, the generation unit can prioritize generating quickly needed measures. This allows the generation unit to prioritize risk mitigation measures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user behavior data into a generation AI, which can estimate the user's emotions and determine the priority of risk mitigation measures based on those emotions.

[0092] The generation unit can generate optimal countermeasures by considering the geographical location information of the business negotiation. The generation unit can generate optimal countermeasures by considering the geographical location information of the business negotiation. The generation unit can generate region-specific risk countermeasures based on the location of the business negotiation company. For example, the generation unit can generate region-specific risk countermeasures based on the location of the business negotiation company. The generation unit can generate countermeasures by considering information on competitors in the vicinity of the business negotiation company. For example, the generation unit can generate countermeasures by considering information on competitors in the vicinity of the business negotiation company. The generation unit can generate countermeasures by considering legal and regulatory information related to the location of the business negotiation company. For example, the generation unit can generate countermeasures by considering legal and regulatory information related to the location of the business negotiation company. As a result, the generation unit can generate optimal countermeasures based on the geographical location information of the business negotiation. Geographic location information includes, for example, GPS data and geographic information systems. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can use AI to optimize information acquisition when generating optimal countermeasures while considering the geographical location information of business negotiations.

[0093] The generation unit can improve the accuracy of proposed solutions by referring to relevant literature on the business deal. For example, the generation unit can improve the accuracy of proposed solutions by referring to relevant literature on the business deal. The generation unit can generate accurate proposed solutions by referring to past performance reports of the company being dealt with. For example, the generation unit can generate accurate proposed solutions by referring to past performance reports of the company being dealt with. The generation unit can generate technical proposed solutions by referring to relevant research papers of the company being dealt with. For example, the generation unit can generate technical proposed solutions by referring to relevant research papers of the company being dealt with. The generation unit can generate proposed solutions that reflect overall industry trends by referring to industry reports of the company being dealt with. For example, the generation unit can generate proposed solutions that reflect overall industry trends by referring to industry reports of the company being dealt with. In this way, the generation unit can improve the accuracy of proposed solutions by referring to relevant literature on the business deal. Relevant literature includes, for example, academic papers and industry reports. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can use AI to optimize information acquisition when improving the accuracy of proposed solutions by referring to relevant literature on business negotiations.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] In addition to acquiring business opportunity information, the acquisition unit can also analyze the financial status of potential business partners. For example, it can acquire financial reports and stock price data from potential business partners to assess their financial health. This allows for a proactive understanding of potential business partner financial risks and the identification of factors that could affect the progress of a business deal. Furthermore, the acquisition unit can adjust the priority of business deals based on the financial status of potential business partners. For example, prioritizing deals with companies in good financial condition can increase the success rate of closing deals.

[0096] In addition to acquiring business negotiation information, the acquisition unit can also evaluate employee satisfaction at prospective business partners. For example, the acquisition unit collects employee review sites and survey results from prospective business partners and analyzes employee satisfaction. This allows the unit to understand the internal environment of prospective business partners and evaluate risk factors for the negotiation. Furthermore, the acquisition unit can adjust the negotiation process based on employee satisfaction. For example, in negotiations with companies where employee satisfaction is low, the risk of losing the deal can be reduced by strengthening risk countermeasures.

[0097] The Summary Acquisition Department can also acquire information on the corporate social responsibility (CSR) activities of prospective clients. For example, it can collect information on prospective clients' CSR reports and environmental protection activities to evaluate their commitment to corporate social responsibility. This allows for an understanding of the prospective client's social standing and the identification of factors that may influence the progress of the negotiation. Furthermore, the Summary Acquisition Department can adjust the negotiation process based on the client's CSR activities. For instance, in negotiations with companies that are actively engaged in CSR activities, strengthening proposals regarding corporate social responsibility can increase the chances of a successful negotiation.

[0098] The notification system can customize notification content based on the culture and values ​​of the target company. For example, the notification system can collect information on the target company's corporate culture and values ​​and adjust the notification content accordingly. This allows for more appropriate communication with the target company. Furthermore, the notification system can also adjust the timing of notifications based on corporate culture. For example, sending notifications at a time that aligns with the values ​​that the target company prioritizes can increase the success rate of the deal.

[0099] The generation unit can generate risk mitigation plans based on the brand image of the client company. For example, the generation unit collects information on the client company's brand image and customizes risk mitigation plans based on that information. This allows it to propose risk mitigation measures that are suitable for the client company's brand strategy. Furthermore, the generation unit can also determine the priority of risk mitigation plans based on brand image. For example, for companies where brand image is important, prioritizing risk mitigation plans that focus on brand protection can increase the success rate of the deal.

[0100] The information retrieval unit can estimate the user's emotions and adjust the method of acquiring sales opportunity information based on the estimated emotions. For example, if the user is stressed, the unit will prioritize acquiring concise information, and if the user is relaxed, it will acquire detailed information. This allows the system to provide appropriate information according to the user's emotions. Furthermore, if the user is in a hurry, the unit can quickly acquire the necessary information. This allows the system to optimize the method of acquiring sales opportunity information according to the user's emotions.

[0101] The summary acquisition unit can estimate the user's emotions and adjust the timing of summary information acquisition based on the estimated emotions. For example, if the user is stressed, the summary acquisition unit reduces the frequency of summary information acquisition and acquires only important information. If the user is relaxed, it can acquire detailed summary information frequently. Furthermore, if the user is in a hurry, the summary acquisition unit can quickly acquire the necessary summary information. In this way, the summary acquisition unit can adjust the timing of summary information acquisition according to the user's emotions.

[0102] The notification unit can estimate the user's emotions and adjust the content of notifications based on those emotions. For example, if the user is stressed, the notification unit will provide a concise and clear notification; if the user is relaxed, it will provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can quickly provide the necessary information. In this way, the notification unit can optimize the content of notifications according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI.

[0103] The generation unit can estimate the user's emotions and adjust the timing of risk mitigation proposal generation based on the estimated emotions. For example, if the user is stressed, the generation unit can quickly generate risk mitigation proposals, and if the user is relaxed, it can generate detailed risk mitigation proposals. Furthermore, if the user is in a hurry, the generation unit can generate concise and clear risk mitigation proposals. In this way, the generation unit can adjust the timing of risk mitigation proposal generation according to the user's emotions.

[0104] The generation unit can estimate the user's emotions and adjust the level of detail in the risk mitigation proposals based on those emotions. For example, if the user is stressed, the generation unit will generate concise and clear risk mitigation proposals; if the user is relaxed, it will generate detailed risk mitigation proposals. Furthermore, if the user is in a hurry, the generation unit can quickly generate the necessary risk mitigation proposals. In this way, the generation unit can adjust the level of detail in the risk mitigation proposals according to the user's emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The retrieval unit retrieves opportunity information. The retrieval unit retrieves opportunity information from, for example, an opportunity management tool. Opportunity management tools include CRM systems and sales support tools. The retrieval unit can retrieve opportunity information using the API of the opportunity management tool. For example, the retrieval unit retrieves opportunity information by calling the API of the opportunity management tool. The retrieval unit can also retrieve opportunity information by importing data exported from the opportunity management tool. For example, the retrieval unit retrieves opportunity information by reading a CSV file exported from the opportunity management tool. Furthermore, the retrieval unit can also retrieve opportunity information by directly accessing the database of the opportunity management tool. For example, the retrieval unit retrieves opportunity information by executing an SQL query on the database of the opportunity management tool. Step 2: The summary acquisition unit obtains summary information about the target company and its business environment. The summary acquisition unit obtains summary information about the target company and its business environment, for example, via the Perplexity API. The Perplexity API provides information such as the target company's business environment and the trends of its competitors. The summary acquisition unit can call the Perplexity API to obtain summary information about the target company and its business environment. For example, the summary acquisition unit provides the name of the target company as input to the Perplexity API and obtains summary information about the target company's business environment and the trends of its competitors. The summary acquisition unit can also obtain information about the business environment from the target company's website or publicly available reports. For example, the summary acquisition unit crawls the target company's website to collect information about the business environment. Furthermore, the summary acquisition unit can obtain information about the business environment from the target company's industry reports and market research reports. For example, the summary acquisition unit analyzes industry reports to obtain information about the target company's business environment. Step 3: The notification unit notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures. The notification unit notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures, for example, via a communication tool API. The notification unit can also notify the sales representative of the risk of losing the deal and proposed risk mitigation measures using email. For example, the notification unit sends the sales representative of the risk of losing the deal and proposed risk mitigation measures via an email server. Furthermore, the notification unit can also notify the sales representative of the risk of losing the deal and proposed risk mitigation measures using a mobile app. For example, the notification unit notifies the sales representative of the risk of losing the deal and proposed risk mitigation measures via a mobile app.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the acquisition unit, summary acquisition unit, notification unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The summary acquisition unit is implemented by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the acquisition unit, summary acquisition unit, notification unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The summary acquisition unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The 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.

[0131] 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.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 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.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the 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.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 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.

[0142] Each of the multiple elements described above, including the acquisition unit, summary acquisition unit, notification unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The summary acquisition unit is implemented by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the acquisition unit, summary acquisition unit, notification unit, and generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The summary acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) The acquisition unit acquires business negotiation information, A summary acquisition unit acquires summary information on the business negotiation company and business environment based on the negotiation information acquired by the aforementioned acquisition unit, A notification unit that notifies the customer of the risk of losing a deal and proposed risk mitigation measures based on the summary information obtained by the summary acquisition unit, Equipped with A system characterized by the following features. (Note 2) The acquisition unit is, Retrieve sales opportunity information from the sales opportunity management tool. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary acquisition unit, Obtain information such as the business environment of the company being negotiated with and the trends of its competitors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Notify the sales representative of the risk of losing the deal and the proposed risk mitigation measures. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a generation unit that generates risk mitigation plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring sales opportunity information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Adjust the level of detail of the information obtained according to the progress of the business negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Prioritize the information to be acquired based on the importance of the business deal. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of opportunity information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, Prioritize retrieving highly relevant information, taking into account the geographical location of the business negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, Analyze past sales negotiation history to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary acquisition unit, It estimates the user's emotions and adjusts how summary information is retrieved based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary acquisition unit, Adjust the level of detail of the information obtained based on the performance and market trends of the companies being negotiated with. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary acquisition unit, Apply different acquisition algorithms depending on the category of the target company. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary acquisition unit, It estimates the user's emotions and determines the priority of summary information to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary acquisition unit, Prioritize obtaining highly relevant information by considering the geographical location of the company being negotiated with. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary acquisition unit, Improve the accuracy of information obtained by referring to relevant literature from the company being negotiated with. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, Adjust the level of detail in notifications based on the severity of the risk of losing the deal. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, Apply different notification algorithms depending on the category of the risk mitigation plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, Prioritize notifications based on the progress of the sales negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, We improve the accuracy of notifications by referring to relevant market data for business opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is We estimate user sentiment and adjust the method for generating risk mitigation plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is Analyze past sales negotiation history to generate optimal solutions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is Customize the proposed solutions based on the current status of the business negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is The system estimates user sentiment and prioritizes risk mitigation measures based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is Generate optimal countermeasures considering the geographical location of business negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is Referencing relevant literature on business negotiations improves the accuracy of proposed solutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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 business negotiation information, A summary acquisition unit acquires summary information on the business negotiation company and business environment based on the negotiation information acquired by the aforementioned acquisition unit, A notification unit that notifies the customer of the risk of losing a deal and proposed risk mitigation measures based on the summary information obtained by the summary acquisition unit, Equipped with A system characterized by the following features.

2. The acquisition unit is, Retrieve sales opportunity information from the sales opportunity management tool. The system according to feature 1.

3. The summary acquisition unit, Obtain information such as the business environment of the company being negotiated with and the trends of its competitors. The system according to feature 1.

4. The aforementioned notification unit, Notify the sales representative of the risk of losing the deal and the proposed risk mitigation measures. The system according to feature 1.

5. It includes a generation unit that generates risk mitigation plans. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring sales opportunity information based on those estimated emotions. The system according to feature 1.

7. The acquisition unit is, Adjust the level of detail of the information obtained according to the progress of the business negotiation. The system according to feature 1.

8. The acquisition unit is, Prioritize the information to be acquired based on the importance of the business deal. The system according to feature 1.

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

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