system

Abstract

The system according to the embodiment aims to support preparation for sales activities, practice of business negotiations, providing points for improvement in document creation, and evaluation of the effectiveness of sales activities. [Solution] The system according to the embodiment includes a reception unit, a dialogue unit, an analysis unit, and an evaluation unit. The reception unit receives input of business negotiation details. The dialogue unit conducts dialogue based on the business negotiation details received by the reception unit. The analysis unit analyzes materials created by the user and provides suggestions for improvement. The evaluation unit analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide for preparation of sales activities, practice of sales negotiations, suggestions for improvement in document creation, or evaluation of the effectiveness of sales activities, so there is room for improvement.

[0005] The system according to the embodiment aims to support preparation for sales activities, practice of business negotiations, providing points for improvement in document creation, and evaluation of the effectiveness of sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a dialogue unit, an analysis unit, and an evaluation unit. The reception unit receives input of business negotiation details. The dialogue unit conducts dialogue based on the business negotiation details received by the reception unit. The analysis unit analyzes materials created by the user and provides suggestions for improvement. The evaluation unit analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. [Effects of the Invention]

[0007] The system according to the embodiment can assist in the preparation of sales activities, practice of business negotiations, providing suggestions for improvement in document creation, and evaluation of the effectiveness of sales activities. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A sales support system according to an embodiment of the present invention supports preparation for sales activities. In this sales support system, an AI interacts with a user as a practice partner and simulates sales negotiations. The AI ​​then provides advice on creating materials and identifies areas for improvement in the materials created by the user. Furthermore, the AI ​​assists in analyzing and reviewing the user's sales activities, evaluating the effectiveness of the user's sales activities. For example, in a sales support system, a user inputs the details of the sales negotiation simulation into the AI, and the AI ​​initiates a dialogue based on the details. For example, when a user is giving a presentation on a new product, the AI ​​poses questions as a customer, and the user responds. Next, the sales support system uploads the materials created by the user to the AI, and the AI ​​analyzes the materials and identifies areas for improvement. For example, the AI ​​provides advice on the structure, design, and clarity of the content of the presentation materials. Furthermore, when a user inputs the results of their sales activities into the AI, the AI ​​analyzes the data and evaluates the effectiveness of the sales activities. For example, the AI ​​analyzes the success rate of sales negotiations and customer reactions, and suggests areas for improvement. This allows sales representatives to effectively prepare for sales negotiations and improve the efficiency of their sales activities. As a result, the sales support system allows sales representatives to prepare for sales negotiations effectively and improve the efficiency of their sales activities. For example, sales negotiation simulations allow sales representatives to approach negotiations with confidence, and advice on creating materials allows them to provide high-quality materials. In addition, by analyzing and reviewing sales activities, sales representatives can identify areas for improvement for the next sales negotiation and conduct more effective sales activities.

[0029] A sales support system according to an embodiment includes a reception unit, a dialogue unit, an analysis unit, and an evaluation unit. The reception unit receives input of business negotiation details. The business negotiation details include, but are not limited to, product descriptions, price negotiations, and contract terms. The reception unit can receive the business negotiation details via, for example, text input, voice input, or image input. The dialogue unit engages in dialogue based on the business negotiation details received by the reception unit. The dialogue unit uses a generation AI to engage in dialogue with a user. For example, the generation AI may ask questions as a customer based on the business negotiation details entered by the user, and the user responds to the questions. The dialogue unit can also use the generation AI to simulate a business negotiation. For example, when a user gives a presentation about a new product, the generation AI may ask questions as a customer, and the user responds to the questions. The analysis unit analyzes materials created by the user and suggests improvements. The analysis unit uses the generation AI to analyze the structure, design, and clarity of the materials. For example, the generation AI may analyze presentation materials uploaded by a user and suggest improvements. The analysis unit can also use the generation AI to analyze the contents of the materials and provide specific improvements. For example, the generation AI provides advice on the structure and design of presentation materials, the clarity of the content, etc. The evaluation unit analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. The evaluation unit uses the generation AI to analyze the results of sales activities. For example, the generation AI analyzes the success rate of sales negotiations and customer reactions, etc., and evaluates the effectiveness of the sales activities. The evaluation unit can also use the generation AI to review sales activities and suggest improvements for the next sales negotiation. For example, the generation AI analyzes the success rate of sales negotiations and customer reactions, etc., and suggests improvements. As a result, the sales support system according to the embodiment can support preparation for sales activities and assist with sales negotiation practice, advice on document creation, and analysis and review of sales activities.

[0030] The reception unit analyzes the user's past negotiation history and selects an efficient input method. For example, if the user has frequently used voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has frequently used images in the past, the reception unit may preferentially suggest image input. This allows efficient input of negotiation details by selecting the optimal input method based on the user's past negotiation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past negotiation history data into a generation AI and have the generation AI select the optimal input method.

[0031] When inputting business negotiation details, the reception unit filters the details based on the user's current project and areas of interest. For example, the reception unit prioritizes displaying business negotiation details related to a project currently in progress by the user. The reception unit can also prioritize displaying business negotiation details related to the user's areas of interest. The reception unit can also filter related business negotiation details based on the user's past project history. This allows for prioritized input of highly relevant business negotiation details by filtering the business negotiation details based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter the business negotiation details.

[0032] When entering business negotiation details, the reception unit selects an efficient input means according to the user's input method. For example, if the user selects voice input, the reception unit performs input using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also perform input using image recognition technology. This enables efficient input of business negotiation details by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0033] The reception unit sets criteria for prioritizing the input of relevant content by taking into account the user's geographical location information when inputting business negotiation content. For example, if the user is in a specific area, the reception unit prioritizes the input of business negotiation content related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize the input of relevant business negotiation content based on the user's current location. Furthermore, if the user is in a specific city, the reception unit can also prioritize the input of business negotiation content related to that city. Thus, by inputting business negotiation content while taking into account the user's geographical location information, it is possible to prioritize the input of highly relevant business negotiation content. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data into a generation AI and have the generation AI determine the priority of the business negotiation content.

[0034] When entering business negotiation details, the reception unit analyzes the user's social media activity and inputs related details. For example, the reception unit prioritizes inputting business negotiation details mentioned by the user on social media. The reception unit can also analyze the user's social media activity and input related business negotiation details. The reception unit can also input related business negotiation details with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and inputting business negotiation details, it is possible to prioritize input of highly relevant business negotiation details. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select business negotiation details.

[0035] The reception unit customizes the input method based on the user's past feedback when entering business negotiation details. For example, if the user has previously preferred voice input, the reception unit may preferentially suggest voice input. Furthermore, if the user has previously preferred text input, the reception unit may preferentially suggest text input. Furthermore, if the user has previously preferred image input, the reception unit may preferentially suggest image input. This allows for efficient input of business negotiation details by customizing the input method in accordance with the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past feedback data into a generation AI and cause the generation AI to customize the input method.

[0036] During the dialogue, the dialogue unit adjusts the level of detail of the dialogue based on the importance of the business negotiation content. For example, the dialogue unit conducts a detailed dialogue for business negotiation content of high importance. The dialogue unit can also conduct a concise dialogue for business negotiation content of low importance. The dialogue unit can also adjust the depth of the dialogue depending on the importance. This enables efficient dialogue by adjusting the level of detail of the dialogue based on the importance of the business negotiation content. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input importance data of the business negotiation content to the generation AI and cause the generation AI to adjust the level of detail of the dialogue.

[0037] During the dialogue, the dialogue unit applies different dialogue algorithms depending on the category of the business negotiation. For example, in the case of a new product presentation, the dialogue unit applies a dialogue algorithm that emphasizes the product's features and advantages. In the case of contract negotiation, the dialogue unit can also apply a dialogue algorithm related to conditions and price. In the case of customer support, the dialogue unit can also apply a dialogue algorithm that focuses on problem solving. In this way, applying a dialogue algorithm depending on the category of the business negotiation enables more effective dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input business negotiation category data into the generation AI and have the generation AI apply the dialogue algorithm.

[0038] During a dialogue, the dialogue unit improves the accuracy of the dialogue based on the user's past dialogue results. The dialogue unit improves the accuracy of the dialogue based on, for example, the content of the dialogues the user has had in the past. The dialogue unit can also analyze the user's past dialogue history and suggest an optimal dialogue method. The dialogue unit can also customize the content of the dialogue by referring to the user's past dialogue results. This enables more effective dialogue by improving the accuracy of the dialogue by referring to the user's past dialogue results. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's past dialogue data into a generation AI and cause the generation AI to improve the accuracy of the dialogue.

[0039] During dialogue, the dialogue unit determines the priority of dialogues based on the submission date of the business negotiations. For example, the dialogue unit prioritizes dialogues for business negotiations with an upcoming submission deadline. The dialogue unit can also postpone dialogues for business negotiations with a distant submission deadline. The dialogue unit can also adjust the priority of dialogues according to the submission date. This enables efficient dialogues by determining the priority of dialogues based on the submission date of the business negotiations. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the submission date of the business negotiations into the generation AI and have the generation AI determine the priority of dialogues.

[0040] During the dialogue, the dialogue unit adjusts the order of dialogue based on the relevance of the business negotiations. For example, the dialogue unit prioritizes dialogue for highly relevant business negotiations. The dialogue unit can also postpone less relevant business negotiations. The dialogue unit can also adjust the order of dialogue according to the relevance of the business negotiations. This enables efficient dialogue by adjusting the order of dialogue based on the relevance of the business negotiations. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input relevance data of the business negotiations into the generation AI and have the generation AI adjust the order of dialogues.

[0041] During the dialogue, the dialogue unit adjusts the use of technical terms in the dialogue based on the user's level of expertise. For example, if the user has specialized knowledge, the dialogue unit uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the dialogue unit can also avoid technical terms. Furthermore, the dialogue unit can adjust the content of the dialogue according to the user's level of expertise. This allows for more appropriate dialogue by adjusting the use of technical terms in the dialogue according to the user's level of expertise. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit may input the user's specialized knowledge data into a generation AI and have the generation AI execute the use of technical terms in the dialogue.

[0042] When analyzing documents, the analysis unit adjusts the level of detail of the analysis based on the importance of the documents. For example, the analysis unit performs a detailed analysis of documents with high importance. The analysis unit can also perform a concise analysis of documents with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the documents. This enables efficient document analysis by adjusting the level of detail of the analysis based on the importance of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input document importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0043] When analyzing documents, the analysis unit applies different analysis algorithms depending on the category of the document. For example, in the case of presentation documents, the analysis unit applies an analysis algorithm that emphasizes visual elements. In addition, in the case of contracts, the analysis unit can also apply an analysis algorithm that emphasizes legal elements. In addition, in the case of technical documents, the analysis unit can also apply an analysis algorithm that emphasizes technical elements. In this way, by applying an analysis algorithm depending on the category of the document, more effective document analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document category data into the generation AI and have the generation AI apply the analysis algorithm.

[0044] During document analysis, the analysis unit improves the accuracy of the analysis based on the user's past document analysis results. For example, the analysis unit improves the accuracy of the analysis based on the results of document analysis previously performed by the user. The analysis unit can also analyze the user's past document analysis history and propose an optimal analysis method. The analysis unit can also customize the content of the analysis by referring to the user's past document analysis results. This enables more effective document analysis by improving the accuracy of the analysis by referring to the user's past document analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past document analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] When analyzing documents, the analysis unit determines the priority of analysis based on the submission date of the documents. For example, the analysis unit prioritizes analysis of documents with an upcoming submission deadline. The analysis unit can also postpone analysis of documents with a distant submission deadline. The analysis unit can also adjust the priority of analysis according to the submission date. This enables efficient document analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document submission date data into the generation AI and have the generation AI determine the analysis priority.

[0046] When analyzing documents, the analysis unit adjusts the order of analysis based on the relevance of the documents. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. The analysis unit can also adjust the order of analysis according to the relevance of the documents. This enables efficient document analysis by adjusting the order of analysis based on the relevance of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0047] When analyzing documents, the analysis unit adjusts the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also avoid technical terms. The analysis unit can also adjust the content of the analysis according to the user's level of expertise. This allows for more appropriate document analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's technical expertise data into a generation AI and have the generation AI use technical terms in the analysis.

[0048] When evaluating sales activities, the evaluation unit refers to past sales data to improve the efficiency of the evaluation algorithm. The evaluation unit, for example, optimizes the evaluation algorithm based on past sales data. The evaluation unit can also analyze past sales data and propose an optimal evaluation method. The evaluation unit can also improve the accuracy of the evaluation by referring to past sales data. This enables more effective evaluation by optimizing the evaluation algorithm by referring to past sales data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past sales data into a generation AI and have the generation AI optimize the evaluation algorithm.

[0049] The evaluation unit updates the evaluation data by reflecting user feedback when evaluating sales activities. The evaluation unit updates the evaluation data based on, for example, user feedback. The evaluation unit can also analyze user feedback and improve the evaluation method. The evaluation unit can also improve the accuracy of the evaluation by referring to user feedback. This enables more effective evaluation by updating the evaluation data by reflecting user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input user feedback data into a generation AI and have the generation AI update the evaluation data.

[0050] When evaluating sales activities, the evaluation unit sets criteria for detailed analysis of the success rate of sales negotiations and customer reactions. The evaluation unit evaluates sales activities based on, for example, the success rate of sales negotiations. The evaluation unit can also analyze customer reactions and evaluate sales activities. The evaluation unit can also evaluate sales activities by comprehensively analyzing the success rate of sales negotiations and customer reactions. This enables more effective evaluation of sales activities by detailed analysis of the success rate of sales negotiations and customer reactions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input sales negotiation success rate data and customer reaction data into the generation AI and have the generation AI perform a detailed analysis.

[0051] When evaluating sales activities, the evaluation unit weights the evaluation data based on the submission time of the business negotiations. For example, the evaluation unit may weight business negotiations with an upcoming submission deadline more highly. The evaluation unit may also weight business negotiations with a distant submission deadline less highly. The evaluation unit may also adjust the weighting of the evaluation data depending on the submission time. This allows for more effective evaluation by weighting the evaluation data based on the submission time of the business negotiations. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input business negotiation submission time data into the generation AI and have the generation AI perform weighting of the evaluation data.

[0052] The evaluation unit integrates information from different data sources to enrich the evaluation data when evaluating sales activities. For example, the evaluation unit integrates customer feedback data to enrich the evaluation data. The evaluation unit can also integrate sales activity record data to enrich the evaluation data. The evaluation unit can also integrate market data to enrich the evaluation data. This enables more effective evaluation by integrating information from different data sources to enrich the evaluation data. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input information from different data sources into the generation AI and have the generation AI integrate the evaluation data.

[0053] The evaluation unit adjusts the evaluation algorithm based on the user's past feedback when evaluating sales activities. The evaluation unit adjusts the evaluation algorithm based on, for example, the user's past feedback. The evaluation unit can also analyze the user's past feedback and propose an optimal evaluation method. The evaluation unit can also improve the accuracy of the evaluation by referring to the user's past feedback. This enables more effective evaluation by adjusting the evaluation algorithm to reflect the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the evaluation algorithm.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit analyzes the user's past negotiation history and selects an efficient input method. For example, if the user has frequently used voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has frequently used images in the past, the reception unit may preferentially suggest image input. This allows efficient input of negotiation details by selecting the optimal input method based on the user's past negotiation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past negotiation history data into the generation AI and have the generation AI select the optimal input method.

[0056] When inputting business negotiation details, the reception unit filters the details based on the user's current project and areas of interest. For example, the reception unit prioritizes displaying business negotiation details related to the user's ongoing project. The reception unit can also prioritize displaying business negotiation details related to the user's areas of interest. Furthermore, the reception unit can filter related business negotiation details based on the user's past project history. This allows for prioritized input of highly relevant business negotiation details by filtering the business negotiation details based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter the business negotiation details.

[0057] When entering business negotiation details, the reception unit selects an efficient input means according to the user's input method. For example, if the user selects voice input, the input is performed using voice recognition technology. The reception unit can also support keyboard input if the user selects text input. Furthermore, if the user selects image input, the reception unit can also perform input using image recognition technology. This enables efficient input of business negotiation details by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal input means.

[0058] During the dialogue, the dialogue unit adjusts the level of detail of the dialogue based on the importance of the business negotiation content. For example, a detailed dialogue is conducted for business negotiation content of high importance. The dialogue unit can also conduct a brief dialogue for business negotiation content of low importance. Furthermore, the dialogue unit can adjust the depth of the dialogue according to the importance. This enables efficient dialogue by adjusting the level of detail of the dialogue based on the importance of the business negotiation content. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input importance data of the business negotiation content to the generation AI and cause the generation AI to adjust the level of detail of the dialogue.

[0059] During the dialogue, the dialogue unit applies different dialogue algorithms depending on the category of the business negotiation. For example, in the case of a new product presentation, a dialogue algorithm that emphasizes the product's features and advantages is applied. In addition, in the case of contract negotiations, the dialogue unit can also apply a dialogue algorithm related to conditions and price. Furthermore, in the case of customer support, the dialogue unit can also apply a dialogue algorithm that focuses on problem solving. In this way, applying a dialogue algorithm depending on the category of the business negotiation enables more effective dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input business negotiation category data into the generation AI and have the generation AI apply the dialogue algorithm.

[0060] During a dialogue, the dialogue unit improves the accuracy of the dialogue based on the user's past dialogue results. For example, the dialogue unit improves the accuracy of the dialogue based on the content of the dialogues the user has had in the past. The dialogue unit can also analyze the user's past dialogue history and suggest an optimal dialogue method. Furthermore, the dialogue unit can also customize the content of the dialogue by referring to the user's past dialogue results. This enables more effective dialogue by improving the dialogue accuracy by referring to the user's past dialogue results. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's past dialogue data into the generation AI and have the generation AI improve the dialogue accuracy.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts input of business negotiation details. The business negotiation details include product descriptions, price negotiations, contract terms, etc. The reception unit can accept business negotiation details by text input, voice input, image input, etc. Step 2: The dialogue unit conducts a dialogue based on the business negotiation details received by the reception unit. The dialogue unit uses a generation AI to dialogue with the user. For example, the generation AI may ask questions as a customer based on the business negotiation details entered by the user, and the user may respond to those questions. The dialogue unit can also use the generation AI to simulate a business negotiation. Step 3: The analysis unit analyzes the materials created by the user and suggests improvements. The analysis unit uses the generation AI to analyze the structure, design, and clarity of the content of the materials. For example, the generation AI analyzes presentation materials uploaded by the user and suggests improvements. The analysis unit can also use the generation AI to analyze the content of the materials and suggest specific improvements. Step 4: The evaluation department analyzes the results of the sales activities and evaluates their effectiveness based on the evaluation criteria. The evaluation department uses the generation AI to analyze the results of the sales activities. For example, the generation AI analyzes the success rate of sales negotiations and customer reactions, and evaluates the effectiveness of the sales activities. The evaluation department can also use the generation AI to review the sales activities and suggest areas for improvement for the next sales negotiation.

[0063] (Example 2) A sales support system according to an embodiment of the present invention supports preparation for sales activities. In this sales support system, an AI interacts with a user as a practice partner and simulates sales negotiations. The AI ​​then provides advice on creating materials and identifies areas for improvement in the materials created by the user. Furthermore, the AI ​​assists in analyzing and reviewing the user's sales activities, evaluating the effectiveness of the user's sales activities. For example, in a sales support system, a user inputs the details of the sales negotiation simulation into the AI, and the AI ​​initiates a dialogue based on the details. For example, when a user is giving a presentation on a new product, the AI ​​poses questions as a customer, and the user responds. Next, the sales support system uploads the materials created by the user to the AI, and the AI ​​analyzes the materials and identifies areas for improvement. For example, the AI ​​provides advice on the structure, design, and clarity of the content of the presentation materials. Furthermore, when a user inputs the results of their sales activities into the AI, the AI ​​analyzes the data and evaluates the effectiveness of the sales activities. For example, the AI ​​analyzes the success rate of sales negotiations and customer reactions, and suggests areas for improvement. This allows sales representatives to effectively prepare for sales negotiations and improve the efficiency of their sales activities. As a result, the sales support system allows sales representatives to prepare for sales negotiations effectively and improve the efficiency of their sales activities. For example, sales negotiation simulations allow sales representatives to approach negotiations with confidence, and advice on creating materials allows them to provide high-quality materials. In addition, by analyzing and reviewing sales activities, sales representatives can identify areas for improvement for the next sales negotiation and conduct more effective sales activities.

[0064] A sales support system according to an embodiment includes a reception unit, a dialogue unit, an analysis unit, and an evaluation unit. The reception unit receives input of business negotiation details. The business negotiation details include, but are not limited to, product descriptions, price negotiations, and contract terms. The reception unit can receive the business negotiation details via, for example, text input, voice input, or image input. The dialogue unit engages in dialogue based on the business negotiation details received by the reception unit. The dialogue unit uses a generation AI to engage in dialogue with a user. For example, the generation AI may ask questions as a customer based on the business negotiation details entered by the user, and the user responds to the questions. The dialogue unit can also use the generation AI to simulate a business negotiation. For example, when a user gives a presentation about a new product, the generation AI may ask questions as a customer, and the user responds to the questions. The analysis unit analyzes materials created by the user and suggests improvements. The analysis unit uses the generation AI to analyze the structure, design, and clarity of the materials. For example, the generation AI may analyze presentation materials uploaded by a user and suggest improvements. The analysis unit can also use the generation AI to analyze the contents of the materials and provide specific improvements. For example, the generation AI provides advice on the structure and design of presentation materials, the clarity of the content, etc. The evaluation unit analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. The evaluation unit uses the generation AI to analyze the results of sales activities. For example, the generation AI analyzes the success rate of sales negotiations and customer reactions, etc., and evaluates the effectiveness of the sales activities. The evaluation unit can also use the generation AI to review sales activities and suggest improvements for the next sales negotiation. For example, the generation AI analyzes the success rate of sales negotiations and customer reactions, etc., and suggests improvements. As a result, the sales support system according to the embodiment can support preparation for sales activities and assist with sales negotiation practice, advice on document creation, and analysis and review of sales activities.

[0065] The reception unit estimates the user's emotions and adjusts the timing of inputting the business negotiation details based on the estimated user emotions. For example, if the user is nervous, the reception unit delays the input timing to allow the user to relax. Furthermore, if the user is relaxed, the reception unit can also speed up the input timing to allow the user to input smoothly. Furthermore, if the user is in a hurry, the reception unit can also optimize the timing to complete the input quickly. By adjusting the input timing of the business negotiation details according to the user's emotions, the business negotiation details can be input at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0066] The reception unit analyzes the user's past negotiation history and selects an efficient input method. For example, if the user has frequently used voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has frequently used images in the past, the reception unit may preferentially suggest image input. This allows efficient input of negotiation details by selecting the optimal input method based on the user's past negotiation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past negotiation history data into a generation AI and have the generation AI select the optimal input method.

[0067] When inputting business negotiation details, the reception unit filters the details based on the user's current project and areas of interest. For example, the reception unit prioritizes displaying business negotiation details related to a project currently in progress by the user. The reception unit can also prioritize displaying business negotiation details related to the user's areas of interest. The reception unit can also filter related business negotiation details based on the user's past project history. This allows for prioritized input of highly relevant business negotiation details by filtering the business negotiation details based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter the business negotiation details.

[0068] When entering business negotiation details, the reception unit selects an efficient input means according to the user's input method. For example, if the user selects voice input, the reception unit performs input using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also perform input using image recognition technology. This enables efficient input of business negotiation details by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0069] The reception unit estimates the user's emotions and determines the priority of the business negotiation contents to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit postpones input of less important business negotiation contents. Furthermore, if the user is relaxed, the reception unit can also prioritize input of more important business negotiation contents. Furthermore, if the user is in a hurry, the reception unit can also prioritize input of business negotiation contents that require quick processing. Thus, by determining the priority of business negotiation contents according to the user's emotions, the business negotiation contents can be input in a more appropriate order. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] The reception unit sets criteria for prioritizing the input of relevant content by taking into account the user's geographical location information when inputting business negotiation content. For example, if the user is in a specific area, the reception unit prioritizes the input of business negotiation content related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize the input of relevant business negotiation content based on the user's current location. Furthermore, if the user is in a specific city, the reception unit can also prioritize the input of business negotiation content related to that city. Thus, by inputting business negotiation content while taking into account the user's geographical location information, it is possible to prioritize the input of highly relevant business negotiation content. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data into a generation AI and have the generation AI determine the priority of the business negotiation content.

[0071] When entering business negotiation details, the reception unit analyzes the user's social media activity and inputs related details. For example, the reception unit prioritizes inputting business negotiation details mentioned by the user on social media. The reception unit can also analyze the user's social media activity and input related business negotiation details. The reception unit can also input related business negotiation details with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and inputting business negotiation details, it is possible to prioritize input of highly relevant business negotiation details. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select business negotiation details.

[0072] The reception unit customizes the input method based on the user's past feedback when entering business negotiation details. For example, if the user has previously preferred voice input, the reception unit may preferentially suggest voice input. Furthermore, if the user has previously preferred text input, the reception unit may preferentially suggest text input. Furthermore, if the user has previously preferred image input, the reception unit may preferentially suggest image input. This allows for efficient input of business negotiation details by customizing the input method in accordance with the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past feedback data into a generation AI and cause the generation AI to customize the input method.

[0073] The dialogue unit estimates the user's emotions and adjusts the dialogue expression method based on the estimated user emotions. For example, if the user is nervous, the dialogue unit may use a calm tone. If the user is relaxed, the dialogue unit may also use a friendly tone. If the user is in a hurry, the dialogue unit may also use a quick and concise dialogue. This allows for more appropriate dialogue by adjusting the dialogue expression method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dialogue unit may be performed using AI, or may be performed without AI. For example, the dialogue unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0074] During the dialogue, the dialogue unit adjusts the level of detail of the dialogue based on the importance of the business negotiation content. For example, the dialogue unit conducts a detailed dialogue for business negotiation content of high importance. The dialogue unit can also conduct a concise dialogue for business negotiation content of low importance. The dialogue unit can also adjust the depth of the dialogue depending on the importance. This enables efficient dialogue by adjusting the level of detail of the dialogue based on the importance of the business negotiation content. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input importance data of the business negotiation content to the generation AI and cause the generation AI to adjust the level of detail of the dialogue.

[0075] During the dialogue, the dialogue unit applies different dialogue algorithms depending on the category of the business negotiation. For example, in the case of a new product presentation, the dialogue unit applies a dialogue algorithm that emphasizes the product's features and advantages. In the case of contract negotiation, the dialogue unit can also apply a dialogue algorithm related to conditions and price. In the case of customer support, the dialogue unit can also apply a dialogue algorithm that focuses on problem solving. In this way, applying a dialogue algorithm depending on the category of the business negotiation enables more effective dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input business negotiation category data into the generation AI and have the generation AI apply the dialogue algorithm.

[0076] During a dialogue, the dialogue unit improves the accuracy of the dialogue based on the user's past dialogue results. The dialogue unit improves the accuracy of the dialogue based on, for example, the content of the dialogues the user has had in the past. The dialogue unit can also analyze the user's past dialogue history and suggest an optimal dialogue method. The dialogue unit can also customize the content of the dialogue by referring to the user's past dialogue results. This enables more effective dialogue by improving the accuracy of the dialogue by referring to the user's past dialogue results. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's past dialogue data into a generation AI and cause the generation AI to improve the accuracy of the dialogue.

[0077] The dialogue unit estimates the user's emotions and adjusts the length of the dialogue based on the estimated user emotions. For example, if the user is nervous, the dialogue unit may hold a short dialogue. Furthermore, if the user is relaxed, the dialogue unit may hold a longer dialogue. Furthermore, if the user is in a hurry, the dialogue unit may quickly complete the dialogue. This allows for more appropriate dialogue by adjusting the length of the dialogue according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0078] During dialogue, the dialogue unit determines the priority of dialogues based on the submission date of the business negotiations. For example, the dialogue unit prioritizes dialogues for business negotiations with an upcoming submission deadline. The dialogue unit can also postpone dialogues for business negotiations with a distant submission deadline. The dialogue unit can also adjust the priority of dialogues according to the submission date. This enables efficient dialogues by determining the priority of dialogues based on the submission date of the business negotiations. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the submission date of the business negotiations into the generation AI and have the generation AI determine the priority of dialogues.

[0079] During the dialogue, the dialogue unit adjusts the order of dialogue based on the relevance of the business negotiations. For example, the dialogue unit prioritizes dialogue for highly relevant business negotiations. The dialogue unit can also postpone less relevant business negotiations. The dialogue unit can also adjust the order of dialogue according to the relevance of the business negotiations. This enables efficient dialogue by adjusting the order of dialogue based on the relevance of the business negotiations. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input relevance data of the business negotiations into the generation AI and have the generation AI adjust the order of dialogues.

[0080] During the dialogue, the dialogue unit adjusts the use of technical terms in the dialogue based on the user's level of expertise. For example, if the user has specialized knowledge, the dialogue unit uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the dialogue unit can also avoid technical terms. Furthermore, the dialogue unit can adjust the content of the dialogue according to the user's level of expertise. This allows for more appropriate dialogue by adjusting the use of technical terms in the dialogue according to the user's level of expertise. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit may input the user's specialized knowledge data into a generation AI and have the generation AI execute the use of technical terms in the dialogue.

[0081] The analysis unit estimates the user's emotions and adjusts the method of analyzing the materials based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. Alternatively, if the user is in a hurry, the analysis unit can quickly provide an analysis result. By adjusting the method of analyzing the materials according to the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] When analyzing documents, the analysis unit adjusts the level of detail of the analysis based on the importance of the documents. For example, the analysis unit performs a detailed analysis of documents with high importance. The analysis unit can also perform a concise analysis of documents with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of the documents. This enables efficient document analysis by adjusting the level of detail of the analysis based on the importance of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input document importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0083] When analyzing documents, the analysis unit applies different analysis algorithms depending on the category of the document. For example, in the case of presentation documents, the analysis unit applies an analysis algorithm that emphasizes visual elements. In addition, in the case of contracts, the analysis unit can also apply an analysis algorithm that emphasizes legal elements. In addition, in the case of technical documents, the analysis unit can also apply an analysis algorithm that emphasizes technical elements. In this way, by applying an analysis algorithm depending on the category of the document, more effective document analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document category data into the generation AI and have the generation AI apply the analysis algorithm.

[0084] During document analysis, the analysis unit improves the accuracy of the analysis based on the user's past document analysis results. For example, the analysis unit improves the accuracy of the analysis based on the results of document analysis previously performed by the user. The analysis unit can also analyze the user's past document analysis history and propose an optimal analysis method. The analysis unit can also customize the content of the analysis by referring to the user's past document analysis results. This enables more effective document analysis by improving the accuracy of the analysis by referring to the user's past document analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past document analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0085] The analysis unit estimates the user's emotions and adjusts the analysis order of the materials based on the estimated user emotions. For example, if the user is nervous, the analysis unit postpones analysis of less important materials. Furthermore, if the user is relaxed, the analysis unit can prioritize analysis of more important materials. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of materials that require quick analysis. By adjusting the analysis order of materials according to the user's emotions, the materials can be analyzed in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0086] When analyzing documents, the analysis unit determines the priority of analysis based on the submission date of the documents. For example, the analysis unit prioritizes analysis of documents with an upcoming submission deadline. The analysis unit can also postpone analysis of documents with a distant submission deadline. The analysis unit can also adjust the priority of analysis according to the submission date. This enables efficient document analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document submission date data into the generation AI and have the generation AI determine the analysis priority.

[0087] When analyzing documents, the analysis unit adjusts the order of analysis based on the relevance of the documents. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. The analysis unit can also adjust the order of analysis according to the relevance of the documents. This enables efficient document analysis by adjusting the order of analysis based on the relevance of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0088] When analyzing documents, the analysis unit adjusts the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also avoid technical terms. The analysis unit can also adjust the content of the analysis according to the user's level of expertise. This allows for more appropriate document analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's technical expertise data into a generation AI and have the generation AI use technical terms in the analysis.

[0089] The evaluation unit estimates the user's emotions and adjusts the evaluation method for sales activities based on the estimated user emotions. For example, the evaluation unit provides a simple evaluation method when the user is nervous. The evaluation unit can also provide a detailed evaluation method when the user is relaxed. The evaluation unit can also quickly provide evaluation results when the user is in a hurry. This enables more appropriate evaluation by adjusting the evaluation method for sales activities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] When evaluating sales activities, the evaluation unit refers to past sales data to improve the efficiency of the evaluation algorithm. The evaluation unit, for example, optimizes the evaluation algorithm based on past sales data. The evaluation unit can also analyze past sales data and propose an optimal evaluation method. The evaluation unit can also improve the accuracy of the evaluation by referring to past sales data. This enables more effective evaluation by optimizing the evaluation algorithm by referring to past sales data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past sales data into a generation AI and have the generation AI optimize the evaluation algorithm.

[0091] The evaluation unit updates the evaluation data by reflecting user feedback when evaluating sales activities. The evaluation unit updates the evaluation data based on, for example, user feedback. The evaluation unit can also analyze user feedback and improve the evaluation method. The evaluation unit can also improve the accuracy of the evaluation by referring to user feedback. This enables more effective evaluation by updating the evaluation data by reflecting user feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input user feedback data into a generation AI and have the generation AI update the evaluation data.

[0092] When evaluating sales activities, the evaluation unit sets criteria for detailed analysis of the success rate of sales negotiations and customer reactions. The evaluation unit evaluates sales activities based on, for example, the success rate of sales negotiations. The evaluation unit can also analyze customer reactions and evaluate sales activities. The evaluation unit can also evaluate sales activities by comprehensively analyzing the success rate of sales negotiations and customer reactions. This enables more effective evaluation of sales activities by detailed analysis of the success rate of sales negotiations and customer reactions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input sales negotiation success rate data and customer reaction data into the generation AI and have the generation AI perform a detailed analysis.

[0093] The evaluation unit estimates the user's emotions and determines the priority of the evaluations based on the estimated user emotions. For example, if the user is nervous, the evaluation unit postpones evaluations of lower importance. Furthermore, if the user is relaxed, the evaluation unit can also prioritize evaluations of higher importance. Furthermore, if the user is in a hurry, the evaluation unit can prioritize evaluations of items that require quick evaluation. This allows evaluations to be performed in a more appropriate order by determining the priority of evaluations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, an AI. For example, the evaluation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0094] When evaluating sales activities, the evaluation unit weights the evaluation data based on the submission time of the business negotiations. For example, the evaluation unit may weight business negotiations with an upcoming submission deadline more highly. The evaluation unit may also weight business negotiations with a distant submission deadline less highly. The evaluation unit may also adjust the weighting of the evaluation data depending on the submission time. This allows for more effective evaluation by weighting the evaluation data based on the submission time of the business negotiations. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input business negotiation submission time data into the generation AI and have the generation AI perform weighting of the evaluation data.

[0095] The evaluation unit integrates information from different data sources to enrich the evaluation data when evaluating sales activities. For example, the evaluation unit integrates customer feedback data to enrich the evaluation data. The evaluation unit can also integrate sales activity record data to enrich the evaluation data. The evaluation unit can also integrate market data to enrich the evaluation data. This enables more effective evaluation by integrating information from different data sources to enrich the evaluation data. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input information from different data sources into the generation AI and have the generation AI integrate the evaluation data.

[0096] The evaluation unit adjusts the evaluation algorithm based on the user's past feedback when evaluating sales activities. The evaluation unit adjusts the evaluation algorithm based on, for example, the user's past feedback. The evaluation unit can also analyze the user's past feedback and propose an optimal evaluation method. The evaluation unit can also improve the accuracy of the evaluation by referring to the user's past feedback. This enables more effective evaluation by adjusting the evaluation algorithm to reflect the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the evaluation algorithm. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, dialogue unit, analysis unit, and evaluation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of the user's business negotiation details. The dialogue unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes materials created by the user and suggests improvements. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, dialogue unit, analysis unit, and evaluation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of the user's business negotiation details. The dialogue unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using a generative AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes materials created by the user and suggests improvements. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, dialogue unit, analysis unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives input of the content of the business negotiation from the user. The dialogue unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes materials created by the user and suggests improvements. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, dialogue unit, analysis unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of the user's business negotiation details. The dialogue unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and engages in dialogue with the user using a generative AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes materials created by the user and suggests improvements. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit analyzes the user's past negotiation history and selects an efficient input method. For example, if the user has frequently used voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has frequently used images in the past, the reception unit may preferentially suggest image input. This allows efficient input of negotiation details by selecting the optimal input method based on the user's past negotiation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past negotiation history data into the generation AI and have the generation AI select the optimal input method.

[0099] The reception unit estimates the user's emotions and adjusts the timing of inputting the business negotiation details based on the estimated user emotions. For example, if the user is nervous, the reception unit delays the input timing to allow the user to relax. Furthermore, if the user is relaxed, the reception unit can also speed up the input timing to allow the user to input smoothly. Furthermore, if the user is in a hurry, the reception unit can optimize the timing to complete the input quickly. This allows the business negotiation details to be input at a more appropriate time by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] When inputting business negotiation details, the reception unit filters the details based on the user's current project and areas of interest. For example, the reception unit prioritizes displaying business negotiation details related to the user's ongoing project. The reception unit can also prioritize displaying business negotiation details related to the user's areas of interest. Furthermore, the reception unit can filter related business negotiation details based on the user's past project history. This allows for prioritized input of highly relevant business negotiation details by filtering the business negotiation details based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter the business negotiation details.

[0101] When entering business negotiation details, the reception unit selects an efficient input means according to the user's input method. For example, if the user selects voice input, the input is performed using voice recognition technology. The reception unit can also support keyboard input if the user selects text input. Furthermore, if the user selects image input, the reception unit can also perform input using image recognition technology. This enables efficient input of business negotiation details by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal input means.

[0102] The reception unit estimates the user's emotions and prioritizes the business negotiation details to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit postpones less important business negotiation details. Furthermore, if the user is relaxed, the reception unit can prioritize inputting more important business negotiation details. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting business negotiation details that require quick processing. This allows the business negotiation details to be input in a more appropriate order by prioritizing the business negotiation details according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0103] The dialogue unit estimates the user's emotions and adjusts the dialogue expression method based on the estimated user emotions. For example, if the user is nervous, the dialogue unit may use a calm tone. If the user is relaxed, the dialogue unit may also use a friendly tone. Furthermore, if the user is in a hurry, the dialogue unit may also use a quick and concise dialogue. This allows for more appropriate dialogue by adjusting the dialogue expression method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] During the dialogue, the dialogue unit adjusts the level of detail of the dialogue based on the importance of the business negotiation content. For example, a detailed dialogue is conducted for business negotiation content of high importance. The dialogue unit can also conduct a brief dialogue for business negotiation content of low importance. Furthermore, the dialogue unit can adjust the depth of the dialogue according to the importance. This enables efficient dialogue by adjusting the level of detail of the dialogue based on the importance of the business negotiation content. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input importance data of the business negotiation content to the generation AI and cause the generation AI to adjust the level of detail of the dialogue.

[0105] During the dialogue, the dialogue unit applies different dialogue algorithms depending on the category of the business negotiation. For example, in the case of a new product presentation, a dialogue algorithm that emphasizes the product's features and advantages is applied. In addition, in the case of contract negotiations, the dialogue unit can also apply a dialogue algorithm related to conditions and price. Furthermore, in the case of customer support, the dialogue unit can also apply a dialogue algorithm that focuses on problem solving. In this way, applying a dialogue algorithm depending on the category of the business negotiation enables more effective dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input business negotiation category data into the generation AI and have the generation AI apply the dialogue algorithm.

[0106] During a dialogue, the dialogue unit improves the accuracy of the dialogue based on the user's past dialogue results. For example, the dialogue unit improves the accuracy of the dialogue based on the content of the dialogues the user has had in the past. The dialogue unit can also analyze the user's past dialogue history and suggest an optimal dialogue method. Furthermore, the dialogue unit can also customize the content of the dialogue by referring to the user's past dialogue results. This enables more effective dialogue by improving the dialogue accuracy by referring to the user's past dialogue results. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's past dialogue data into the generation AI and have the generation AI improve the dialogue accuracy.

[0107] The dialogue unit estimates the user's emotions and adjusts the length of the dialogue based on the estimated user emotions. For example, if the user is nervous, the dialogue unit may hold a short dialogue. Furthermore, if the user is relaxed, the dialogue unit may hold a longer dialogue. Furthermore, if the user is in a hurry, the dialogue unit may quickly complete the dialogue. This allows for more appropriate dialogue by adjusting the length of the dialogue according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit accepts input of business negotiation details. The business negotiation details include product descriptions, price negotiations, contract terms, etc. The reception unit can accept business negotiation details by text input, voice input, image input, etc. Step 2: The dialogue unit conducts a dialogue based on the business negotiation details received by the reception unit. The dialogue unit uses a generation AI to dialogue with the user. For example, the generation AI may ask questions as a customer based on the business negotiation details entered by the user, and the user may respond to those questions. The dialogue unit can also use the generation AI to simulate a business negotiation. Step 3: The analysis unit analyzes the materials created by the user and suggests improvements. The analysis unit uses the generation AI to analyze the structure, design, and clarity of the content of the materials. For example, the generation AI analyzes presentation materials uploaded by the user and suggests improvements. The analysis unit can also use the generation AI to analyze the content of the materials and suggest specific improvements. Step 4: The evaluation department analyzes the results of the sales activities and evaluates their effectiveness based on the evaluation criteria. The evaluation department uses the generation AI to analyze the results of the sales activities. For example, the generation AI analyzes the success rate of sales negotiations and customer reactions, and evaluates the effectiveness of the sales activities. The evaluation department can also use the generation AI to review the sales activities and suggest areas for improvement for the next sales negotiation.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a 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.

[0148] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] 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.

[0173] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of business negotiation details; a dialogue unit that conducts dialogue based on the business negotiation content received by the reception unit; An analysis section that analyzes the materials created by users and provides suggestions for improvement; An evaluation unit that analyzes the results of sales activities and evaluates their effectiveness based on evaluation criteria. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of inputting the details of the business negotiation based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past sales history and select an efficient input method 2. The system of claim 1.

4. The reception unit Filtering opportunity entries based on the user's current projects and interests 2. The system of claim 1.

5. The reception unit When entering business details, select the most efficient input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Infer the user's emotions and prioritize the business content to be entered based on the estimated user emotions 2. The system of claim 1.

7. The reception unit When entering deal details, set criteria to prioritize the entry of relevant details by taking into account the user's geographic location information.

2. The system of claim 1.

8. The reception unit When entering business details, analyze the user's social media activity and enter relevant details 2. The system of claim 1.

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