system

Abstract

The system according to this embodiment aims to efficiently collect information on business negotiations and automatically generate appropriate proposals. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, an output unit, and a simulation unit. The collection unit collects information on business negotiations. The generation unit automatically generates proposals based on the information collected by the collection unit. The output unit outputs the proposals generated by the generation unit. The simulation unit provides a business negotiation simulation.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently collect negotiation information and automatically generate appropriate proposals, and there is room for improvement in improving the success rate of negotiations.

[0005] The system according to the embodiment aims to efficiently collect negotiation information and automatically generate appropriate proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, an output unit, and a simulation unit. The collection unit collects information on business negotiations. The generation unit automatically generates proposals based on the information collected by the collection unit. The output unit outputs the proposals generated by the generation unit. The simulation unit provides a business negotiation simulation. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect information on business negotiations and automatically generate appropriate proposals. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI-powered sales negotiation checklist and dialogue training system according to an embodiment of the present invention is a system in which AI analyzes sales negotiation information and automatically generates necessary proposals. This system collects sales negotiation information, and the AI ​​analyzes that information to automatically generate proposals. In addition, the AI, having learned potential customer responses, provides sales negotiation simulations to improve the responsiveness of sales members. This compensates for overlooked sales negotiations, increases the success rate, and contributes to achieving departmental budget targets. For example, there is a collection unit that collects sales negotiation information. This collection unit collects information on the content of sales negotiations and customer information. For example, sales negotiation minutes and customer profile information are collected. Next, there is a generation unit that automatically generates proposals based on the collected information. This generation unit analyzes the collected information and makes proposals based on customer needs and past sales negotiation history. For example, it automatically generates new proposals based on products and services that the customer has shown interest in in the past. The generated proposals are output by an output unit. The output unit provides the generated proposals to sales members. For example, they are output as proposal documents or presentation materials. Furthermore, there is a simulation unit that provides sales negotiation simulations. This simulation unit provides sales negotiation simulations using AI that has learned from customer reactions. For example, it predicts how customers will react and simulates how to respond. This system can compensate for overlooked opportunities in sales negotiations and increase the success rate. Sales members can make more accurate proposals by proceeding with negotiations based on proposals generated by the AI. In addition, sales negotiation simulations can improve their ability to respond to customer reactions. This is expected to contribute to achieving departmental budget targets. As a result, the AI ​​sales negotiation checklist and dialogue training system can efficiently collect, analyze, generate proposals for, and simulate sales negotiations.

[0029] The AI-powered business negotiation checklist and dialogue training system according to this embodiment comprises a collection unit, a generation unit, an output unit, and a simulation unit. The collection unit collects business negotiation information. Business negotiation information includes, but is not limited to, the content of the negotiation, customer information, and past negotiation history. The collection unit collects, for example, minutes of the negotiation. The collection unit can also collect customer profile information. For example, it collects the customer's name, contact information, and past transaction history. Furthermore, the collection unit can also collect information such as the purpose and agenda of the negotiation and the participants. The generation unit analyzes the information collected by the collection unit and automatically generates proposals. Proposals are made, for example, based on customer needs and past negotiation history, but is not limited to such examples. The generation unit automatically generates new proposals based on, for example, products and services that the customer has shown interest in in the past. The generation unit can also make proposals based on customer requests, expectations, and problems. For example, the generation unit analyzes customer needs and generates proposals accordingly. The output unit outputs the proposals generated by the generation unit. The output may be, for example, a proposal or presentation materials, but is not limited to such examples. The output unit may, for example, provide a proposal to a sales member. The output unit may also create and provide presentation materials to a sales member. For example, the output unit may output a proposal in PDF format and send it to a sales member. The simulation unit provides a sales negotiation simulation using AI that has learned customer reactions. The simulation may, for example, predict how a customer will react and simulate how to respond, but is not limited to such examples. The simulation unit may, for example, predict a customer's reaction and simulate how to respond. The simulation unit may also improve the responsiveness of sales members based on customer reactions. For example, the simulation unit learns customer reactions and simulates how to respond. As a result, the AI ​​sales negotiation checklist & dialogue training system according to this embodiment can efficiently collect, analyze, generate proposals for, and simulate sales negotiation information.

[0030] The data collection unit collects information about business negotiations. This information includes, but is not limited to, the content of the negotiation, customer information, and past negotiation history. For example, the data collection unit collects minutes of business negotiations. The data collection unit can also collect customer profile information, such as the customer's name, contact information, and past transaction history. Furthermore, the data collection unit can collect information such as the purpose and agenda of the negotiation and the participants. The data collection unit has the function to automatically analyze the minutes of the negotiation and extract important points and keywords. For example, it can use speech recognition technology to convert the audio data of the negotiation into text and extract important information from that text data. In addition, customer profile information is automatically obtained from CRM systems and customer databases, and the latest information is always reflected. Furthermore, information on the purpose and agenda of the negotiation and participants is automatically collected from calendar apps and email systems and used as basic data to understand the overall picture of the negotiation. As a result, the data collection unit can efficiently collect diverse information related to business negotiations and provide it to the analysis and generation units.

[0031] The generation unit analyzes the information collected by the collection unit and automatically generates suggestions. These suggestions are based on, for example, customer needs and past sales history, but are not limited to these examples. For instance, the generation unit automatically generates new suggestions based on products and services that the customer has shown interest in in the past. The generation unit can also make suggestions based on customer demands, expectations, and problems. For example, the generation unit analyzes customer needs and generates suggestions accordingly. The generation unit uses natural language processing technology to analyze collected text data and identify customer needs and interests. For example, it analyzes past sales history and customer feedback to understand what products and services customers are interested in. Furthermore, the generation unit uses machine learning algorithms to analyze customer behavior patterns and purchase history and generate optimal suggestions. For example, it suggests relevant new products and services based on products and services the customer has purchased in the past. In addition, the generation unit analyzes customer demands, expectations, and problems and generates solutions and suggestions accordingly. For example, if a customer has a specific problem, it provides specific suggestions to solve that problem. This allows the generation unit to automatically generate high-quality proposals tailored to customer needs, thereby improving the proposal capabilities of sales team members.

[0032] The output unit outputs the proposals generated by the generation unit. The output may, but is not limited to, proposal documents or presentation materials. For example, the output unit can provide proposal documents to sales members. It can also create and provide presentation materials to sales members. For instance, the output unit can output proposal documents in PDF format and send them to sales members. The output unit has a function to automatically insert graphs, charts, images, etc., to make the generated proposals visually easy to understand. For example, the proposal may include graphs and charts showing sales forecasts and market analysis results, providing visually appealing materials to customers. Furthermore, the output unit can output proposal documents and presentation materials in multiple formats. For example, it can output not only in PDF format, but also in PowerPoint and Word formats, allowing for flexible responses to the needs of sales members. In addition, the output unit has a function to save the generated proposals to cloud storage, allowing sales members to access them at any time. This enables the output unit to efficiently output generated proposals and support sales activities.

[0033] The simulation department provides sales negotiation simulations using AI that has learned from customer responses. The simulations predict, for example, how customers will react and simulate how to respond, but are not limited to these examples. The simulation department can also improve the responsiveness of sales members based on customer responses. For example, the simulation department learns from customer responses and simulates how to respond. The simulation department uses generative AI to automatically generate sales negotiation scenarios, allowing sales members to train in an environment close to actual sales negotiations. For example, the AI ​​learns from past sales negotiation data, predicts what questions and objections customers will ask, and generates optimal answers. Furthermore, the simulation department uses speech recognition technology to analyze sales members' statements in real time and provide appropriate feedback. For example, if a sales member provides an appropriate answer to a customer's question, the AI ​​evaluates the answer and points out areas for improvement. In addition, the simulation department prepares multiple scenarios to train sales members to handle various situations. For example, the simulations include scenarios where customers negotiate prices or where they respond to proposals from competitors. This allows the simulation department to improve the responsiveness of sales members and increase the success rate in actual business negotiations.

[0034] The data collection unit can collect information on the content of business negotiations and customer information. For example, the data collection unit collects information such as the purpose of the business negotiation, agenda, and participants. For example, the data collection unit clarifies the purpose of the business negotiation, sets the agenda, and collects information on the participants. The data collection unit can also collect information such as the customer's name, contact information, and past transaction history. For example, the data collection unit collects the customer's name and contact information and checks past transaction history. Furthermore, the data collection unit can collect minutes of business negotiations and record the content of the negotiations. For example, the data collection unit creates minutes of business negotiations and records the content of the negotiations in detail. This allows for the efficient collection of information on the content of business negotiations and customer information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the minutes of business negotiations into AI, which can automatically analyze the content of the negotiations and extract the necessary information.

[0035] The generation unit can analyze the information collected by the collection unit and make proposals based on customer needs and past sales history. For example, the generation unit can analyze customer requests, expectations, and problems and generate proposals accordingly. For example, the generation unit can analyze customer requests and make proposals based on them. The generation unit can also analyze the customer's past sales history and make proposals based on it. For example, the generation unit can automatically generate new proposals based on products and services that the customer has shown interest in in the past. Furthermore, the generation unit can analyze customer needs and make proposals accordingly. For example, the generation unit can analyze customer needs and make proposals based on them. This enables the automatic generation of proposals based on customer needs and past sales history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the information collected by the collection unit into AI, which can automatically analyze the information and generate proposals.

[0036] The simulation unit can provide sales negotiation simulations using AI that has learned from customer reactions. For example, the simulation unit can predict how a customer will react and simulate how to respond. For example, the simulation unit can predict customer reactions and simulate how to respond. The simulation unit can also improve the responsiveness of sales members based on customer reactions. For example, the simulation unit learns from customer reactions and simulates how to respond. Furthermore, the simulation unit can analyze customer reactions in real time and provide sales negotiation simulations. For example, the simulation unit analyzes customer reactions in real time and simulates how to respond. This allows the simulation unit to predict customer reactions and provide sales negotiation simulations. Some or all of the above processes in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input customer reaction data into AI, which can automatically analyze the reactions and perform simulations.

[0037] The data collection unit can analyze past sales history and select appropriate information collection methods. For example, the data collection unit can identify information collection methods for successful sales from past sales history and apply similar methods. The data collection unit can also analyze past sales history and avoid information collection methods for unsuccessful sales. For example, the data collection unit can analyze past sales history, identify information collection methods for unsuccessful sales, and avoid them. Furthermore, the data collection unit can select information collection methods that received a good response from customers based on past sales history. For example, the data collection unit can analyze past sales history, identify information collection methods that received a good response from customers, and select them. This allows for the selection of the optimal information collection method based on past sales history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sales history data into AI, which can automatically analyze the data and select the optimal information collection method.

[0038] The data collection unit can filter the collected business opportunity information based on the customer's current business situation and areas of interest. For example, the data collection unit can analyze the customer's current business situation and collect only the relevant information. For example, the data collection unit can analyze the customer's current business situation, identify relevant information, and collect it. The data collection unit can also eliminate unnecessary information and collect only the necessary information based on the customer's areas of interest. For example, the data collection unit can identify the customer's areas of interest and filter the information based on them. Furthermore, the data collection unit can combine the customer's business situation and areas of interest to filter the information to the most suitable. For example, the data collection unit can combine the customer's business situation and areas of interest to identify the information to the most suitable and collect it. This allows the data collection unit to filter the information to the necessary information based on the customer's business situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input customer business situation data and areas of interest data into AI, which can then automatically analyze the data and filter the information to the necessary information.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location when collecting business negotiation information. For example, the data collection unit can prioritize the collection of region-specific information based on the customer's geographical location. For example, the data collection unit can identify and collect region-specific information based on the customer's geographical location. The data collection unit can also collect information on nearby competitors by considering the customer's geographical location. For example, the data collection unit can identify and collect information on nearby competitors based on the customer's geographical location. Furthermore, the data collection unit can collect information on regional market trends based on the customer's geographical location. For example, the data collection unit can identify and collect information on regional market trends based on the customer's geographical location. This allows for the priority collection of highly relevant information based on the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer geographical location data into AI, which can automatically analyze the data, identify highly relevant information, and collect it.

[0040] The data collection unit can analyze the customer's social media activity and collect relevant information when collecting business opportunity information. For example, the data collection unit can analyze the customer's social media activity and collect information related to topics of interest. For example, the data collection unit can analyze the customer's social media activity, identify topics of interest, and collect information related to them. The data collection unit can also collect business opportunity-related information based on the customer's social media posts. For example, the data collection unit can analyze the customer's social media posts and collect information related to business opportunity based on them. Furthermore, the data collection unit can also collect the latest information considering the customer's frequency of social media activity. For example, the data collection unit can analyze the customer's frequency of social media activity and collect the latest information based on it. This allows for the collection of relevant information based on the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input customer social media data into AI, which can automatically analyze the data, identify relevant information, and collect it.

[0041] The generation unit can adjust the level of detail of a proposal based on the customer's importance when generating proposals. For example, the generation unit can generate detailed proposals for important customers. The generation unit can also generate standard proposals for general customers. Furthermore, the generation unit can generate concise proposals for less important customers. This allows the level of detail of a proposal to be adjusted according to the customer's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer importance data into AI, which can automatically analyze the data and adjust the level of detail of the proposal.

[0042] The generation unit can apply different proposal algorithms depending on the customer's industry and business category when generating proposals. For example, if the customer is in the manufacturing industry, the generation unit can apply a proposal algorithm specifically tailored to the manufacturing industry. The generation unit can also apply a proposal algorithm specifically tailored to the service industry if the customer is in the service industry. Furthermore, if the customer is in the retail industry, the generation unit can apply a proposal algorithm specifically tailored to the retail industry. This allows the generation unit to generate proposals tailored to the customer's industry and business category. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input customer industry data and business category data into AI, which can automatically analyze the data and apply an appropriate proposal algorithm.

[0043] The generation unit can determine the priority of proposals based on the customer's past responses when generating proposals. For example, the generation unit can prioritize generating proposals that the customer has responded to favorably in the past. The generation unit can also avoid proposals that the customer has responded to negatively in the past. Furthermore, the generation unit can analyze the customer's past responses and prioritize generating the most effective proposals. For example, the generation unit can analyze the customer's past responses, identify the most effective proposals, and prioritize generating them. This allows the generation unit to determine the priority of proposals based on the customer's past responses. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the customer's past response data into AI, which can automatically analyze the data and determine the priority of proposals.

[0044] The generation unit can adjust the order of proposals based on customer relevance when generating proposals. For example, the generation unit may first present the proposal that is most relevant to the customer's needs. The generation unit can also adjust the order of proposals based on the customer's areas of interest. For example, the generation unit may identify the customer's areas of interest and adjust the order of proposals accordingly. Furthermore, the generation unit may prioritize presenting the most relevant proposals according to the customer's business situation. For example, the generation unit may analyze the customer's business situation, identify the most relevant proposals based on that analysis, and prioritize presenting them. This allows the order of proposals to be adjusted based on customer relevance. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input customer relevance data into AI, which can automatically analyze the data and adjust the order of proposals.

[0045] The output unit can adjust the level of detail in the output based on the importance of the proposals. For example, the output unit can output important proposals in a format that includes detailed information. The output unit can also output general proposals in a format that includes standard information. Furthermore, the output unit can output less important proposals in a format that includes concise information. This allows the level of detail in the output to be adjusted according to the importance of the proposals. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input proposal importance data into AI, which can automatically analyze the data and adjust the level of detail in the output.

[0046] The output unit can apply different output formats depending on the customer's industry and business category when outputting data. For example, if the customer is in the manufacturing industry, the output unit can apply an output format specific to the manufacturing industry. The output unit can also apply an output format specific to the service industry if the customer is in the service industry. Furthermore, if the customer is in the retail industry, the output unit can apply an output format specific to the retail industry. This allows for the application of output formats tailored to the customer's industry and business category. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input customer industry data and business category data into AI, which can automatically analyze the data and apply the appropriate output format.

[0047] The output unit can select the optimal output format when outputting data, taking into account the customer's geographical location information. For example, the output unit can output data in a format that includes region-specific information based on the customer's geographical location information. For example, the output unit can identify region-specific information based on the customer's geographical location information and output data in a format that includes it. The output unit can also output data in a format that includes nearby competitor information, taking into account the customer's geographical location information. For example, the output unit can identify nearby competitor information based on the customer's geographical location information and output data in a format that includes it. Furthermore, the output unit can output data in a format that includes regional market trends based on the customer's geographical location information. For example, the output unit can identify regional market trends based on the customer's geographical location information and output data in a format that includes it. This allows the output unit to select the optimal output format based on the customer's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the customer's geographical location data into AI, which can automatically analyze the data and select the optimal output format.

[0048] The output unit can analyze the customer's social media activity and output relevant information at the time of output. For example, the output unit can analyze the customer's social media activity and output information related to topics of interest. For example, the output unit can analyze the customer's social media activity, identify topics of interest, and output information related to them. The output unit can also output information related to business opportunities based on the customer's social media posts. For example, the output unit can analyze the customer's social media posts and output information related to business opportunities based on that. Furthermore, the output unit can output the latest information considering the customer's frequency of social media activity. For example, the output unit can analyze the customer's frequency of social media activity and output the latest information based on that. This allows the output unit to output relevant information based on the customer's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input customer social media data into AI, which can automatically analyze the data, identify relevant information, and output it.

[0049] The simulation unit can improve the accuracy of the simulation by referring to past sales data during the simulation. For example, the simulation unit can recreate successful sales scenarios based on past sales data. The simulation unit can also analyze past sales data and avoid unsuccessful sales scenarios. For example, the simulation unit can analyze past sales data, identify unsuccessful sales scenarios, and avoid them. Furthermore, the simulation unit can refer to past sales data to predict customer responses and improve the accuracy of the simulation. For example, the simulation unit can refer to past sales data to predict customer responses and improve the accuracy of the simulation based on that. This allows the simulation to improve accuracy based on past sales data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past sales data into AI, which can automatically analyze the data and improve the accuracy of the simulation.

[0050] The simulation unit can apply different simulation methods depending on the customer's industry and business category during simulation. For example, if the customer is in the manufacturing industry, the simulation unit can apply a simulation method specialized for the manufacturing industry. Similarly, if the customer is in the service industry, the simulation unit can apply a simulation method specialized for the service industry. Furthermore, if the customer is in the retail industry, the simulation unit can apply a simulation method specialized for the retail industry. This allows for the application of simulation methods tailored to the customer's industry and business category. Some or all of the above-described processes in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input customer industry data and business category data into AI, which can automatically analyze the data and apply an appropriate simulation method.

[0051] The simulation unit can perform simulations while taking into account the customer's geographical location information. For example, the simulation unit can simulate region-specific scenarios based on the customer's geographical location information. The simulation unit can also perform simulations that include nearby competitor information, taking into account the customer's geographical location information. Furthermore, the simulation unit can perform simulations that reflect regional market trends based on the customer's geographical location information. This allows simulations to be performed based on the customer's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer geographical location data into AI, which can automatically analyze the data and perform simulations.

[0052] The simulation unit can improve the accuracy of the simulation by referring to relevant customer literature during the simulation. For example, the simulation unit can refer to relevant customer literature and reflect information related to the customer's business in the simulation. For example, the simulation unit can refer to relevant customer literature and reflect information related to the customer's business in the simulation. The simulation unit can also perform simulations tailored to the customer's needs based on relevant customer literature. For example, the simulation unit can perform simulations tailored to the customer's needs based on relevant customer literature. Furthermore, the simulation unit can refer to relevant customer literature to predict customer responses and improve the accuracy of the simulation. For example, the simulation unit can refer to relevant customer literature to predict customer responses and improve the accuracy of the simulation based on those predictions. This allows the simulation to improve the accuracy of the simulation based on relevant customer literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer literature data into AI, which can automatically analyze the data and improve the accuracy of the simulation.

[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] The data collection unit can analyze a customer's past purchase history when collecting sales opportunity information and prioritize the collection of relevant information. For example, based on a customer's past purchase history, the data collection unit can identify and collect information about products and services that the customer might be interested in. The data collection unit can also analyze a customer's purchase history and prioritize the collection of information related to products and services that the customer has purchased in the past. Furthermore, based on a customer's purchase history, the data collection unit can also collect information about products and services that the customer is likely to repurchase. This allows for the priority collection of relevant information based on a customer's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input customer purchase history data into AI, which can automatically analyze the data, identify relevant information, and collect it.

[0055] The generation unit can analyze past customer feedback and adjust the proposal content when generating proposals. For example, the generation unit can generate proposals that meet the customer's preferences and requests based on feedback provided by the customer in the past. The generation unit can also analyze customer feedback and generate proposals that improve on areas where the customer was dissatisfied. Furthermore, the generation unit can generate proposals that highlight points that the customer particularly valued based on customer feedback. This allows the proposal content to be adjusted based on past customer feedback. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer feedback data into AI, which can automatically analyze the data and adjust the proposal content.

[0056] The simulation unit can perform business negotiation simulations while taking the customer's cultural background into consideration. For example, the simulation unit can provide culturally appropriate scenarios based on the customer's cultural background. The simulation unit can also perform simulations to avoid cultural misunderstandings, taking the customer's cultural background into consideration. Furthermore, the simulation unit can provide simulations that take into account cultural customs and values, based on the customer's cultural background. This allows simulations to be performed while considering the customer's cultural background. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer cultural background data into AI, which can then automatically analyze the data and perform simulations.

[0057] The output unit can adjust the output content when outputting, taking into account the customer's past purchase history. For example, the output unit can highlight information about products and services that the customer might be interested in, based on the customer's past purchase history. The output unit can also prioritize outputting information related to products and services that the customer has previously purchased, taking into account the customer's purchase history. Furthermore, the output unit can output information about products and services that the customer is likely to repurchase, based on the customer's purchase history. This allows the output content to be adjusted based on the customer's past purchase history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input customer purchase history data into AI, which can then automatically analyze the data and adjust the output content.

[0058] The simulation unit can apply different simulation methods depending on the customer's industry and business category during simulation. For example, if the customer is in the manufacturing industry, the simulation unit will apply a simulation method specialized for manufacturing. Similarly, if the customer is in the service industry, the simulation unit can apply a simulation method specialized for the service industry. Furthermore, if the customer is in the retail industry, the simulation unit can apply a simulation method specialized for retail. This allows for the application of simulation methods tailored to the customer's industry and business category. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer industry data and business category data into AI, which can then automatically analyze the data and apply an appropriate simulation method.

[0059] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location when collecting business negotiation information. For example, the data collection unit can prioritize the collection of region-specific information based on the customer's geographical location. The data collection unit can also collect information on nearby competitors by considering the customer's geographical location. Furthermore, the data collection unit can collect regional market trends based on the customer's geographical location. This allows for the priority collection of highly relevant information based on the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer geographical location data into AI, which can automatically analyze the data, identify highly relevant information, and collect it.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection department gathers information about the business deal. This information includes, for example, the content of the deal, customer information, and past deal history. The collection department also gathers information such as meeting minutes, customer profile information (name, contact information, past transaction history, etc.), the purpose and agenda of the deal, and participants. Step 2: The generation unit analyzes the information collected by the collection unit and automatically generates proposals. Proposals are based on customer needs and past sales history. For example, new proposals are automatically generated based on products and services that the customer has shown interest in in the past. Proposals are also made based on customer requests, expectations, and problems. Step 3: The output unit outputs the proposal generated by the generation unit. The output is provided as a proposal document or presentation material. For example, the proposal document is output in PDF format and provided to the sales team. Step 4: The simulation department provides sales negotiation simulations using AI that has learned from customer reactions. The simulation predicts how customers will react and simulates how to respond. This improves the responsiveness of sales team members.

[0062] (Example of form 2) The AI-powered sales negotiation checklist and dialogue training system according to an embodiment of the present invention is a system in which AI analyzes sales negotiation information and automatically generates necessary proposals. This system collects sales negotiation information, and the AI ​​analyzes that information to automatically generate proposals. In addition, the AI, having learned potential customer responses, provides sales negotiation simulations to improve the responsiveness of sales members. This compensates for overlooked sales negotiations, increases the success rate, and contributes to achieving departmental budget targets. For example, there is a collection unit that collects sales negotiation information. This collection unit collects information on the content of sales negotiations and customer information. For example, sales negotiation minutes and customer profile information are collected. Next, there is a generation unit that automatically generates proposals based on the collected information. This generation unit analyzes the collected information and makes proposals based on customer needs and past sales negotiation history. For example, it automatically generates new proposals based on products and services that the customer has shown interest in in the past. The generated proposals are output by an output unit. The output unit provides the generated proposals to sales members. For example, they are output as proposal documents or presentation materials. Furthermore, there is a simulation unit that provides sales negotiation simulations. This simulation unit provides sales negotiation simulations using AI that has learned from customer reactions. For example, it predicts how customers will react and simulates how to respond. This system can compensate for overlooked opportunities in sales negotiations and increase the success rate. Sales members can make more accurate proposals by proceeding with negotiations based on proposals generated by the AI. In addition, sales negotiation simulations can improve their ability to respond to customer reactions. This is expected to contribute to achieving departmental budget targets. As a result, the AI ​​sales negotiation checklist and dialogue training system can efficiently collect, analyze, generate proposals for, and simulate sales negotiations.

[0063] The AI-powered business negotiation checklist and dialogue training system according to this embodiment comprises a collection unit, a generation unit, an output unit, and a simulation unit. The collection unit collects business negotiation information. Business negotiation information includes, but is not limited to, the content of the negotiation, customer information, and past negotiation history. The collection unit collects, for example, minutes of the negotiation. The collection unit can also collect customer profile information. For example, it collects the customer's name, contact information, and past transaction history. Furthermore, the collection unit can also collect information such as the purpose and agenda of the negotiation and the participants. The generation unit analyzes the information collected by the collection unit and automatically generates proposals. Proposals are made, for example, based on customer needs and past negotiation history, but is not limited to such examples. The generation unit automatically generates new proposals based on, for example, products and services that the customer has shown interest in in the past. The generation unit can also make proposals based on customer requests, expectations, and problems. For example, the generation unit analyzes customer needs and generates proposals accordingly. The output unit outputs the proposals generated by the generation unit. The output may be, for example, a proposal or presentation materials, but is not limited to such examples. The output unit may, for example, provide a proposal to a sales member. The output unit may also create and provide presentation materials to a sales member. For example, the output unit may output a proposal in PDF format and send it to a sales member. The simulation unit provides a sales negotiation simulation using AI that has learned customer reactions. The simulation may, for example, predict how a customer will react and simulate how to respond, but is not limited to such examples. The simulation unit may, for example, predict a customer's reaction and simulate how to respond. The simulation unit may also improve the responsiveness of sales members based on customer reactions. For example, the simulation unit learns customer reactions and simulates how to respond. As a result, the AI ​​sales negotiation checklist & dialogue training system according to this embodiment can efficiently collect, analyze, generate proposals for, and simulate sales negotiation information.

[0064] The data collection unit collects information about business negotiations. This information includes, but is not limited to, the content of the negotiation, customer information, and past negotiation history. For example, the data collection unit collects minutes of business negotiations. The data collection unit can also collect customer profile information, such as the customer's name, contact information, and past transaction history. Furthermore, the data collection unit can collect information such as the purpose and agenda of the negotiation and the participants. The data collection unit has the function to automatically analyze the minutes of the negotiation and extract important points and keywords. For example, it can use speech recognition technology to convert the audio data of the negotiation into text and extract important information from that text data. In addition, customer profile information is automatically obtained from CRM systems and customer databases, and the latest information is always reflected. Furthermore, information on the purpose and agenda of the negotiation and participants is automatically collected from calendar apps and email systems and used as basic data to understand the overall picture of the negotiation. As a result, the data collection unit can efficiently collect diverse information related to business negotiations and provide it to the analysis and generation units.

[0065] The generation unit analyzes the information collected by the collection unit and automatically generates suggestions. These suggestions are based on, for example, customer needs and past sales history, but are not limited to these examples. For instance, the generation unit automatically generates new suggestions based on products and services that the customer has shown interest in in the past. The generation unit can also make suggestions based on customer demands, expectations, and problems. For example, the generation unit analyzes customer needs and generates suggestions accordingly. The generation unit uses natural language processing technology to analyze collected text data and identify customer needs and interests. For example, it analyzes past sales history and customer feedback to understand what products and services customers are interested in. Furthermore, the generation unit uses machine learning algorithms to analyze customer behavior patterns and purchase history and generate optimal suggestions. For example, it suggests relevant new products and services based on products and services the customer has purchased in the past. In addition, the generation unit analyzes customer demands, expectations, and problems and generates solutions and suggestions accordingly. For example, if a customer has a specific problem, it provides specific suggestions to solve that problem. This allows the generation unit to automatically generate high-quality proposals tailored to customer needs, thereby improving the proposal capabilities of sales team members.

[0066] The output unit outputs the proposals generated by the generation unit. The output may, but is not limited to, proposal documents or presentation materials. For example, the output unit can provide proposal documents to sales members. It can also create and provide presentation materials to sales members. For instance, the output unit can output proposal documents in PDF format and send them to sales members. The output unit has a function to automatically insert graphs, charts, images, etc., to make the generated proposals visually easy to understand. For example, the proposal may include graphs and charts showing sales forecasts and market analysis results, providing visually appealing materials to customers. Furthermore, the output unit can output proposal documents and presentation materials in multiple formats. For example, it can output not only in PDF format, but also in PowerPoint and Word formats, allowing for flexible responses to the needs of sales members. In addition, the output unit has a function to save the generated proposals to cloud storage, allowing sales members to access them at any time. This enables the output unit to efficiently output generated proposals and support sales activities.

[0067] The simulation department provides sales negotiation simulations using AI that has learned from customer responses. The simulations predict, for example, how customers will react and simulate how to respond, but are not limited to these examples. The simulation department can also improve the responsiveness of sales members based on customer responses. For example, the simulation department learns from customer responses and simulates how to respond. The simulation department uses generative AI to automatically generate sales negotiation scenarios, allowing sales members to train in an environment close to actual sales negotiations. For example, the AI ​​learns from past sales negotiation data, predicts what questions and objections customers will ask, and generates optimal answers. Furthermore, the simulation department uses speech recognition technology to analyze sales members' statements in real time and provide appropriate feedback. For example, if a sales member provides an appropriate answer to a customer's question, the AI ​​evaluates the answer and points out areas for improvement. In addition, the simulation department prepares multiple scenarios to train sales members to handle various situations. For example, the simulations include scenarios where customers negotiate prices or where they respond to proposals from competitors. This allows the simulation department to improve the responsiveness of sales members and increase the success rate in actual business negotiations.

[0068] The data collection unit can collect information on the content of business negotiations and customer information. For example, the data collection unit collects information such as the purpose of the business negotiation, agenda, and participants. For example, the data collection unit clarifies the purpose of the business negotiation, sets the agenda, and collects information on the participants. The data collection unit can also collect information such as the customer's name, contact information, and past transaction history. For example, the data collection unit collects the customer's name and contact information and checks past transaction history. Furthermore, the data collection unit can collect minutes of business negotiations and record the content of the negotiations. For example, the data collection unit creates minutes of business negotiations and records the content of the negotiations in detail. This allows for the efficient collection of information on the content of business negotiations and customer information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the minutes of business negotiations into AI, which can automatically analyze the content of the negotiations and extract the necessary information.

[0069] The generation unit can analyze the information collected by the collection unit and make proposals based on customer needs and past sales history. For example, the generation unit can analyze customer requests, expectations, and problems and generate proposals accordingly. For example, the generation unit can analyze customer requests and make proposals based on them. The generation unit can also analyze the customer's past sales history and make proposals based on it. For example, the generation unit can automatically generate new proposals based on products and services that the customer has shown interest in in the past. Furthermore, the generation unit can analyze customer needs and make proposals accordingly. For example, the generation unit can analyze customer needs and make proposals based on them. This enables the automatic generation of proposals based on customer needs and past sales history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the information collected by the collection unit into AI, which can automatically analyze the information and generate proposals.

[0070] The simulation unit can provide sales negotiation simulations using AI that has learned from customer reactions. For example, the simulation unit can predict how a customer will react and simulate how to respond. For example, the simulation unit can predict customer reactions and simulate how to respond. The simulation unit can also improve the responsiveness of sales members based on customer reactions. For example, the simulation unit learns from customer reactions and simulates how to respond. Furthermore, the simulation unit can analyze customer reactions in real time and provide sales negotiation simulations. For example, the simulation unit analyzes customer reactions in real time and simulates how to respond. This allows the simulation unit to predict customer reactions and provide sales negotiation simulations. Some or all of the above processes in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input customer reaction data into AI, which can automatically analyze the reactions and perform simulations.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of collecting sales opportunity information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. The data collection unit can also collect sales opportunity information immediately if the user is relaxed, efficiently gathering data. Furthermore, if the user is in a hurry, the data collection unit can quickly collect sales opportunity information and obtain the necessary information in a short time. This allows the timing of collecting sales opportunity information to be adjusted 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI, which can automatically analyze the emotion and adjust the timing of data collection.

[0072] The data collection unit can analyze past sales history and select appropriate information collection methods. For example, the data collection unit can identify information collection methods for successful sales from past sales history and apply similar methods. The data collection unit can also analyze past sales history and avoid information collection methods for unsuccessful sales. For example, the data collection unit can analyze past sales history, identify information collection methods for unsuccessful sales, and avoid them. Furthermore, the data collection unit can select information collection methods that received a good response from customers based on past sales history. For example, the data collection unit can analyze past sales history, identify information collection methods that received a good response from customers, and select them. This allows for the selection of the optimal information collection method based on past sales history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sales history data into AI, which can automatically analyze the data and select the optimal information collection method.

[0073] The data collection unit can filter the collected business opportunity information based on the customer's current business situation and areas of interest. For example, the data collection unit can analyze the customer's current business situation and collect only the relevant information. For example, the data collection unit can analyze the customer's current business situation, identify relevant information, and collect it. The data collection unit can also eliminate unnecessary information and collect only the necessary information based on the customer's areas of interest. For example, the data collection unit can identify the customer's areas of interest and filter the information based on them. Furthermore, the data collection unit can combine the customer's business situation and areas of interest to filter the information to the most suitable. For example, the data collection unit can combine the customer's business situation and areas of interest to identify the information to the most suitable and collect it. This allows the data collection unit to filter the information to the necessary information based on the customer's business situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input customer business situation data and areas of interest data into AI, which can then automatically analyze the data and filter the information to the necessary information.

[0074] The data collection unit can estimate the user's emotions and determine the priority of the sales opportunity information to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information and prioritize collecting important information. For example, if the user is stressed, the data collection unit will postpone collecting less important information. The data collection unit can also collect all information equally if the user is relaxed. For example, if the user is relaxed, the data collection unit will collect all information equally. Furthermore, if the user is in a hurry, the data collection unit will prioritize collecting the most important information. For example, if the user is in a hurry, the data collection unit will prioritize collecting the most important information. This allows for the prioritization of sales opportunity information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI, which can then automatically analyze the emotions and determine the priority of the business opportunity information to collect.

[0075] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location when collecting business negotiation information. For example, the data collection unit can prioritize the collection of region-specific information based on the customer's geographical location. For example, the data collection unit can identify and collect region-specific information based on the customer's geographical location. The data collection unit can also collect information on nearby competitors by considering the customer's geographical location. For example, the data collection unit can identify and collect information on nearby competitors based on the customer's geographical location. Furthermore, the data collection unit can collect information on regional market trends based on the customer's geographical location. For example, the data collection unit can identify and collect information on regional market trends based on the customer's geographical location. This allows for the priority collection of highly relevant information based on the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer geographical location data into AI, which can automatically analyze the data, identify highly relevant information, and collect it.

[0076] The data collection unit can analyze the customer's social media activity and collect relevant information when collecting business opportunity information. For example, the data collection unit can analyze the customer's social media activity and collect information related to topics of interest. For example, the data collection unit can analyze the customer's social media activity, identify topics of interest, and collect information related to them. The data collection unit can also collect business opportunity-related information based on the customer's social media posts. For example, the data collection unit can analyze the customer's social media posts and collect information related to business opportunity based on them. Furthermore, the data collection unit can also collect the latest information considering the customer's frequency of social media activity. For example, the data collection unit can analyze the customer's frequency of social media activity and collect the latest information based on it. This allows for the collection of relevant information based on the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input customer social media data into AI, which can automatically analyze the data, identify relevant information, and collect it.

[0077] The generation unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand suggestions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand suggestions. The generation unit can also generate suggestions that include detailed information if the user is relaxed. For example, if the user is relaxed, the generation unit can generate suggestions that include detailed information. Furthermore, if the user is in a hurry, the generation unit can generate concise and to-the-point suggestions. For example, if the user is in a hurry, the generation unit can generate concise and to-the-point suggestions. This allows the presentation of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the AI, which can then automatically analyze the emotions and adjust the way suggestions are expressed.

[0078] The generation unit can adjust the level of detail of a proposal based on the customer's importance when generating proposals. For example, the generation unit can generate detailed proposals for important customers. The generation unit can also generate standard proposals for general customers. Furthermore, the generation unit can generate concise proposals for less important customers. This allows the level of detail of a proposal to be adjusted according to the customer's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer importance data into AI, which can automatically analyze the data and adjust the level of detail of the proposal.

[0079] The generation unit can apply different proposal algorithms depending on the customer's industry and business category when generating proposals. For example, if the customer is in the manufacturing industry, the generation unit can apply a proposal algorithm specifically tailored to the manufacturing industry. The generation unit can also apply a proposal algorithm specifically tailored to the service industry if the customer is in the service industry. Furthermore, if the customer is in the retail industry, the generation unit can apply a proposal algorithm specifically tailored to the retail industry. This allows the generation unit to generate proposals tailored to the customer's industry and business category. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input customer industry data and business category data into AI, which can automatically analyze the data and apply an appropriate proposal algorithm.

[0080] The generation unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the generation unit can generate short, concise suggestions. For example, if the user is stressed, the generation unit can generate short, concise suggestions. For example, if the user is relaxed, the generation unit can generate longer suggestions that include detailed explanations. For example, if the user is relaxed, the generation unit can generate longer suggestions that include detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate short suggestions that can be read quickly. For example, if the user is in a hurry, the generation unit can generate short suggestions that can be read quickly. This allows the length of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the AI, which can then automatically analyze the emotion and adjust the length of the suggestions.

[0081] The generation unit can determine the priority of proposals based on the customer's past responses when generating proposals. For example, the generation unit can prioritize generating proposals that the customer has responded to favorably in the past. The generation unit can also avoid proposals that the customer has responded to negatively in the past. Furthermore, the generation unit can analyze the customer's past responses and prioritize generating the most effective proposals. For example, the generation unit can analyze the customer's past responses, identify the most effective proposals, and prioritize generating them. This allows the generation unit to determine the priority of proposals based on the customer's past responses. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the customer's past response data into AI, which can automatically analyze the data and determine the priority of proposals.

[0082] The generation unit can adjust the order of proposals based on customer relevance when generating proposals. For example, the generation unit may first present the proposal that is most relevant to the customer's needs. The generation unit can also adjust the order of proposals based on the customer's areas of interest. For example, the generation unit may identify the customer's areas of interest and adjust the order of proposals accordingly. Furthermore, the generation unit may prioritize presenting the most relevant proposals according to the customer's business situation. For example, the generation unit may analyze the customer's business situation, identify the most relevant proposals based on that analysis, and prioritize presenting them. This allows the order of proposals to be adjusted based on customer relevance. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input customer relevance data into AI, which can automatically analyze the data and adjust the order of proposals.

[0083] The output unit can estimate the user's emotions and adjust the output format based on the estimated emotions. For example, if the user is stressed, the output unit will output in a simple and easy-to-read format. For example, if the user is stressed, the output unit will output in a simple and easy-to-read format. The output unit can also output in a format that includes detailed information if the user is relaxed. For example, if the user is relaxed, the output unit will output in a format that includes detailed information. Furthermore, if the user is in a hurry, the output unit can output in a concise format that gets straight to the point. For example, if the user is in a hurry, the output unit will output in a concise format that gets straight to the point. This allows the output format to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user emotion data into the AI, which can automatically analyze the emotions and adjust the output format.

[0084] The output unit can adjust the level of detail in the output based on the importance of the proposals. For example, the output unit can output important proposals in a format that includes detailed information. The output unit can also output general proposals in a format that includes standard information. Furthermore, the output unit can output less important proposals in a format that includes concise information. This allows the level of detail in the output to be adjusted according to the importance of the proposals. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input proposal importance data into AI, which can automatically analyze the data and adjust the level of detail in the output.

[0085] The output unit can apply different output formats depending on the customer's industry and business category when outputting data. For example, if the customer is in the manufacturing industry, the output unit can apply an output format specific to the manufacturing industry. The output unit can also apply an output format specific to the service industry if the customer is in the service industry. Furthermore, if the customer is in the retail industry, the output unit can apply an output format specific to the retail industry. This allows for the application of output formats tailored to the customer's industry and business category. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input customer industry data and business category data into AI, which can automatically analyze the data and apply the appropriate output format.

[0086] The output unit can estimate the user's emotions and adjust the order of output based on the estimated emotions. For example, if the user is stressed, the output unit will output important information first. For example, if the user is relaxed, the output unit will output all information evenly. For example, if the user is relaxed, the output unit will output all information evenly. Furthermore, if the user is in a hurry, the output unit will output the most important information with the highest priority. For example, if the user is in a hurry, the output unit will output the most important information with the highest priority. This allows the order of output to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user emotion data into the AI, which can automatically analyze the emotions and adjust the order of output.

[0087] The output unit can select the optimal output format when outputting data, taking into account the customer's geographical location information. For example, the output unit can output data in a format that includes region-specific information based on the customer's geographical location information. For example, the output unit can identify region-specific information based on the customer's geographical location information and output data in a format that includes it. The output unit can also output data in a format that includes nearby competitor information, taking into account the customer's geographical location information. For example, the output unit can identify nearby competitor information based on the customer's geographical location information and output data in a format that includes it. Furthermore, the output unit can output data in a format that includes regional market trends based on the customer's geographical location information. For example, the output unit can identify regional market trends based on the customer's geographical location information and output data in a format that includes it. This allows the output unit to select the optimal output format based on the customer's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the customer's geographical location data into AI, which can automatically analyze the data and select the optimal output format.

[0088] The output unit can analyze the customer's social media activity and output relevant information at the time of output. For example, the output unit can analyze the customer's social media activity and output information related to topics of interest. For example, the output unit can analyze the customer's social media activity, identify topics of interest, and output information related to them. The output unit can also output information related to business opportunities based on the customer's social media posts. For example, the output unit can analyze the customer's social media posts and output information related to business opportunities based on that. Furthermore, the output unit can output the latest information considering the customer's frequency of social media activity. For example, the output unit can analyze the customer's frequency of social media activity and output the latest information based on that. This allows the output unit to output relevant information based on the customer's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input customer social media data into AI, which can automatically analyze the data, identify relevant information, and output it.

[0089] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is stressed, the simulation unit can provide a simple and easy-to-understand scenario. The simulation unit can also provide a detailed scenario if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can provide a concise and to-the-point scenario. This allows the simulation scenario to be adjusted 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 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 simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into an AI, which can automatically analyze the emotions and adjust the simulation scenario.

[0090] The simulation unit can improve the accuracy of the simulation by referring to past sales data during the simulation. For example, the simulation unit can recreate successful sales scenarios based on past sales data. The simulation unit can also analyze past sales data and avoid unsuccessful sales scenarios. For example, the simulation unit can analyze past sales data, identify unsuccessful sales scenarios, and avoid them. Furthermore, the simulation unit can refer to past sales data to predict customer responses and improve the accuracy of the simulation. For example, the simulation unit can refer to past sales data to predict customer responses and improve the accuracy of the simulation based on that. This allows the simulation to improve accuracy based on past sales data. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past sales data into AI, which can automatically analyze the data and improve the accuracy of the simulation.

[0091] The simulation unit can apply different simulation methods depending on the customer's industry and business category during simulation. For example, if the customer is in the manufacturing industry, the simulation unit can apply a simulation method specialized for the manufacturing industry. Similarly, if the customer is in the service industry, the simulation unit can apply a simulation method specialized for the service industry. Furthermore, if the customer is in the retail industry, the simulation unit can apply a simulation method specialized for the retail industry. This allows for the application of simulation methods tailored to the customer's industry and business category. Some or all of the above-described processes in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input customer industry data and business category data into AI, which can automatically analyze the data and apply an appropriate simulation method.

[0092] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is stressed, the simulation unit can provide a simple and highly visible display method of the results. For example, if the user is stressed, the simulation unit can provide a simple and highly visible display method of the results. The simulation unit can also provide a display method of the results that includes detailed information if the user is relaxed. For example, if the user is relaxed, the simulation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the simulation unit can provide a concise display method that gets straight to the point. For example, if the user is in a hurry, the simulation unit can provide a concise display method that gets straight to the point. This allows the display method of the simulation results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into the AI, which can automatically analyze the emotions and adjust how the simulation results are displayed.

[0093] The simulation unit can perform simulations while taking into account the customer's geographical location information. For example, the simulation unit can simulate region-specific scenarios based on the customer's geographical location information. The simulation unit can also perform simulations that include nearby competitor information, taking into account the customer's geographical location information. Furthermore, the simulation unit can perform simulations that reflect regional market trends based on the customer's geographical location information. This allows simulations to be performed based on the customer's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer geographical location data into AI, which can automatically analyze the data and perform simulations.

[0094] The simulation unit can improve the accuracy of the simulation by referring to relevant customer literature during the simulation. For example, the simulation unit can refer to relevant customer literature and reflect information related to the customer's business in the simulation. For example, the simulation unit can refer to relevant customer literature and reflect information related to the customer's business in the simulation. The simulation unit can also perform simulations tailored to the customer's needs based on relevant customer literature. For example, the simulation unit can perform simulations tailored to the customer's needs based on relevant customer literature. Furthermore, the simulation unit can refer to relevant customer literature to predict customer responses and improve the accuracy of the simulation. For example, the simulation unit can refer to relevant customer literature to predict customer responses and improve the accuracy of the simulation based on those predictions. This allows the simulation to improve the accuracy of the simulation based on relevant customer literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer literature data into AI, which can automatically analyze the data and improve the accuracy of the simulation.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The data collection unit can analyze a customer's past purchase history when collecting sales opportunity information and prioritize the collection of relevant information. For example, based on a customer's past purchase history, the data collection unit can identify and collect information about products and services that the customer might be interested in. The data collection unit can also analyze a customer's purchase history and prioritize the collection of information related to products and services that the customer has purchased in the past. Furthermore, based on a customer's purchase history, the data collection unit can also collect information about products and services that the customer is likely to repurchase. This allows for the priority collection of relevant information based on a customer's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input customer purchase history data into AI, which can automatically analyze the data, identify relevant information, and collect it.

[0097] The generation unit can analyze past customer feedback and adjust the proposal content when generating proposals. For example, the generation unit can generate proposals that meet the customer's preferences and requests based on feedback provided by the customer in the past. The generation unit can also analyze customer feedback and generate proposals that improve on areas where the customer was dissatisfied. Furthermore, the generation unit can generate proposals that highlight points that the customer particularly valued based on customer feedback. This allows the proposal content to be adjusted based on past customer feedback. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer feedback data into AI, which can automatically analyze the data and adjust the proposal content.

[0098] The simulation unit can perform business negotiation simulations while taking the customer's cultural background into consideration. For example, the simulation unit can provide culturally appropriate scenarios based on the customer's cultural background. The simulation unit can also perform simulations to avoid cultural misunderstandings, taking the customer's cultural background into consideration. Furthermore, the simulation unit can provide simulations that take into account cultural customs and values, based on the customer's cultural background. This allows simulations to be performed while considering the customer's cultural background. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer cultural background data into AI, which can then automatically analyze the data and perform simulations.

[0099] The output unit can adjust the output content when outputting, taking into account the customer's past purchase history. For example, the output unit can highlight information about products and services that the customer might be interested in, based on the customer's past purchase history. The output unit can also prioritize outputting information related to products and services that the customer has previously purchased, taking into account the customer's purchase history. Furthermore, the output unit can output information about products and services that the customer is likely to repurchase, based on the customer's purchase history. This allows the output content to be adjusted based on the customer's past purchase history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input customer purchase history data into AI, which can then automatically analyze the data and adjust the output content.

[0100] The data collection unit can estimate the user's emotions and prioritize the sales opportunity information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information and prioritize collecting important information. If the user is relaxed, the data collection unit can collect all information equally. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting the most important information. This allows for the prioritization of sales opportunity information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can automatically analyze the emotions and determine the priority of sales opportunity information to collect.

[0101] The generation unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the generation unit can generate simple and easy-to-understand suggestions. If the user is relaxed, the generation unit can also generate suggestions that include detailed information. Furthermore, if the user is in a hurry, the generation unit can generate concise and to-the-point suggestions. This allows the presentation of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can automatically analyze the emotions and adjust the presentation of suggestions.

[0102] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is stressed, the simulation unit can provide a simple and easy-to-understand scenario. If the user is relaxed, the simulation unit can also provide a detailed scenario. Furthermore, if the user is in a hurry, the simulation unit can provide a concise and to-the-point scenario. This allows the simulation scenario to be adjusted 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 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 simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into an AI, which can automatically analyze the emotions and adjust the simulation scenario.

[0103] The output unit can estimate the user's emotions and adjust the output format based on the estimated emotions. For example, if the user is stressed, the output unit will output in a simple and highly visible format. If the user is relaxed, the output unit can also output in a format that includes detailed information. Furthermore, if the user is in a hurry, the output unit can output in a concise format that gets straight to the point. This allows the output format to be adjusted 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 output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into an AI, which can automatically analyze the emotions and adjust the output format.

[0104] The simulation unit can apply different simulation methods depending on the customer's industry and business category during simulation. For example, if the customer is in the manufacturing industry, the simulation unit will apply a simulation method specialized for manufacturing. Similarly, if the customer is in the service industry, the simulation unit can apply a simulation method specialized for the service industry. Furthermore, if the customer is in the retail industry, the simulation unit can apply a simulation method specialized for retail. This allows for the application of simulation methods tailored to the customer's industry and business category. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input customer industry data and business category data into AI, which can then automatically analyze the data and apply an appropriate simulation method.

[0105] The data collection unit can prioritize the collection of highly relevant information by considering the customer's geographical location when collecting business negotiation information. For example, the data collection unit can prioritize the collection of region-specific information based on the customer's geographical location. The data collection unit can also collect information on nearby competitors by considering the customer's geographical location. Furthermore, the data collection unit can collect regional market trends based on the customer's geographical location. This allows for the priority collection of highly relevant information based on the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer geographical location data into AI, which can automatically analyze the data, identify highly relevant information, and collect it.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The collection department gathers information about the business deal. This information includes, for example, the content of the deal, customer information, and past deal history. The collection department also gathers information such as meeting minutes, customer profile information (name, contact information, past transaction history, etc.), the purpose and agenda of the deal, and participants. Step 2: The generation unit analyzes the information collected by the collection unit and automatically generates proposals. Proposals are based on customer needs and past sales history. For example, new proposals are automatically generated based on products and services that the customer has shown interest in in the past. Proposals are also made based on customer requests, expectations, and problems. Step 3: The output unit outputs the proposal generated by the generation unit. The output is provided as a proposal document or presentation material. For example, the proposal document is output in PDF format and provided to the sales team. Step 4: The simulation department provides sales negotiation simulations using AI that has learned from customer reactions. The simulation predicts how customers will react and simulates how to respond. This improves the responsiveness of sales team members.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect details of business negotiations and customer information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and analyzes the collected information to automatically generate proposals. The output unit is implemented by the control unit 46A of the smart device 14, for example, and provides the generated proposals to the sales members. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and provides a business negotiation simulation using AI that has learned customer reactions. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect details of business negotiations and customer information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, to analyze the collected information and automatically generate proposals. The output unit is implemented by the control unit 46A of the smart glasses 214, for example, to provide the generated proposals to the sales members. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, to provide a business negotiation simulation using AI that has learned customer reactions. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect details of business negotiations and customer information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, to analyze the collected information and automatically generate proposals. The output unit is implemented by the control unit 46A of the headset terminal 314, for example, to provide the generated proposals to the sales members. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, to provide a business negotiation simulation using AI that has learned customer reactions. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] For example, the data collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect details of business negotiations and customer information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and analyzes the collected information to automatically generate proposals. The output unit is implemented by the control unit 46A of the robot 414, for example, and provides the generated proposals to the sales members. The simulation unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and provides a business negotiation simulation using AI that has learned customer reactions. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) The collection department collects information on business negotiations, A generation unit that automatically generates proposals based on the information collected by the collection unit, An output unit that outputs the proposal generated by the generation unit, It includes a simulation unit that provides business negotiation simulations. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information on the details of the business negotiation and customer information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The information collected by the aforementioned data collection unit is analyzed, and proposals are made based on customer needs and past business negotiation history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, We provide sales negotiation simulations using AI that has learned from customer reactions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting sales opportunity information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past sales negotiation history and select the appropriate information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting sales opportunity information, filter it based on the customer's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the opportunity information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting sales information, prioritize the collection of highly relevant information by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information on business opportunities, we analyze the customer's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating proposals, adjust the level of detail in the proposals based on the importance of the customer. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating proposals, different proposal algorithms are applied depending on the customer's industry and business category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating proposals, we prioritize them based on the customer's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating proposals, adjust the order of proposals based on customer relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The output unit is, It estimates the user's emotions and adjusts the output format based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The output unit is, When outputting, adjust the level of detail in the output based on the importance of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, When outputting, different output formats are applied depending on the customer's industry and business category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, It estimates the user's emotions and adjusts the order of the output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When outputting data, the system selects the optimal output format, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, During output, the system analyzes the customer's social media activity and outputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During simulations, past sales data is referenced to improve the accuracy of the simulations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During simulation, different simulation methods are applied depending on the customer's industry and business category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, During the simulation, the customer's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, During simulations, we refer to relevant customer literature to improve the accuracy of the simulations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 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 collection department collects information on business negotiations, A generation unit that automatically generates proposals based on the information collected by the collection unit, An output unit that outputs the proposal generated by the generation unit, It includes a simulation unit that provides business negotiation simulations. A system characterized by the following features.

2. The aforementioned collection unit is Collect information on the details of the business negotiation and the customer. The system according to feature 1.

3. The generating unit is The information collected by the aforementioned data collection unit is analyzed, and proposals are made based on customer needs and past business negotiation history. The system according to feature 1.

4. The aforementioned simulation unit, We provide sales negotiation simulations using AI that has learned from customer reactions. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting sales opportunity information based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze past sales negotiation history and select the appropriate information gathering method. The system according to feature 1.

7. The aforementioned collection unit is When collecting sales opportunity information, filter it based on the customer's current business situation and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the opportunity information to collect based on those estimated emotions. The system according to feature 1.

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