System

Abstract

To provide a system according to an embodiment that efficiently selects a prospective customer and a commodity to be proposed in a business activity.SOLUTION: A system according to an embodiment comprises a collection unit, a learning unit, a selection unit, and a presentation unit. The collection unit collects the minutes of past business activities and information on the Internet. The learning unit learns the information collected by the collection unit. The selection unit selects a prospective customer and a commodity to be proposed on the basis of the information learned by the learning unit. The presentation unit presents the prospective customer and the commodity to be proposed selected by the selection unit.SELECTED DRAWING: Figure 1

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] With conventional technology, selecting potential customers and proposed products during sales activities was time-consuming and labor-intensive, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently select potential customers and proposed products in sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a selection unit, and a presentation unit. The collection unit collects minutes of past sales activities and information on the Internet. The learning unit learns the information collected by the collection unit. The selection unit selects potential customers and proposed products based on the information learned by the learning unit. The presentation unit presents the potential customers and proposed products selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select potential customers and proposed products in 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 activity efficiency system according to an embodiment of the present invention is a system for selecting potential customers and proposed products by collecting minutes of past sales activities and online information and learning from the collected information. This system selects potential customers and proposed products by collecting minutes of past sales activities and online information and learning from the collected information. First, a collection unit collects past sales activity minutes and online information. Next, a learning unit learns the collected information and analyzes past sales activity patterns and market trends. For example, the past sales activity minutes can be used to analyze which proposals were successful and which customers showed interest. Furthermore, current market trends and customer needs can be identified from online information. This allows for the selection of potential customers and proposed products. A selection unit selects potential customers and proposed products based on the information learned by the learning unit. For example, based on past sales activity patterns and market trends, it can select which customers are potential customers and which products are suitable for proposals. Finally, a presentation unit presents the selected potential customers and proposed products. This allows sales representatives to conduct sales activities efficiently. For example, it is possible to analyze information from potential customers' websites and social media to understand their needs and interests. It is also possible to estimate the user's emotions and adjust the timing of information collection. This reduces sales man-hours and improves efficiency. As a result, the sales activity efficiency system allows sales representatives to conduct sales activities more efficiently.

[0029] A sales activity efficiency improvement system according to an embodiment includes a collection unit, a learning unit, a selection unit, and a presentation unit. The collection unit collects minutes of past sales activities and information on the Internet. The minutes of past sales activities include, but are not limited to, meeting records, memos, and audio recordings. The information on the Internet includes, but is not limited to, news articles, blog posts, and social media posts. The collection unit collects information on the Internet using, for example, crawling technology. The collection unit can also digitize the minutes of past sales activities and collect them as text data. The learning unit learns from the information collected by the collection unit. Learning can be performed using, for example, but is not limited to, algorithms such as machine learning and deep learning. The learning unit analyzes, for example, the minutes of past sales activities to determine which proposals were successful and which customers showed interest. The learning unit can also identify current market trends and customer needs from information on the Internet. The selection unit selects potential customers and proposed products based on the information learned by the learning unit. The selection may be performed based on, for example, past sales activity patterns and market trends, but is not limited to, such examples. The selection unit selects which customers are potential customers and which products are suitable for proposal, for example, based on past sales activity patterns and market trends. The presentation unit presents the potential customers and proposed products selected by the selection unit. The presentation may be performed in a format optimized for the sales representative's device, for example, but is not limited to such examples. The presentation unit may analyze, for example, information on the potential customers' websites and social media to understand the customers' needs and interests. The presentation unit may also estimate the user's emotions and adjust the timing of information collection. As a result, the sales activity efficiency system according to the embodiment enables sales representatives to conduct sales activities efficiently.

[0030] The collection department collects minutes of past sales activities and online information. Examples of past sales activity minutes include, but are not limited to, meeting notes, memos, and audio recordings. These minutes contain detailed records of salesperson proposals, customer responses, and meeting progress, and serve as an important data source for analyzing the success and failure factors of sales activities. The collection department can digitize these minutes and collect them as text data. For example, audio recordings can be converted into text using voice recognition technology, and meeting notes and memos can be scanned and converted into text data using OCR technology. Examples of online information include, but are not limited to, news articles, blog posts, and social media posts. This information serves as an important data source for understanding current market trends and customer needs. The collection department collects online information using crawling technology. Crawling technology automatically crawls websites and collects necessary information, allowing for efficient collection of large amounts of data. The collection department centrally manages the collected data and can connect with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the learning and selection units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The learning unit learns the information collected by the collection unit. Learning can be performed using algorithms such as, but not limited to, machine learning and deep learning. Specifically, the learning unit analyzes minutes of past sales activities to determine which proposals were successful and which customers showed interest. For example, natural language processing technology can be used to analyze the text data of the minutes to extract commonalities between successful proposals and patterns of customer responses. Voice recognition technology can also be used to analyze audio recordings to understand customer emotions and reactions. Furthermore, the learning unit can also understand current market trends and customer needs from information on the internet. For example, news articles and blog posts can be analyzed to extract trends and customer interests in specific industries and markets. Social media posts can also be analyzed to understand real-time customer reactions and opinions. The learning unit integrates this information to obtain insights useful for improving the efficiency of sales activities. For example, the learning unit can learn what proposals are effective based on past success stories and then make proposals tailored to current market trends and customer needs. The learning unit can also continuously learn from new data to improve the accuracy of the model. This allows the learning unit to provide the knowledge necessary to improve the efficiency of sales activities and improve the performance of the entire system.

[0032] The selection unit selects potential customers and proposed products based on the information learned by the learning unit. The selection is performed, for example, based on past sales activity patterns and market trends, but is not limited to these examples. Specifically, the selection unit analyzes past sales activity patterns to identify potential customers. For example, it identifies customers with similar needs based on commonalities between past successful proposals. It also analyzes market trends to select products with high demand in the current market. For example, it analyzes news articles and blog posts to identify growing interest in specific products. The selection unit integrates this information to select optimal potential customers and proposed products. Furthermore, the selection unit can continuously evaluate the selection results and revise the selection criteria as needed. For example, it can evaluate the results of actual sales activities based on the selection results and improve the accuracy of the selection criteria. The selection unit can also share the selection results with other systems and departments to support the efficiency of sales activities. For example, it can provide the selection results to sales representatives and provide them with information to make optimal proposals. This allows the selection unit to select potential customers and proposed products necessary to improve the efficiency of sales activities, thereby improving the performance of the entire system.

[0033] The presentation unit presents the prospective customers and proposed products selected by the selection unit. The presentation may be, for example, in a format optimized for the sales representative's device, but is not limited to such an example. Specifically, the presentation unit may analyze information from the prospective customer's website or social media to understand the customer's needs and interests. For example, the presentation unit may analyze the customer's website to identify products and services in which the customer is interested. The presentation unit may also analyze social media posts to understand the customer's real-time reactions and opinions. Based on this information, the presentation unit provides the sales representative with information to make optimal proposals. For example, the presentation unit may present proposal content and timing tailored to the customer's needs. The presentation unit may also estimate the user's emotions and adjust the timing of information gathering. For example, by making proposals during times when the customer is busy, the success rate of the proposals can be improved. Furthermore, the presentation unit may continuously evaluate the presentation results and revise the presentation content as necessary. For example, the presentation unit may evaluate the results of actual sales activities based on the presentation results and improve the accuracy of the presentation content. The presentation unit may also share the presentation results with other systems or departments to help improve the efficiency of sales activities. This allows the presentation unit to provide information for making optimal proposals to sales representatives, thereby supporting the improvement of efficiency in sales activities.

[0034] The learning unit can learn from the collected information and analyze past sales activity patterns and market trends. For example, the learning unit can analyze what proposals were successful and what types of customers showed interest from minutes of past sales activities. For example, the learning unit can extract commonalities between successful proposals to improve the success rate of proposals. The learning unit can also grasp current market trends and customer needs from information on the Internet. For example, the learning unit can analyze news articles and blog posts to understand current market trends. This analysis of past sales activity patterns and market trends improves the accuracy of selecting potential customers and proposed products. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the collected information into AI and have the AI ​​analyze past sales activity patterns and market trends.

[0035] The collection unit can analyze information on the websites and social media of potential customers to understand their needs and interests. The collection unit, for example, uses crawling technology to analyze the websites of potential customers. For example, the collection unit analyzes a company's official website, blog, product pages, etc. to understand the customer's needs and interests. The collection unit can also analyze information on social media to understand the customer's needs and interests. For example, the collection unit analyzes Facebook (registered trademark) posts, X (formerly Twitter (registered trademark)) tweets, LinkedIn (registered trademark) updates, etc. to understand the customer's needs and interests. This understanding of the customer's needs and interests enables more appropriate proposals to be made. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information on the websites and social media of potential customers into AI and have the AI ​​understand the customer's needs and interests.

[0036] The collection unit can analyze the contents of minutes of past sales activities and filter information based on keywords and phrases. For example, the collection unit prioritizes collecting information containing keywords such as "success" and "conclusion" from minutes of past sales activities. For example, the collection unit prioritizes collecting information containing keywords such as "success" and "conclusion" from past minutes. The collection unit can also filter information containing keywords such as "complaint" and "problem" from past minutes to extract areas for improvement. For example, the collection unit filters information containing keywords such as "complaint" and "problem" from past minutes to extract areas for improvement. The collection unit can also collect information containing keywords such as "new product" and "new service" from past minutes and use it in the latest proposals. For example, the collection unit collects information containing keywords such as "new product" and "new service" from past minutes and use it in the latest proposals. In this way, important information can be efficiently collected by filtering information based on specific keywords and phrases. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection department can input minutes of past sales activities into the AI ​​and have it filter the information based on keywords and phrases.

[0037] The collection unit can collect information from the Internet in real time and reflect the latest market trends. The collection unit, for example, collects the latest market trends from news sites on the Internet and updates the information in real time. For example, the collection unit collects the latest market trends from news sites on the Internet and updates the information in real time. The collection unit can also collect trend information from social media to understand customer interests. For example, the collection unit collects trend information from social media to understand customer interests. The collection unit can also collect the latest technical information from industry-specific blogs and forums and reflect it in proposals. For example, the collection unit collects the latest technical information from industry-specific blogs and forums and reflects it in proposals. This improves the accuracy of proposals by reflecting the latest market trends. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input information from the Internet into AI and cause the AI ​​to collect information in real time and reflect market trends.

[0038] The collection unit can expand the scope of information collected to include competitor trends and industry news. For example, the collection unit collects information about new products and services from competitor websites. The collection unit can also collect the latest market trends and technology trends from industry news sites. For example, the collection unit collects the latest market trends and technology trends from industry news sites. The collection unit can also analyze competitors' social media posts to understand their marketing strategies and customer responses. For example, the collection unit analyzes competitors' social media posts to understand their marketing strategies and customer responses. This enables more comprehensive information collection by collecting competitor trends and industry news. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input information from competitors' websites and social media sites into AI and have the AI ​​collect and analyze the information.

[0039] The learning unit can analyze patterns of past sales activities and extract commonalities among successful proposals. For example, the learning unit extracts commonalities among proposals that led to successful deals from minutes of past sales activities. For example, the learning unit extracts commonalities among proposals that led to successful deals from minutes of past sales activities. The learning unit can also analyze keywords and phrases contained in successful proposals to find commonalities. For example, the learning unit analyzes keywords and phrases contained in successful proposals to find commonalities. The learning unit can also analyze customer attributes and market conditions of successful proposals to extract commonalities. For example, the learning unit analyzes customer attributes and market conditions of successful proposals to extract commonalities. By extracting commonalities among successful proposals, the success rate of proposals is improved. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input minutes of past sales activities into AI and have the AI ​​extract commonalities among successful proposals.

[0040] The learning unit can analyze market trends and build a model for predicting future trends. The learning unit, for example, analyzes past market data and builds a model for predicting future trends. For example, the learning unit analyzes past market data and builds a model for predicting future trends. The learning unit can also analyze information on the Internet and build a model for predicting future market trends. For example, the learning unit analyzes information on the Internet and builds a model for predicting future market trends. The learning unit can also analyze customer needs and interests and build a model for predicting future trends. For example, the learning unit analyzes customer needs and interests and builds a model for predicting future trends. This predicts future trends and improves the accuracy of proposals. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past market data and information on the Internet into AI and cause the AI ​​to predict future trends.

[0041] The learning unit can improve the accuracy of suggestions by including customer feedback and ratings in the learning data. For example, the learning unit includes customer feedback in the learning data to improve the accuracy of suggestions. For example, the learning unit includes customer feedback in the learning data to improve the accuracy of suggestions. The learning unit can also include customer ratings in the learning data to generate suggestions with a high success rate. For example, the learning unit includes customer ratings in the learning data to generate suggestions with a high success rate. The learning unit can also analyze customer feedback and ratings to find areas for improvement in the proposal. For example, the learning unit analyzes customer feedback and ratings to find areas for improvement in the proposal. In this way, the accuracy of suggestions is improved by including customer feedback and ratings. Some or all of the above-described processing in the learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit can input customer feedback and ratings into AI and cause the AI ​​to improve the accuracy of suggestions.

[0042] The learning department can share the analyzed information with other departments and utilize it in company-wide strategies. For example, the learning department shares the analyzed market trends with the marketing department and utilizes it in company-wide strategies. For example, the learning department shares the analyzed market trends with the marketing department and utilizes it in company-wide strategies. The learning department can also share the analyzed customer needs with the product development department and utilize it in the development of new products. For example, the learning department can share the analyzed customer needs with the product development department and utilize it in the development of new products. The learning department can also share the analyzed common points of successful proposals with the sales department and utilize them in sales strategies. For example, the learning department can share the analyzed common points of successful proposals with the sales department and utilize them in sales strategies. In this way, the analyzed information can be utilized in company-wide strategies, thereby improving the efficiency of the entire company. Some or all of the above-mentioned processing in the learning department can be performed using, for example, AI, or can be performed without using AI. For example, the learning department can input the analyzed information into AI and have the AI ​​share the information with other departments.

[0043] The selection unit can score prospective customers based on past success cases. The selection unit, for example, scores prospective customers based on past success cases. For example, the selection unit scores prospective customers based on past success cases. The selection unit can also score prospective customers based on customer attributes of successful proposals. For example, the selection unit scores prospective customers based on customer attributes of successful proposals. The selection unit can also score prospective customers based on market conditions of successful proposals. For example, the selection unit scores prospective customers based on market conditions of successful proposals. This enables efficient customer selection by scoring prospective customers based on past success cases. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input past success cases into AI and have the AI ​​perform scoring of prospective customers.

[0044] When selecting suggested products, the selection unit can select the optimal product by taking into consideration the customer's purchase history and interests. The selection unit, for example, selects the optimal suggested product based on the customer's purchase history. For example, the selection unit selects the optimal suggested product based on the customer's purchase history. The selection unit can also select the optimal suggested product based on the customer's interests. For example, the selection unit selects the optimal suggested product based on the customer's interests. The selection unit can also select the optimal suggested product by analyzing the customer's purchase history and interests. For example, the selection unit analyzes the customer's purchase history and interests and selects the optimal suggested product. This makes it possible to select the optimal suggested product by taking the customer's purchase history and interests into consideration. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the customer's purchase history and interests into AI and have the AI ​​select the optimal suggested product.

[0045] The selection unit can prioritize the selected potential customers and proposed products based on the schedules of the sales representatives. For example, the selection unit prioritizes potential customers based on the schedules of the sales representatives. The selection unit can also prioritize proposed products based on the schedules of the sales representatives. For example, the selection unit prioritizes proposed products based on the schedules of the sales representatives. The selection unit can also prioritize products by taking into account the schedules of the sales representatives and the needs of the potential customers. For example, the selection unit prioritizes products by taking into account the schedules of the sales representatives and the needs of the potential customers. This enables efficient sales activities by prioritizing products based on the schedules of the sales representatives. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the schedules of the sales representatives and the needs of the potential customers into AI and have the AI ​​perform the prioritization.

[0046] The selection unit can link the selected information with other sales tools. The selection unit, for example, links the selected prospective customer information to a CRM system. For example, the selection unit links the selected prospective customer information to a CRM system. The selection unit can also link the selected proposed product information to a sales support tool. For example, the selection unit links the selected proposed product information to a sales support tool. The selection unit can also link the selected information to a marketing automation tool. For example, the selection unit links the selected information to a marketing automation tool. This links the information to other sales tools, thereby improving the efficiency of sales activities. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the selected information into AI and have the AI ​​execute linking with other sales tools.

[0047] The presentation unit can display detailed information and past interactions with the prospective customer at the time of presentation. The presentation unit, for example, displays basic information (such as name and contact information) of the prospective customer. For example, the presentation unit displays basic information (such as name and contact information) of the prospective customer. The presentation unit can also display past interactions with the prospective customer (such as emails and phone calls). For example, the presentation unit displays past interactions with the prospective customer (such as emails and phone calls). The presentation unit can also display information about the prospective customer's interests and needs. For example, the presentation unit displays information about the prospective customer's interests and needs. This enables more appropriate suggestions to be made by displaying detailed information and past interactions with the prospective customer. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input detailed information and past interactions with the prospective customer into AI and have the AI ​​display the information.

[0048] The presentation unit can highlight the advantages and past success stories of the proposed product when presenting it. The presentation unit, for example, displays the main advantages of the proposed product in bullet points. For example, the presentation unit displays the main advantages of the proposed product in bullet points. The presentation unit can also specifically display past success stories of the proposed product. For example, the presentation unit specifically displays past success stories of the proposed product. The presentation unit can also display graphs and charts to visually highlight the advantages of the proposed product. For example, the presentation unit displays graphs and charts to visually highlight the advantages of the proposed product. This highlights the advantages and past success stories of the proposed product, thereby improving the success rate of the proposal. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the advantages and past success stories of the proposed product into AI and have the AI ​​display the information.

[0049] The presentation unit can optimize the presented information for the sales representative's device before displaying it. The presentation unit, for example, optimizes the information to fit the screen size of a smartphone before displaying it. For example, the presentation unit optimizes the information to fit the screen size of a smartphone before displaying it. The presentation unit can also optimize the information to fit the large screen of a tablet before displaying it. For example, the presentation unit optimizes the information to fit the large screen of a tablet before displaying it. The presentation unit can also optimize the information to fit the screen of a desktop PC before displaying it. For example, the presentation unit optimizes the information to fit the screen of a desktop PC before displaying it. This enables efficient information presentation by displaying information optimized for the sales representative's device. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input information from the sales representative's device into AI and have the AI ​​optimize the information.

[0050] The presentation unit can customize the information to be presented based on feedback from the sales representative. The presentation unit, for example, customizes the content of the information to be presented based on the feedback from the sales representative. For example, the presentation unit customizes the content of the information to be presented based on the feedback from the sales representative. The presentation unit can also customize the method of displaying the information to be presented based on the feedback from the sales representative. For example, the presentation unit customizes the method of displaying the information to be presented based on the feedback from the sales representative. The presentation unit can also customize the priority of the information to be presented based on the feedback from the sales representative. For example, the presentation unit customizes the priority of the information to be presented based on the feedback from the sales representative. This enables more appropriate information to be presented by customizing the information based on the feedback from the sales representative. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the feedback from the sales representative into AI and have the AI ​​customize the information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The sales activity efficiency improvement system can further include a purchase history analysis unit that analyzes customer purchase histories. The purchase history analysis unit collects past customer purchase data and analyzes which products and services were effective for which customers. For example, the purchase history analysis unit analyzes the types and frequency of products purchased by customers in the past and selects the next product to propose. The purchase history analysis unit can also analyze customer purchasing patterns and make proposals according to seasons and events. Furthermore, the purchase history analysis unit can combine customer purchase histories with market trends to predict future purchases. This makes it possible to make proposals based on customer purchase histories, improving the success rate of proposals.

[0053] The learning department can further collect customer feedback and reflect it in the learning data. For example, the learning department can collect how customers responded to proposals and analyze in more detail the commonalities of successful proposals. The learning department can also identify areas for improvement in proposals based on customer feedback and reflect these in future proposals. Furthermore, the learning department can analyze customer feedback and understand changes in customer needs and interests. In this way, by utilizing customer feedback, the accuracy of proposals can be improved and customer satisfaction can be increased.

[0054] The collection unit can further analyze customers' social media activities to understand their interests. For example, the collection unit can analyze what customers post on Facebook and X (formerly Twitter) to identify their interests. The collection unit can also collect information on online communities and forums in which customers participate to understand their areas of interest. Furthermore, the collection unit can analyze articles and comments shared by customers to understand their values ​​and preferences. This makes it possible to make more personalized proposals by utilizing customers' social media activities.

[0055] The collection unit can further analyze the customer's purchase history and understand the customer's purchasing patterns. For example, the collection unit collects data on products and services that the customer has purchased in the past and analyzes when and which products were purchased. The collection unit can also identify purchasing patterns according to seasons and events based on the customer's purchase history. Furthermore, the collection unit can combine the customer's purchase history with market trends to predict future purchases. This makes it possible to make proposals based on the customer's purchase history, improving the success rate of such proposals.

[0056] The learning department can further collect customer feedback and reflect it in the learning data. For example, the learning department can collect how customers responded to proposals and analyze in more detail the commonalities of successful proposals. The learning department can also identify areas for improvement in proposals based on customer feedback and reflect these in future proposals. Furthermore, the learning department can analyze customer feedback and understand changes in customer needs and interests. In this way, by utilizing customer feedback, the accuracy of proposals can be improved and customer satisfaction can be increased.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection department collects minutes of past sales activities and information on the Internet. Minutes of past sales activities include meeting records, memos, and audio recordings, while information on the Internet includes news articles, blog posts, and social media posts. The collection department uses crawling technology to collect information on the Internet, and digitizes and collects minutes of past sales activities as text data. Step 2: The learning department studies the information collected by the collection department. Learning is carried out using algorithms such as machine learning and deep learning. The learning department analyzes successful proposals and customer interests from minutes of past sales activities, and understands current market trends and customer needs from information on the Internet. Step 3: The selection unit selects potential customers and proposed products based on the information learned by the learning unit. Selection is based on past sales activity patterns and market trends, and determines which customers are potential customers and which products are suitable for proposals. Step 4: The presentation unit presents the potential customers and proposed products selected by the selection unit. The presentation is made in a format optimized for the sales representative's device, and information from the potential customers' websites and social media is analyzed to understand their needs and interests. It can also estimate the user's emotions and adjust the timing of information collection.

[0059] (Example 2) A sales activity efficiency system according to an embodiment of the present invention is a system for selecting potential customers and proposed products by collecting minutes of past sales activities and online information and learning from the collected information. This system selects potential customers and proposed products by collecting minutes of past sales activities and online information and learning from the collected information. First, a collection unit collects past sales activity minutes and online information. Next, a learning unit learns the collected information and analyzes past sales activity patterns and market trends. For example, the past sales activity minutes can be used to analyze which proposals were successful and which customers showed interest. Furthermore, current market trends and customer needs can be identified from online information. This allows for the selection of potential customers and proposed products. A selection unit selects potential customers and proposed products based on the information learned by the learning unit. For example, based on past sales activity patterns and market trends, it can select which customers are potential customers and which products are suitable for proposals. Finally, a presentation unit presents the selected potential customers and proposed products. This allows sales representatives to conduct sales activities efficiently. For example, it is possible to analyze information from potential customers' websites and social media to understand their needs and interests. It is also possible to estimate the user's emotions and adjust the timing of information collection. This reduces sales man-hours and improves efficiency. As a result, the sales activity efficiency system allows sales representatives to conduct sales activities more efficiently.

[0060] A sales activity efficiency improvement system according to an embodiment includes a collection unit, a learning unit, a selection unit, and a presentation unit. The collection unit collects minutes of past sales activities and information on the Internet. The minutes of past sales activities include, but are not limited to, meeting records, memos, and audio recordings. The information on the Internet includes, but is not limited to, news articles, blog posts, and social media posts. The collection unit collects information on the Internet using, for example, crawling technology. The collection unit can also digitize the minutes of past sales activities and collect them as text data. The learning unit learns from the information collected by the collection unit. Learning can be performed using, for example, but is not limited to, algorithms such as machine learning and deep learning. The learning unit analyzes, for example, the minutes of past sales activities to determine which proposals were successful and which customers showed interest. The learning unit can also identify current market trends and customer needs from information on the Internet. The selection unit selects potential customers and proposed products based on the information learned by the learning unit. The selection may be performed based on, for example, past sales activity patterns and market trends, but is not limited to, such examples. The selection unit selects which customers are potential customers and which products are suitable for proposal, for example, based on past sales activity patterns and market trends. The presentation unit presents the potential customers and proposed products selected by the selection unit. The presentation may be performed in a format optimized for the sales representative's device, for example, but is not limited to such examples. The presentation unit may analyze, for example, information on the potential customers' websites and social media to understand the customers' needs and interests. The presentation unit may also estimate the user's emotions and adjust the timing of information collection. As a result, the sales activity efficiency system according to the embodiment enables sales representatives to conduct sales activities efficiently.

[0061] The collection department collects minutes of past sales activities and online information. Examples of past sales activity minutes include, but are not limited to, meeting notes, memos, and audio recordings. These minutes contain detailed records of salesperson proposals, customer responses, and meeting progress, and serve as an important data source for analyzing the success and failure factors of sales activities. The collection department can digitize these minutes and collect them as text data. For example, audio recordings can be converted into text using voice recognition technology, and meeting notes and memos can be scanned and converted into text data using OCR technology. Examples of online information include, but are not limited to, news articles, blog posts, and social media posts. This information serves as an important data source for understanding current market trends and customer needs. The collection department collects online information using crawling technology. Crawling technology automatically crawls websites and collects necessary information, allowing for efficient collection of large amounts of data. The collection department centrally manages the collected data and can connect with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the learning and selection units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0062] The learning unit learns the information collected by the collection unit. Learning can be performed using algorithms such as, but not limited to, machine learning and deep learning. Specifically, the learning unit analyzes minutes of past sales activities to determine which proposals were successful and which customers showed interest. For example, natural language processing technology can be used to analyze the text data of the minutes to extract commonalities between successful proposals and patterns of customer responses. Voice recognition technology can also be used to analyze audio recordings to understand customer emotions and reactions. Furthermore, the learning unit can also understand current market trends and customer needs from information on the internet. For example, news articles and blog posts can be analyzed to extract trends and customer interests in specific industries and markets. Social media posts can also be analyzed to understand real-time customer reactions and opinions. The learning unit integrates this information to obtain insights useful for improving the efficiency of sales activities. For example, the learning unit can learn what proposals are effective based on past success stories and then make proposals tailored to current market trends and customer needs. The learning unit can also continuously learn from new data to improve the accuracy of the model. This allows the learning unit to provide the knowledge necessary to improve the efficiency of sales activities and improve the performance of the entire system.

[0063] The selection unit selects potential customers and proposed products based on the information learned by the learning unit. The selection is performed, for example, based on past sales activity patterns and market trends, but is not limited to these examples. Specifically, the selection unit analyzes past sales activity patterns to identify potential customers. For example, it identifies customers with similar needs based on commonalities between past successful proposals. It also analyzes market trends to select products with high demand in the current market. For example, it analyzes news articles and blog posts to identify growing interest in specific products. The selection unit integrates this information to select optimal potential customers and proposed products. Furthermore, the selection unit can continuously evaluate the selection results and revise the selection criteria as needed. For example, it can evaluate the results of actual sales activities based on the selection results and improve the accuracy of the selection criteria. The selection unit can also share the selection results with other systems and departments to support the efficiency of sales activities. For example, it can provide the selection results to sales representatives and provide them with information to make optimal proposals. This allows the selection unit to select potential customers and proposed products necessary to improve the efficiency of sales activities, thereby improving the performance of the entire system.

[0064] The presentation unit presents the prospective customers and proposed products selected by the selection unit. The presentation may be, for example, in a format optimized for the sales representative's device, but is not limited to such an example. Specifically, the presentation unit may analyze information from the prospective customer's website or social media to understand the customer's needs and interests. For example, the presentation unit may analyze the customer's website to identify products and services in which the customer is interested. The presentation unit may also analyze social media posts to understand the customer's real-time reactions and opinions. Based on this information, the presentation unit provides the sales representative with information to make optimal proposals. For example, the presentation unit may present proposal content and timing tailored to the customer's needs. The presentation unit may also estimate the user's emotions and adjust the timing of information gathering. For example, by making proposals during times when the customer is busy, the success rate of the proposals can be improved. Furthermore, the presentation unit may continuously evaluate the presentation results and revise the presentation content as necessary. For example, the presentation unit may evaluate the results of actual sales activities based on the presentation results and improve the accuracy of the presentation content. The presentation unit may also share the presentation results with other systems or departments to help improve the efficiency of sales activities. This allows the presentation unit to provide information for making optimal proposals to sales representatives, thereby supporting the improvement of efficiency in sales activities.

[0065] The learning unit can learn from the collected information and analyze past sales activity patterns and market trends. For example, the learning unit can analyze what proposals were successful and what types of customers showed interest from minutes of past sales activities. For example, the learning unit can extract commonalities between successful proposals to improve the success rate of proposals. The learning unit can also grasp current market trends and customer needs from information on the Internet. For example, the learning unit can analyze news articles and blog posts to understand current market trends. This analysis of past sales activity patterns and market trends improves the accuracy of selecting potential customers and proposed products. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the collected information into AI and have the AI ​​analyze past sales activity patterns and market trends.

[0066] The collection unit can analyze information on the websites and social media of potential customers to understand their needs and interests. The collection unit, for example, uses crawling technology to analyze the websites of potential customers. For example, the collection unit analyzes a company's official website, blog, product pages, etc. to understand the customer's needs and interests. The collection unit can also analyze information on social media to understand the customer's needs and interests. For example, the collection unit analyzes Facebook posts, tweets on X (formerly Twitter), LinkedIn updates, etc. to understand the customer's needs and interests. This understanding of the customer's needs and interests enables more appropriate proposals to be made. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information on the websites and social media of potential customers into AI and have the AI ​​understand the customer's needs and interests.

[0067] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This enables efficient information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0068] The collection unit can analyze the contents of minutes of past sales activities and filter information based on keywords and phrases. For example, the collection unit prioritizes collecting information containing keywords such as "success" and "conclusion" from minutes of past sales activities. For example, the collection unit prioritizes collecting information containing keywords such as "success" and "conclusion" from past minutes. The collection unit can also filter information containing keywords such as "complaint" and "problem" from past minutes to extract areas for improvement. For example, the collection unit filters information containing keywords such as "complaint" and "problem" from past minutes to extract areas for improvement. The collection unit can also collect information containing keywords such as "new product" and "new service" from past minutes and use it in the latest proposals. For example, the collection unit collects information containing keywords such as "new product" and "new service" from past minutes and use it in the latest proposals. In this way, important information can be efficiently collected by filtering information based on specific keywords and phrases. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection department can input minutes of past sales activities into the AI ​​and have it filter the information based on keywords and phrases.

[0069] The collection unit can collect information from the Internet in real time and reflect the latest market trends. The collection unit, for example, collects the latest market trends from news sites on the Internet and updates the information in real time. For example, the collection unit collects the latest market trends from news sites on the Internet and updates the information in real time. The collection unit can also collect trend information from social media to understand customer interests. For example, the collection unit collects trend information from social media to understand customer interests. The collection unit can also collect the latest technical information from industry-specific blogs and forums and reflect it in proposals. For example, the collection unit collects the latest technical information from industry-specific blogs and forums and reflects it in proposals. This improves the accuracy of proposals by reflecting the latest market trends. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input information from the Internet into AI and cause the AI ​​to collect information in real time and reflect market trends.

[0070] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and determines the priority of information. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of information. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations and determines the priority of information. This enables efficient information collection by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0071] The collection unit can expand the scope of information collected to include competitor trends and industry news. For example, the collection unit collects information about new products and services from competitor websites. The collection unit can also collect the latest market trends and technology trends from industry news sites. For example, the collection unit collects the latest market trends and technology trends from industry news sites. The collection unit can also analyze competitors' social media posts to understand their marketing strategies and customer responses. For example, the collection unit analyzes competitors' social media posts to understand their marketing strategies and customer responses. This enables more comprehensive information collection by collecting competitor trends and industry news. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input information from competitors' websites and social media sites into AI and have the AI ​​collect and analyze the information.

[0072] The learning unit can estimate a user's emotions and select training data based on the estimated user emotions. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on changes in facial expressions and selects training data. The learning unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the learning unit analyzes the tone and speed of the voice, calculates an emotion score, and selects training data. The learning unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on heart rate fluctuations and selects training data. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The learning unit can analyze patterns of past sales activities and extract commonalities among successful proposals. For example, the learning unit extracts commonalities among proposals that led to successful deals from minutes of past sales activities. For example, the learning unit extracts commonalities among proposals that led to successful deals from minutes of past sales activities. The learning unit can also analyze keywords and phrases contained in successful proposals to find commonalities. For example, the learning unit analyzes keywords and phrases contained in successful proposals to find commonalities. The learning unit can also analyze customer attributes and market conditions of successful proposals to extract commonalities. For example, the learning unit analyzes customer attributes and market conditions of successful proposals to extract commonalities. By extracting commonalities among successful proposals, the success rate of proposals is improved. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input minutes of past sales activities into AI and have the AI ​​extract commonalities among successful proposals.

[0074] The learning unit can analyze market trends and build a model for predicting future trends. The learning unit, for example, analyzes past market data and builds a model for predicting future trends. For example, the learning unit analyzes past market data and builds a model for predicting future trends. The learning unit can also analyze information on the Internet and build a model for predicting future market trends. For example, the learning unit analyzes information on the Internet and builds a model for predicting future market trends. The learning unit can also analyze customer needs and interests and build a model for predicting future trends. For example, the learning unit analyzes customer needs and interests and builds a model for predicting future trends. This predicts future trends and improves the accuracy of proposals. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past market data and information on the Internet into AI and cause the AI ​​to predict future trends.

[0075] The learning unit can estimate the user's emotions and adjust the parameters of the learning algorithm based on the estimated user emotions. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on changes in facial expressions and adjusts the parameters of the learning algorithm. The learning unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the learning unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the parameters of the learning algorithm. The learning unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the learning unit calculates an emotion score based on heart rate fluctuations and adjusts the parameters of the learning algorithm. This enables efficient learning by adjusting the parameters of the learning algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] The learning unit can improve the accuracy of suggestions by including customer feedback and ratings in the learning data. For example, the learning unit includes customer feedback in the learning data to improve the accuracy of suggestions. For example, the learning unit includes customer feedback in the learning data to improve the accuracy of suggestions. The learning unit can also include customer ratings in the learning data to generate suggestions with a high success rate. For example, the learning unit includes customer ratings in the learning data to generate suggestions with a high success rate. The learning unit can also analyze customer feedback and ratings to find areas for improvement in the proposal. For example, the learning unit analyzes customer feedback and ratings to find areas for improvement in the proposal. In this way, the accuracy of suggestions is improved by including customer feedback and ratings. Some or all of the above-described processing in the learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit can input customer feedback and ratings into AI and cause the AI ​​to improve the accuracy of suggestions.

[0077] The learning department can share the analyzed information with other departments and utilize it in company-wide strategies. For example, the learning department shares the analyzed market trends with the marketing department and utilizes it in company-wide strategies. For example, the learning department shares the analyzed market trends with the marketing department and utilizes it in company-wide strategies. The learning department can also share the analyzed customer needs with the product development department and utilize it in the development of new products. For example, the learning department can share the analyzed customer needs with the product development department and utilize it in the development of new products. The learning department can also share the analyzed common points of successful proposals with the sales department and utilize them in sales strategies. For example, the learning department can share the analyzed common points of successful proposals with the sales department and utilize them in sales strategies. In this way, the analyzed information can be utilized in company-wide strategies, thereby improving the efficiency of the entire company. Some or all of the above-mentioned processing in the learning department can be performed using, for example, AI, or can be performed without using AI. For example, the learning department can input the analyzed information into AI and have the AI ​​share the information with other departments.

[0078] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated user emotions. For example, the selection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on changes in facial expressions and adjusts the selection criteria. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the selection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the selection criteria. The selection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on heart rate fluctuations and adjusts the selection criteria. This enables efficient selection by adjusting the selection criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0079] The selection unit can score prospective customers based on past success cases. The selection unit, for example, scores prospective customers based on past success cases. For example, the selection unit scores prospective customers based on past success cases. The selection unit can also score prospective customers based on customer attributes of successful proposals. For example, the selection unit scores prospective customers based on customer attributes of successful proposals. The selection unit can also score prospective customers based on market conditions of successful proposals. For example, the selection unit scores prospective customers based on market conditions of successful proposals. This enables efficient customer selection by scoring prospective customers based on past success cases. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input past success cases into AI and have the AI ​​perform scoring of prospective customers.

[0080] When selecting suggested products, the selection unit can select the optimal product by taking into consideration the customer's purchase history and interests. The selection unit, for example, selects the optimal suggested product based on the customer's purchase history. For example, the selection unit selects the optimal suggested product based on the customer's purchase history. The selection unit can also select the optimal suggested product based on the customer's interests. For example, the selection unit selects the optimal suggested product based on the customer's interests. The selection unit can also select the optimal suggested product by analyzing the customer's purchase history and interests. For example, the selection unit analyzes the customer's purchase history and interests and selects the optimal suggested product. This makes it possible to select the optimal suggested product by taking the customer's purchase history and interests into consideration. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the customer's purchase history and interests into AI and have the AI ​​select the optimal suggested product.

[0081] The selection unit can estimate the user's emotion and adjust the display method of the selection results based on the estimated user emotion. For example, the selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on changes in facial expression and adjusts the display method of the selection results. The selection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the selection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the selection results. The selection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the selection unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the selection results. This enables efficient information presentation by adjusting the display method of the selection results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The selection unit can prioritize the selected potential customers and proposed products based on the schedules of the sales representatives. For example, the selection unit prioritizes potential customers based on the schedules of the sales representatives. The selection unit can also prioritize proposed products based on the schedules of the sales representatives. For example, the selection unit prioritizes proposed products based on the schedules of the sales representatives. The selection unit can also prioritize products by taking into account the schedules of the sales representatives and the needs of the potential customers. For example, the selection unit prioritizes products by taking into account the schedules of the sales representatives and the needs of the potential customers. This enables efficient sales activities by prioritizing products based on the schedules of the sales representatives. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the schedules of the sales representatives and the needs of the potential customers into AI and have the AI ​​perform the prioritization.

[0083] The selection unit can link the selected information with other sales tools. The selection unit, for example, links the selected prospective customer information to a CRM system. For example, the selection unit links the selected prospective customer information to a CRM system. The selection unit can also link the selected proposed product information to a sales support tool. For example, the selection unit links the selected proposed product information to a sales support tool. The selection unit can also link the selected information to a marketing automation tool. For example, the selection unit links the selected information to a marketing automation tool. This links the information to other sales tools, thereby improving the efficiency of sales activities. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the selected information into AI and have the AI ​​execute linking with other sales tools.

[0084] The presentation unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, the presentation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on changes in facial expression and adjusts the presentation method. The presentation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the presentation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the presentation method. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on heart rate fluctuations and adjusts the presentation method. This enables efficient information presentation by adjusting the presentation method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The presentation unit can display detailed information and past interactions with the prospective customer at the time of presentation. The presentation unit, for example, displays basic information (such as name and contact information) of the prospective customer. For example, the presentation unit displays basic information (such as name and contact information) of the prospective customer. The presentation unit can also display past interactions with the prospective customer (such as emails and phone calls). For example, the presentation unit displays past interactions with the prospective customer (such as emails and phone calls). The presentation unit can also display information about the prospective customer's interests and needs. For example, the presentation unit displays information about the prospective customer's interests and needs. This enables more appropriate suggestions to be made by displaying detailed information and past interactions with the prospective customer. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input detailed information and past interactions with the prospective customer into AI and have the AI ​​display the information.

[0086] The presentation unit can highlight the advantages and past success stories of the proposed product when presenting it. The presentation unit, for example, displays the main advantages of the proposed product in bullet points. For example, the presentation unit displays the main advantages of the proposed product in bullet points. The presentation unit can also specifically display past success stories of the proposed product. For example, the presentation unit specifically displays past success stories of the proposed product. The presentation unit can also display graphs and charts to visually highlight the advantages of the proposed product. For example, the presentation unit displays graphs and charts to visually highlight the advantages of the proposed product. This highlights the advantages and past success stories of the proposed product, thereby improving the success rate of the proposal. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the advantages and past success stories of the proposed product into AI and have the AI ​​display the information.

[0087] The presentation unit can estimate the user's emotions and prioritize the content to be presented based on the estimated user emotions. For example, the presentation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on changes in facial expression and prioritizes the content to be presented. The presentation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the presentation unit analyzes the tone and speed of the voice, calculates an emotion score, and prioritizes the content to be presented. The presentation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the presentation unit calculates an emotion score based on heart rate fluctuations and prioritizes the content to be presented. This enables efficient information presentation by prioritizing the content to be presented according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] The presentation unit can optimize the presented information for the sales representative's device before displaying it. The presentation unit, for example, optimizes the information to fit the screen size of a smartphone before displaying it. For example, the presentation unit optimizes the information to fit the screen size of a smartphone before displaying it. The presentation unit can also optimize the information to fit the large screen of a tablet before displaying it. For example, the presentation unit optimizes the information to fit the large screen of a tablet before displaying it. The presentation unit can also optimize the information to fit the screen of a desktop PC before displaying it. For example, the presentation unit optimizes the information to fit the screen of a desktop PC before displaying it. This enables efficient information presentation by displaying information optimized for the sales representative's device. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input information from the sales representative's device into AI and have the AI ​​optimize the information.

[0089] The presentation unit can customize the information to be presented based on feedback from the sales representative. The presentation unit, for example, customizes the content of the information to be presented based on the feedback from the sales representative. For example, the presentation unit customizes the content of the information to be presented based on the feedback from the sales representative. The presentation unit can also customize the method of displaying the information to be presented based on the feedback from the sales representative. For example, the presentation unit customizes the method of displaying the information to be presented based on the feedback from the sales representative. The presentation unit can also customize the priority of the information to be presented based on the feedback from the sales representative. For example, the presentation unit customizes the priority of the information to be presented based on the feedback from the sales representative. This enables more appropriate information to be presented by customizing the information based on the feedback from the sales representative. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the feedback from the sales representative into AI and have the AI ​​customize the information.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The sales activity efficiency improvement system can further include a purchase history analysis unit that analyzes customer purchase histories. The purchase history analysis unit collects past customer purchase data and analyzes which products and services were effective for which customers. For example, the purchase history analysis unit analyzes the types and frequency of products purchased by customers in the past and selects the next product to propose. The purchase history analysis unit can also analyze customer purchasing patterns and make proposals according to seasons and events. Furthermore, the purchase history analysis unit can combine customer purchase histories with market trends to predict future purchases. This makes it possible to make proposals based on customer purchase histories, improving the success rate of proposals.

[0092] The learning department can further collect customer feedback and reflect it in the learning data. For example, the learning department can collect how customers responded to proposals and analyze in more detail the commonalities of successful proposals. The learning department can also identify areas for improvement in proposals based on customer feedback and reflect these in future proposals. Furthermore, the learning department can analyze customer feedback and understand changes in customer needs and interests. In this way, by utilizing customer feedback, the accuracy of proposals can be improved and customer satisfaction can be increased.

[0093] The collection unit can further analyze customers' social media activities to understand their interests. For example, the collection unit can analyze what customers post on Facebook and X (formerly Twitter) to identify their interests. The collection unit can also collect information on online communities and forums in which customers participate to understand their areas of interest. Furthermore, the collection unit can analyze articles and comments shared by customers to understand their values ​​and preferences. This makes it possible to make more personalized proposals by utilizing customers' social media activities.

[0094] The collection unit can further estimate the customer's emotions and determine the priority of information collection based on the estimated emotions. For example, the collection unit captures the customer's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Based on the estimated emotions, the collection unit prioritizes collecting information that the customer is interested in. The collection unit can also record the customer's voice and estimate the emotions using voice analysis technology. Furthermore, the collection unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This makes it possible to collect information according to the customer's emotions and make more effective suggestions.

[0095] The collection unit can further analyze the customer's purchase history and understand the customer's purchasing patterns. For example, the collection unit collects data on products and services that the customer has purchased in the past and analyzes when and which products were purchased. The collection unit can also identify purchasing patterns according to seasons and events based on the customer's purchase history. Furthermore, the collection unit can combine the customer's purchase history with market trends to predict future purchases. This makes it possible to make proposals based on the customer's purchase history, improving the success rate of such proposals.

[0096] The collection unit can further estimate the customer's emotions and adjust the timing of information collection based on the estimated emotions. For example, the collection unit captures the customer's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Based on the estimated emotions, the collection unit collects information when the customer is relaxed. The collection unit can also record the customer's voice and estimate the emotions using voice analysis technology. Furthermore, the collection unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This makes it possible to collect information according to the customer's emotions and make more effective suggestions.

[0097] The learning unit can further estimate the customer's emotions and select learning data based on the estimated emotions. For example, the learning unit captures the customer's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Based on the estimated emotions, the learning unit prioritizes learning data in which the customer expressed positive emotions. The learning unit can also record the customer's voice and estimate emotions using voice analysis technology. Furthermore, the learning unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. This makes it possible to select learning data according to the customer's emotions, allowing for more effective suggestions.

[0098] The learning department can further collect customer feedback and reflect it in the learning data. For example, the learning department can collect how customers responded to proposals and analyze in more detail the commonalities of successful proposals. The learning department can also identify areas for improvement in proposals based on customer feedback and reflect these in future proposals. Furthermore, the learning department can analyze customer feedback and understand changes in customer needs and interests. In this way, by utilizing customer feedback, the accuracy of proposals can be improved and customer satisfaction can be increased.

[0099] The selection unit can further estimate the customer's emotions and adjust the selection criteria based on the estimated emotions. For example, the selection unit captures the customer's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Based on the estimated emotions, the selection unit makes more proactive suggestions if the customer is showing positive emotions. The selection unit can also record the customer's voice and estimate the emotions using voice analysis technology. Furthermore, the selection unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This makes it possible to adjust the selection criteria according to the customer's emotions, resulting in more effective suggestions.

[0100] The presentation unit can further estimate the customer's emotions and prioritize the content to be presented based on the estimated emotions. For example, the presentation unit captures the customer's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Based on the estimated emotion, the presentation unit prioritizes the presentation of information that the customer is interested in. The presentation unit can also record the customer's voice and estimate the emotion using voice analysis technology. Furthermore, the presentation unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to present information according to the customer's emotions, resulting in more effective suggestions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection department collects minutes of past sales activities and information on the Internet. Minutes of past sales activities include meeting records, memos, and audio recordings, while information on the Internet includes news articles, blog posts, and social media posts. The collection department uses crawling technology to collect information on the Internet, and digitizes and collects minutes of past sales activities as text data. Step 2: The learning department studies the information collected by the collection department. Learning is carried out using algorithms such as machine learning and deep learning. The learning department analyzes successful proposals and customer interests from minutes of past sales activities, and understands current market trends and customer needs from information on the Internet. Step 3: The selection unit selects potential customers and proposed products based on the information learned by the learning unit. Selection is based on past sales activity patterns and market trends, and determines which customers are potential customers and which products are suitable for proposals. Step 4: The presentation unit presents the potential customers and proposed products selected by the selection unit. The presentation is made in a format optimized for the sales representative's device, and information from the potential customers' websites and social media is analyzed to understand their needs and interests. It can also estimate the user's emotions and adjust the timing of information collection.

[0103] 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.

[0104] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0105] 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.

[0106] For example, the collection unit can collect minutes of past sales activities and information on the Internet using the camera 42 and microphone 38B of the smart device 14. The learning unit, which is realized, for example, by the specific processing unit 290 of the data processing device 12, learns from the collected information and analyzes past sales activity patterns and market trends. The selection unit, which is realized, for example, by the specific processing unit 290 of the data processing device 12, selects potential customers and proposed products based on the learned information. The presentation unit, which is realized, for example, by the control unit 46A of the smart device 14, presents the selected potential customers and proposed products to the sales representative. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] For example, the collection unit can collect minutes of past sales activities and information on the Internet using the camera 42 and microphone 238 of the smart glasses 214. The learning unit, which is realized, for example, by the specific processing unit 290 of the data processing device 12, learns the collected information and analyzes past sales activity patterns and market trends. The selection unit, which is realized, for example, by the specific processing unit 290 of the data processing device 12, selects potential customers and proposed products based on the learned information. The presentation unit, which is realized, for example, by the control unit 46A of the smart glasses 214, presents the selected potential customers and proposed products to the sales representative. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] For example, the collection unit can collect minutes of past sales activities and information on the Internet using the camera 42 and microphone 238 of the headset terminal 314. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns from the collected information and analyzes past sales activity patterns and market trends. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects potential customers and proposed products based on the learned information. The presentation unit is realized, for example, by the control unit 46A of the headset terminal 314, and presents the selected potential customers and proposed products to the sales representative. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] For example, the collection unit can collect minutes of past sales activities and information on the Internet using the camera 42 and microphone 238 of the robot 414. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns from the collected information and analyzes past sales activity patterns and market trends. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects potential customers and proposed products based on the learned information. The presentation unit is realized, for example, by the control unit 46A of the robot 414, and presents the selected potential customers and proposed products to the sales representative. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Appendix 1) A collection department that collects minutes of past sales activities and information on the Internet; a learning unit that learns the information collected by the collection unit; a selection unit that selects potential customers and proposed products based on the information learned by the learning unit; a presentation unit that presents the potential customers and proposed products selected by the selection unit. A system characterized by: (Appendix 2) The learning unit Learn from the collected information and analyze past sales activity patterns and market trends 2. The system of claim 1. (Appendix 3) The collecting unit Analyzing potential customers' websites and social media information to understand their needs and interests 2. The system of claim 1. (Appendix 4) The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions 2. The system of claim 1. (Appendix 5) The collecting unit Analyze the content of past sales notes and filter information based on keywords and phrases 2. The system of claim 1. (Appendix 6) The collecting unit Collect information from the internet in real time to reflect the latest market trends 2. The system of claim 1. (Appendix 7) The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. 2. The system of claim 1. (Appendix 8) The collecting unit Expanding the scope of information collected to include competitor activity and industry news 2. The system of claim 1. (Appendix 9) The learning unit Estimate the user's emotions and select training data based on the estimated user emotions. 2. The system of claim 1. (Appendix 10) The learning unit Analyze patterns of past sales activities and extract commonalities among successful proposals 2. The system of claim 1. (Appendix 11) The learning unit Analyze market trends and build models to predict future trends 2. The system of claim 1. (Appendix 12) The learning unit Estimate the user's emotions and adjust the parameters of the learning algorithm based on the estimated user emotions. 2. The system of claim 1. (Appendix 13) The learning unit Improve the accuracy of suggestions by including customer feedback and ratings in the training data 2. The system of claim 1. (Appendix 14) The learning unit Share the analyzed information with other departments and use it in company-wide strategies 2. The system of claim 1. (Appendix 15) The selection unit Estimate user emotions and adjust selection criteria based on the estimated user emotions 2. The system of claim 1. (Appendix 16) The selection unit Scoring leads based on past success 2. The system of claim 1. (Appendix 17) The selection unit When selecting proposed products, select the most suitable products by taking into consideration the customer's purchasing history and interests. 2. The system of claim 1. (Appendix 18) The selection unit Inferring user emotions and adjusting the display method of selection results based on the estimated user emotions 2. The system of claim 1. (Appendix 19) The selection unit Prioritize selected prospects and proposed products based on sales representatives' schedules 2. The system of claim 1. (Appendix 20) The selection unit Link selected information with other sales tools 2. The system of claim 1. (Appendix 21) The presentation unit Estimate the user's emotions and adjust the presentation method based on the estimated user emotions 2. The system of claim 1. (Appendix 22) The presentation unit When presented, display lead details and past interactions 2. The system of claim 1. (Appendix 23) The presentation unit When presenting, highlight the advantages of the proposed product and past success stories 2. The system of claim 1. (Appendix 24) The presentation unit Estimate the user's emotions and prioritize the content to be presented based on the estimated user emotions. 2. The system of claim 1. (Appendix 25) The presentation unit Display information optimized for the salesperson's device 2. The system of claim 1. (Appendix 26) The presentation unit Customize the information presented based on seller feedback 2. The system of claim 1. [Explanation of symbols]

[0175] 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 collection department that collects minutes of past sales activities and information on the Internet; a learning unit that learns the information collected by the collection unit; A selection unit that selects potential customers and proposed products based on the information learned by the learning unit; A presentation unit that presents the potential customers and proposed products selected by the selection unit. A system characterized by:

2. The learning unit is Learn from the collected information and analyze past sales activity patterns and market trends 2. The system of claim 1 .

3. The collecting unit includes: Analyze website and social media information of potential customers to understand their needs and interests 2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the content of past sales notes and filter information based on keywords and phrases 2. The system of claim 1 .

6. The collecting unit includes: Collect information from the Internet in real time to reflect the latest market trends 2. The system of claim 1 .

7. The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions.

2. The system of claim 1 .

8. The collecting unit includes: Expand the scope of information collected to include competitor activity and industry news 2. The system of claim 1 .

9. The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions.

2. The system of claim 1 .

10. The learning unit is Analyze patterns of past sales activities and extract commonalities among successful proposals 2. The system of claim 1 .

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