System

Abstract

An object of a system according to an embodiment is to enable a salesperson to efficiently create a hypothesis proposal material.SOLUTION: A system according to an embodiment includes an information collection unit, an information analysis unit, and a material generation unit. The information collecting unit collects information of an in-house system or a customer's website. The information analysis unit analyzes the information collected by the information collection unit and arranges the information in a form that is easy for the salesperson to use. The material generation unit automatically generates a hypothesis proposal material based on the information organized by the information analysis 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] Conventional technology requires salespeople to spend a lot of time and effort creating materials, making it difficult to conduct efficient sales activities.

[0005] The system according to the embodiment aims to enable salespeople to efficiently create hypothesis proposal materials. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information analysis unit, and a material generation unit. The information collection unit collects information from an internal system or a customer's website. The information analysis unit analyzes the information collected by the information collection unit and organizes it in a format that is easy for salespeople to use. The material generation unit automatically generates hypothesis proposal materials based on the information organized by the information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable salespeople to efficiently create hypothesis proposal materials. [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) The document creation support system according to an embodiment of the present invention is a system that significantly reduces the time and effort required for salespeople to create documents and allows them to efficiently create hypothesis proposal documents. This system uses a generation AI to integrate and systematically manage information from internal systems, customer websites, and other sources, allowing salespeople to quickly obtain the information they need and support them in creating documents. As a result, the document creation support system allows salespeople to confidently prepare hypothesis proposal documents even if a customer visit is scheduled for the same day, which ultimately leads to the creation of new business opportunities.

[0029] A material creation support system according to an embodiment includes an information collection unit, an information analysis unit, and a material generation unit. The information collection unit collects information from an internal system or a customer's website. For example, the information collection unit acquires past transaction history and customer purchase history from an internal database. The information collection unit can also collect the latest news and product information from customer websites. The information collection unit can also acquire information from external systems using API integration. For example, the information collection unit acquires customer information from an internal CRM system and transaction data from an ERP system. The information collection unit collects the latest news and product information from customer websites using scraping technology. API integration enables real-time acquisition of information from external data sources. The information analysis unit analyzes the information collected by the information collection unit and organizes it in a format that is easy for salespeople to use. For example, the information analysis unit organizes the collected information by category and displays it in a dashboard format. The information analysis unit can also use text mining technology to analyze customer industry trends and competitor information. The information analysis unit can also improve the quality of the collected information using data cleansing technology. For example, the information analysis department organizes collected information by category and displays it in dashboard format. Text mining technology is used to analyze customer industry trends and information on competitors. Data cleansing technology is used to improve the quality of collected information. The document generation department automatically generates hypothesis proposal materials based on the information organized by the information analysis department. For example, the document generation department creates materials including proposal details and solutions based on the customer's needs and issues. The document generation department can also create materials according to a format specified by the salesperson and automatically insert necessary graphs and charts. Furthermore, the document generation department can also customize the content and design of the materials using generation AI. For example, the document generation department creates materials including proposal details and solutions based on the customer's needs and issues. The document generation department creates materials according to a format specified by the salesperson and automatically inserts necessary graphs and charts. The document generation department can also customize the content and design of the materials using generation AI.As a result, the document creation support system according to the embodiment significantly reduces the time and man-hours required for salespeople to create documents, enabling them to efficiently create hypothesis proposal documents. For example, even if a salesperson is scheduled to visit a customer on the same day, they can quickly gather the necessary information and create proposal documents. Furthermore, by making proposals based on the customer's needs and issues, the possibility of generating new business increases. Furthermore, customizing documents and utilizing feedback enables more effective proposals.

[0030] The information gathering unit collects the latest posts and comments from customers' social media accounts, allowing them to reflect their real-time interests. For example, the information gathering unit uses a generation AI to monitor customers' social media accounts and collect the latest posts and comments. For example, it obtains content posted by customers on Twitter (registered trademark) or LinkedIn in real time to understand their interests. The information gathering unit also analyzes data collected from customers' social media accounts to identify topics and trends of interest to customers. For example, it extracts frequently mentioned keywords and hashtags to reflect the customer's interests. The information gathering unit also updates the customer's interests in real time based on the collected social media data, allowing salespeople to create proposal materials based on the latest information. For example, it reflects topics that customers have recently become interested in in the proposal materials. This allows proposal materials to be created that reflect the customer's latest interests.

[0031] The information gathering department can analyze customers' past inquiry history and support tickets to extract their potential needs and problems. For example, the information gathering department uses a generation AI to analyze customers' past inquiry history and identify frequently occurring problems and questions. For example, if there are many inquiries about a particular product, it will make suggestions related to that product. The information gathering department also collects support ticket data and extracts problems and areas for improvement that customers are facing. For example, if there are many complaints about a particular function, it will make suggestions to improve that function. The information gathering department can also predict customers' potential needs based on inquiry history and support ticket data and reflect them in proposal materials. For example, it can suggest functions and services that customers may need in the future. This allows the department to understand customers' potential needs and problems and reflect them in proposal materials.

[0032] The information gathering department can collect best practices and success stories from different industries and reflect them in proposal materials for customers. For example, the generative AI can collect best practices from different industries and reflect them in proposal materials for customers. For example, it can incorporate marketing strategies that have been successful in other industries into proposals. The information gathering department can also collect success stories and introduce them as specific examples in proposal materials for customers. For example, it can make proposals based on success stories from other industries. The information gathering department can also analyze data from different industries to provide new perspectives in proposal materials for customers. For example, it can reflect trends and technologies from other industries in proposals. This allows best practices and success stories from different industries to be reflected in proposal materials.

[0033] The information gathering department can collect information from the websites and news articles of the customer's competitors and conduct competitive analysis. For example, the information gathering department uses a generative AI to monitor the websites of the customer's competitors and collect the latest product information and news. For example, it makes proposals based on new product announcements by competitors. The information gathering department also collects news articles about competitors and conducts competitive analysis. For example, it analyzes the market trends and strategies of competitors and reflects these in proposal materials. The information gathering department also makes proposals that emphasize competitive advantages to the customer based on the collected information about competitors. For example, it makes proposals that exploit the weaknesses of competitors. In this way, it is possible to collect information about the customer's competitors and conduct competitive analysis.

[0034] The information analysis unit can predict customer purchasing patterns and trends based on the collected information and provide them to salespeople. For example, the information analysis unit uses a generative AI to analyze a customer's purchasing history and identify purchasing patterns. For example, it predicts purchasing trends related to specific seasons or events. The information analysis unit also predicts future purchasing trends for customers based on the collected data and provides them to salespeople. For example, it predicts the next purchasing cycle and reflects this in proposal materials. The information analysis unit also analyzes customer purchasing patterns and predicts demand for specific products and services. For example, it suggests products that the customer is likely to purchase next. In this way, customer purchasing patterns and trends can be predicted and provided to salespeople.

[0035] The information analysis department can automatically extract keywords and topics specific to the customer's industry and use them in the creation of materials. For example, the information analysis department uses generative AI to analyze data related to the customer's industry and extract specific keywords and topics. For example, the latest industry trends and technical terms are reflected in the proposal materials. The information analysis department also automatically extracts keywords specific to the customer's industry and strengthens the content of the proposal materials. For example, industry terminology and important topics are included. The information analysis department also identifies important topics related to the customer's industry based on the collected data and reflects them in the proposal materials. For example, the latest industry news and research results are introduced. This allows keywords and topics specific to the customer's industry to be extracted and used in the creation of materials.

[0036] The information analysis department can predict future customer needs based on collected information and reflect them in proposal materials. For example, the information analysis department uses generative AI to analyze a customer's past purchase history and inquiry history to predict future needs. For example, it can suggest products and services that are likely to be needed next. The information analysis department can also predict future customer needs based on collected data and reflect them in proposal materials. For example, it can suggest functions and services that the customer is likely to need in the future. The information analysis department can also analyze the customer's industry and market trends to predict future needs. For example, it can make proposals based on the latest industry trends. This makes it possible to predict future customer needs and reflect them in proposal materials.

[0037] The information analysis department can analyze the client's industry trends based on the collected information and provide the results to salespeople. For example, the information analysis department uses generative AI to analyze data related to the client's industry and identify industry trends. For example, the latest industry trends and technological trends are reflected in proposal materials. The information analysis department also analyzes the client's industry trends based on the collected data and provides the results to salespeople. For example, it analyzes the industry's competitive situation and market needs and reflects them in proposal materials. The information analysis department also identifies important topics related to the client's industry and reflects them in proposal materials. For example, it introduces the latest industry news and research results. This allows the client's industry trends to be analyzed and provided to salespeople.

[0038] The document generation department customizes proposal materials based on past customer feedback, enabling more effective proposals to be made. For example, the document generation department uses a generation AI to analyze past customer feedback and customize proposal materials. For example, it may emphasize proposal content that has been well received by customers in the past. The document generation department also adjusts the content of the proposal materials based on customer feedback data. For example, it may make proposals that improve upon problems that customers have pointed out in the past. The document generation department also customizes the design and format of proposal materials based on the collected feedback data. For example, it may adopt a layout and color usage that suits the customer's preferences. This allows proposal materials to be customized based on past customer feedback, enabling more effective proposals to be made.

[0039] The document generation unit can automatically apply designs and formats specific to the customer's industry to create proposal documents. For example, the generation AI automatically applies design templates related to the customer's industry to create proposal documents. For example, industry-standard formats and designs are used. The document generation unit also reflects design elements specific to the customer's industry in the proposal documents. For example, documents are created using industry logos and colors. The generation AI also automatically applies industry-specific formats to maintain consistency in the proposal documents. For example, a standard report format for the industry is used. This makes it possible to automatically apply designs and formats specific to the customer's industry to create proposal documents.

[0040] The document generation unit can automatically generate proposal materials that incorporate best practices from different industries. For example, the generation AI in the document generation unit collects best practices from different industries and automatically generates proposal materials based on them. For example, strategies that have been successful in other industries are incorporated into proposals. The document generation unit also automatically generates proposal materials based on successful cases from different industries. For example, proposals are made that refer to successful cases from other industries. The generation AI in the document generation unit also analyzes data from different industries and automatically generates proposal materials that provide new perspectives. For example, trends and technologies from other industries are reflected in proposals. This makes it possible to automatically generate proposal materials that incorporate best practices from different industries.

[0041] The document generation unit can create proposal materials based on success stories of the customer's competitors. For example, the generation AI of the document generation unit collects success stories of the customer's competitors and creates proposal materials based on them. For example, a proposal is made that references the success stories of competitors. The document generation unit also creates proposal materials for customers based on the success stories of competitors. For example, the material generation unit analyzes the success factors of competitors and reflects them in proposals. The document generation unit also analyzes data of competitors using the generation AI to create proposal materials that emphasize their competitive advantage over the customer. For example, a proposal is made that targets the weaknesses of competitors. In this way, proposal materials can be created based on the success stories of the customer's competitors.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The information gathering department can predict a customer's future needs based on their past purchase history and inquiry history. For example, if there are many inquiries about a particular product, it will make suggestions related to that product. The information gathering department also collects support ticket data and extracts the problems and areas for improvement that customers are facing. For example, if there is a lot of dissatisfaction with a particular function, it will make suggestions to improve that function. The information gathering department can also predict a customer's potential needs based on inquiry history and support ticket data and reflect them in proposal materials. For example, it can suggest functions and services that the customer may need in the future. This allows the department to understand the customer's potential needs and problems and reflect them in proposal materials.

[0044] The information analysis unit can predict customer purchasing patterns and trends based on the collected information and provide them to salesmen. For example, the generative AI analyzes a customer's purchasing history and identifies purchasing patterns. For example, it predicts purchasing trends related to specific seasons or events. The information analysis unit also predicts future purchasing trends for customers based on the collected data and provides them to salesmen. For example, it predicts the next purchasing cycle and reflects this in proposal materials. The information analysis unit also analyzes customer purchasing patterns and predicts demand for specific products and services. For example, it suggests products that the customer is likely to purchase next. In this way, customer purchasing patterns and trends can be predicted and provided to salesmen.

[0045] The information gathering department can collect best practices and success stories from different industries and reflect them in proposal materials for customers. For example, the generative AI can collect best practices from different industries and reflect them in proposal materials for customers. For example, it can incorporate marketing strategies that have been successful in other industries into proposals. The information gathering department can also collect success stories and introduce them as specific examples in proposal materials for customers. For example, it can make proposals based on success stories from other industries. The information gathering department can also analyze data from different industries to provide new perspectives in proposal materials for customers. For example, it can reflect trends and technologies from other industries in proposals. This allows best practices and success stories from other industries to be reflected in proposal materials.

[0046] The information gathering department can collect information from the websites and news articles of the customer's competitors and conduct competitive analysis. For example, the generative AI monitors the websites of the customer's competitors and collects the latest product information and news. For example, it makes proposals based on competitors' new product announcements. The information gathering department also collects news articles about competitors and conducts competitive analysis. For example, it analyzes the competitors' market trends and strategies and reflects them in proposal materials. The information gathering department also makes proposals that emphasize competitive advantages to the customer based on the collected information about competitors. For example, it makes proposals that exploit the competitors' weaknesses. In this way, it is possible to collect information about the customer's competitors and conduct competitive analysis.

[0047] The information analysis department can automatically extract keywords and topics specific to the customer's industry and use them in the creation of materials. For example, the generative AI analyzes data related to the customer's industry and extracts specific keywords and topics. For example, the latest industry trends and technical terms can be reflected in the proposal materials. The information analysis department also automatically extracts keywords specific to the customer's industry and strengthens the content of the proposal materials. For example, it can include industry terminology and important topics. The information analysis department can also identify important topics related to the customer's industry based on the collected data and reflect them in the proposal materials. For example, it can introduce the latest industry news and research results. This allows the extraction of keywords and topics specific to the customer's industry and use them in the creation of materials.

[0048] The document generation department customizes proposal materials based on past customer feedback, enabling more effective proposals. For example, the generation AI analyzes past customer feedback and customizes proposal materials. For example, it emphasizes proposal content that has been well received by customers in the past. The document generation department also adjusts the content of proposal materials based on customer feedback data. For example, it makes proposals that improve upon problems that customers have pointed out in the past. The document generation department also customizes the design and format of proposal materials based on the collected feedback data. For example, it adopts a layout and color usage that suits the customer's preferences. This allows proposal materials to be customized based on past customer feedback, enabling more effective proposals to be made.

[0049] The document generation unit can automatically apply designs and formats specific to the customer's industry to create proposal documents. For example, the generation AI automatically applies design templates related to the customer's industry to create proposal documents. For example, it uses industry-standard formats and designs. The document generation unit also reflects design elements specific to the customer's industry in the proposal documents. For example, it creates documents using industry logos and colors. The document generation unit also automatically applies industry-specific formats to maintain consistency in the proposal documents. For example, it uses industry-standard report formats. This makes it possible to automatically apply designs and formats specific to the customer's industry to create proposal documents.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information collection department collects information from internal systems or customer websites. For example, the information collection department obtains past transaction history and customer purchase history from an internal database. It can also collect the latest news and product information from customer websites. It can also obtain information from external systems using API integration. For example, the information collection department obtains customer information from an internal CRM system and transaction data from an ERP system. It uses scraping technology to collect the latest news and product information from customer websites. Using API integration, it is possible to obtain information in real time from external data sources. Step 2: The information analysis department analyzes the information collected by the information collection department and organizes it in a format that is easy for salespeople to use. For example, the information analysis department organizes the collected information by category and displays it in dashboard format. It can also use text mining technology to analyze customer industry trends and information on competitors. It can also use data cleansing technology to improve the quality of the collected information. Step 3: The document generation unit automatically generates hypothesis proposal materials based on the information organized by the information analysis unit. For example, the document generation unit creates materials that include proposal content and solutions based on the customer's needs and issues. It can also create materials according to the format specified by the salesperson and automatically insert necessary graphs and charts. Furthermore, the content and design of the materials can be customized using generation AI.

[0052] (Example 2) The document creation support system according to an embodiment of the present invention is a system that significantly reduces the time and effort required for salespeople to create documents and allows them to efficiently create hypothesis proposal documents. This system uses a generation AI to integrate and systematically manage information from internal systems, customer websites, and other sources, allowing salespeople to quickly obtain the information they need and support them in creating documents. As a result, the document creation support system allows salespeople to confidently prepare hypothesis proposal documents even if a customer visit is scheduled for the same day, which ultimately leads to the creation of new business opportunities.

[0053] A material creation support system according to an embodiment includes an information collection unit, an information analysis unit, and a material generation unit. The information collection unit collects information from an internal system or a customer's website. For example, the information collection unit acquires past transaction history and customer purchase history from an internal database. The information collection unit can also collect the latest news and product information from customer websites. The information collection unit can also acquire information from external systems using API integration. For example, the information collection unit acquires customer information from an internal CRM system and transaction data from an ERP system. The information collection unit collects the latest news and product information from customer websites using scraping technology. API integration enables real-time acquisition of information from external data sources. The information analysis unit analyzes the information collected by the information collection unit and organizes it in a format that is easy for salespeople to use. For example, the information analysis unit organizes the collected information by category and displays it in a dashboard format. The information analysis unit can also use text mining technology to analyze customer industry trends and competitor information. The information analysis unit can also improve the quality of the collected information using data cleansing technology. For example, the information analysis department organizes collected information by category and displays it in dashboard format. Text mining technology is used to analyze customer industry trends and information on competitors. Data cleansing technology is used to improve the quality of collected information. The document generation department automatically generates hypothesis proposal materials based on the information organized by the information analysis department. For example, the document generation department creates materials including proposal details and solutions based on the customer's needs and issues. The document generation department can also create materials according to a format specified by the salesperson and automatically insert necessary graphs and charts. Furthermore, the document generation department can also customize the content and design of the materials using generation AI. For example, the document generation department creates materials including proposal details and solutions based on the customer's needs and issues. The document generation department creates materials according to a format specified by the salesperson and automatically inserts necessary graphs and charts. The document generation department can also customize the content and design of the materials using generation AI.As a result, the document creation support system according to the embodiment significantly reduces the time and man-hours required for salespeople to create documents, enabling them to efficiently create hypothesis proposal documents. For example, even if a salesperson is scheduled to visit a customer on the same day, they can quickly gather the necessary information and create proposal documents. Furthermore, by making proposals based on the customer's needs and issues, the possibility of generating new business increases. Furthermore, customizing documents and utilizing feedback enables more effective proposals.

[0054] The information gathering unit collects the latest posts and comments from customers' social media accounts, allowing them to reflect their real-time interests. For example, the information gathering unit uses a generation AI to monitor customers' social media accounts and collect the latest posts and comments. For example, it obtains content posted by customers on Twitter (registered trademark) or LinkedIn in real time to understand their interests. The information gathering unit also analyzes data collected from customers' social media accounts to identify topics and trends of interest to customers. For example, it extracts frequently mentioned keywords and hashtags to reflect the customer's interests. The information gathering unit also updates the customer's interests in real time based on the collected social media data, allowing salespeople to create proposal materials based on the latest information. For example, it reflects topics that customers have recently become interested in in the proposal materials. This allows proposal materials to be created that reflect the customer's latest interests.

[0055] The information gathering department can analyze customers' past inquiry history and support tickets to extract their potential needs and problems. For example, the information gathering department uses a generation AI to analyze customers' past inquiry history and identify frequently occurring problems and questions. For example, if there are many inquiries about a particular product, it will make suggestions related to that product. The information gathering department also collects support ticket data and extracts problems and areas for improvement that customers are facing. For example, if there are many complaints about a particular function, it will make suggestions to improve that function. The information gathering department can also predict customers' potential needs based on inquiry history and support ticket data and reflect them in proposal materials. For example, it can suggest functions and services that customers may need in the future. This allows the department to understand customers' potential needs and problems and reflect them in proposal materials.

[0056] The information gathering unit can use the emotion estimation function to estimate customer emotions from customer websites and social media posts, and filter information based on those emotions. For example, the information gathering unit uses a generation AI to analyze customer websites and social media posts, and then estimates customer emotions using the emotion estimation function. For example, it prioritizes collecting posts with strong positive emotions. The information gathering unit also uses the emotion estimation function to calculate an emotion score from the content of customer posts and filter out posts with strong negative emotions. For example, it excludes posts of anger or dissatisfaction. The information gathering unit also selects information to be reflected in proposal materials based on customer emotion data. For example, it includes topics with strong positive emotions in the proposal materials. This makes it possible to collect information based on customer emotions and reflect it in proposal materials.

[0057] The information gathering department can collect best practices and success stories from different industries and reflect them in proposal materials for customers. For example, the generative AI can collect best practices from different industries and reflect them in proposal materials for customers. For example, it can incorporate marketing strategies that have been successful in other industries into proposals. The information gathering department can also collect success stories and introduce them as specific examples in proposal materials for customers. For example, it can make proposals based on success stories from other industries. The information gathering department can also analyze data from different industries to provide new perspectives in proposal materials for customers. For example, it can reflect trends and technologies from other industries in proposals. This allows best practices and success stories from different industries to be reflected in proposal materials.

[0058] The information gathering department can collect information from the websites and news articles of the customer's competitors and conduct competitive analysis. For example, the information gathering department uses a generative AI to monitor the websites of the customer's competitors and collect the latest product information and news. For example, it makes proposals based on new product announcements by competitors. The information gathering department also collects news articles about competitors and conducts competitive analysis. For example, it analyzes the market trends and strategies of competitors and reflects these in proposal materials. The information gathering department also makes proposals that emphasize competitive advantages to the customer based on the collected information about competitors. For example, it makes proposals that exploit the weaknesses of competitors. In this way, it is possible to collect information about the customer's competitors and conduct competitive analysis.

[0059] The information gathering unit can use the emotion estimation function to estimate customer emotions from customer websites and social media posts, and filter information based on those emotions. For example, the information gathering unit uses a generation AI to analyze customer websites and social media posts, and then estimates customer emotions using the emotion estimation function. For example, it prioritizes collecting posts with strong positive emotions. The information gathering unit also uses the emotion estimation function to calculate an emotion score from the content of customer posts and filter out posts with strong negative emotions. For example, it excludes posts of anger or dissatisfaction. The information gathering unit also selects information to be reflected in proposal materials based on customer emotion data. For example, it includes topics with strong positive emotions in the proposal materials. This makes it possible to collect information based on customer emotions and reflect it in proposal materials.

[0060] The information analysis unit can predict customer purchasing patterns and trends based on the collected information and provide them to salespeople. For example, the information analysis unit uses a generative AI to analyze a customer's purchasing history and identify purchasing patterns. For example, it predicts purchasing trends related to specific seasons or events. The information analysis unit also predicts future purchasing trends for customers based on the collected data and provides them to salespeople. For example, it predicts the next purchasing cycle and reflects this in proposal materials. The information analysis unit also analyzes customer purchasing patterns and predicts demand for specific products and services. For example, it suggests products that the customer is likely to purchase next. In this way, customer purchasing patterns and trends can be predicted and provided to salespeople.

[0061] The information analysis department can automatically extract keywords and topics specific to the customer's industry and use them in the creation of materials. For example, the information analysis department uses generative AI to analyze data related to the customer's industry and extract specific keywords and topics. For example, the latest industry trends and technical terms are reflected in the proposal materials. The information analysis department also automatically extracts keywords specific to the customer's industry and strengthens the content of the proposal materials. For example, industry terminology and important topics are included. The information analysis department also identifies important topics related to the customer's industry based on the collected data and reflects them in the proposal materials. For example, the latest industry news and research results are introduced. This allows keywords and topics specific to the customer's industry to be extracted and used in the creation of materials.

[0062] The information analysis unit can extract data related to customer emotions from the information collected using the emotion estimation function and provide it to salespeople. For example, the information analysis unit analyzes information collected by the generative AI and uses the emotion estimation function to extract data related to customer emotions. For example, it provides data with a strong positive emotion as a priority. The information analysis unit also uses the emotion estimation function to calculate a customer emotion score and filter out data with a strong negative emotion. For example, it excludes data of anger and dissatisfaction. The information analysis unit also selects information to be reflected in proposal materials based on the customer emotion data. For example, it includes topics with a strong positive emotion in the proposal materials. This allows data related to customer emotions to be extracted and provided to salespeople.

[0063] The information analysis department can predict future customer needs based on collected information and reflect them in proposal materials. For example, the information analysis department uses generative AI to analyze a customer's past purchase history and inquiry history to predict future needs. For example, it can suggest products and services that are likely to be needed next. The information analysis department can also predict future customer needs based on collected data and reflect them in proposal materials. For example, it can suggest functions and services that the customer is likely to need in the future. The information analysis department can also analyze the customer's industry and market trends to predict future needs. For example, it can make proposals based on the latest industry trends. This makes it possible to predict future customer needs and reflect them in proposal materials.

[0064] The information analysis department can analyze the client's industry trends based on the collected information and provide the results to salespeople. For example, the information analysis department uses generative AI to analyze data related to the client's industry and identify industry trends. For example, the latest industry trends and technological trends are reflected in proposal materials. The information analysis department also analyzes the client's industry trends based on the collected data and provides the results to salespeople. For example, it analyzes the industry's competitive situation and market needs and reflects them in proposal materials. The information analysis department also identifies important topics related to the client's industry and reflects them in proposal materials. For example, it introduces the latest industry news and research results. This allows the client's industry trends to be analyzed and provided to salespeople.

[0065] The information analysis unit can extract data related to customer emotions from the information collected using the emotion estimation function and provide it to salespeople. For example, the information analysis unit analyzes information collected by the generative AI and uses the emotion estimation function to extract data related to customer emotions. For example, it provides data with a strong positive emotion as a priority. The information analysis unit also uses the emotion estimation function to calculate a customer emotion score and filter out data with a strong negative emotion. For example, it excludes data of anger and dissatisfaction. The information analysis unit also selects information to be reflected in proposal materials based on the customer emotion data. For example, it includes topics with a strong positive emotion in the proposal materials. This allows data related to customer emotions to be extracted and provided to salespeople.

[0066] The document generation department customizes proposal materials based on past customer feedback, enabling more effective proposals to be made. For example, the document generation department uses a generation AI to analyze past customer feedback and customize proposal materials. For example, it may emphasize proposal content that has been well received by customers in the past. The document generation department also adjusts the content of the proposal materials based on customer feedback data. For example, it may make proposals that improve upon problems that customers have pointed out in the past. The document generation department also customizes the design and format of proposal materials based on the collected feedback data. For example, it may adopt a layout and color usage that suits the customer's preferences. This allows proposal materials to be customized based on past customer feedback, enabling more effective proposals to be made.

[0067] The document generation unit can automatically apply designs and formats specific to the customer's industry to create proposal documents. For example, the generation AI automatically applies design templates related to the customer's industry to create proposal documents. For example, industry-standard formats and designs are used. The document generation unit also reflects design elements specific to the customer's industry in the proposal documents. For example, documents are created using industry logos and colors. The generation AI also automatically applies industry-specific formats to maintain consistency in the proposal documents. For example, a standard report format for the industry is used. This makes it possible to automatically apply designs and formats specific to the customer's industry to create proposal documents.

[0068] The material generation unit can use the emotion estimation function to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest. For example, the material generation unit uses a generation AI to analyze the customer's emotional data and automatically generate proposal content that elicits positive emotions. For example, it makes proposals that make the customer feel happy or excited. The material generation unit also uses the emotion estimation function to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest. For example, it emphasizes topics that are likely to interest the customer. The material generation unit also adjusts the content of the proposal material based on the customer's emotional data and creates materials that attract the customer's interest. For example, it makes proposals that focus on topics that the customer has positive emotions about. This makes it possible to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest.

[0069] The document generation unit can automatically generate proposal materials that incorporate best practices from different industries. For example, the generation AI in the document generation unit collects best practices from different industries and automatically generates proposal materials based on them. For example, strategies that have been successful in other industries are incorporated into proposals. The document generation unit also automatically generates proposal materials based on successful cases from different industries. For example, proposals are made that refer to successful cases from other industries. The generation AI in the document generation unit also analyzes data from different industries and automatically generates proposal materials that provide new perspectives. For example, trends and technologies from other industries are reflected in proposals. This makes it possible to automatically generate proposal materials that incorporate best practices from different industries.

[0070] The document generation unit can create proposal materials based on success stories of the customer's competitors. For example, the generation AI of the document generation unit collects success stories of the customer's competitors and creates proposal materials based on them. For example, a proposal is made that references the success stories of competitors. The document generation unit also creates proposal materials for customers based on the success stories of competitors. For example, the material generation unit analyzes the success factors of competitors and reflects them in proposals. The document generation unit also analyzes data of competitors using the generation AI to create proposal materials that emphasize their competitive advantage over the customer. For example, a proposal is made that targets the weaknesses of competitors. In this way, proposal materials can be created based on the success stories of the customer's competitors.

[0071] The material generation unit can use the emotion estimation function to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest. For example, the material generation unit uses a generation AI to analyze the customer's emotional data and automatically generate proposal content that elicits positive emotions. For example, it makes proposals that make the customer feel happy or excited. The material generation unit also uses the emotion estimation function to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest. For example, it emphasizes topics that are likely to interest the customer. The material generation unit also adjusts the content of the proposal material based on the customer's emotional data and creates materials that attract the customer's interest. For example, it makes proposals that focus on topics that the customer has positive emotions about. This makes it possible to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The information gathering department can predict a customer's future needs based on their past purchase history and inquiry history. For example, if there are many inquiries about a particular product, it will make suggestions related to that product. The information gathering department also collects support ticket data and extracts the problems and areas for improvement that customers are facing. For example, if there is a lot of dissatisfaction with a particular function, it will make suggestions to improve that function. The information gathering department can also predict a customer's potential needs based on inquiry history and support ticket data and reflect them in proposal materials. For example, it can suggest functions and services that the customer may need in the future. This allows the department to understand the customer's potential needs and problems and reflect them in proposal materials.

[0074] The information analysis unit can predict customer purchasing patterns and trends based on the collected information and provide them to salesmen. For example, the generative AI analyzes a customer's purchasing history and identifies purchasing patterns. For example, it predicts purchasing trends related to specific seasons or events. The information analysis unit also predicts future purchasing trends for customers based on the collected data and provides them to salesmen. For example, it predicts the next purchasing cycle and reflects this in proposal materials. The information analysis unit also analyzes customer purchasing patterns and predicts demand for specific products and services. For example, it suggests products that the customer is likely to purchase next. In this way, customer purchasing patterns and trends can be predicted and provided to salesmen.

[0075] The information gathering department can collect best practices and success stories from different industries and reflect them in proposal materials for customers. For example, the generative AI can collect best practices from different industries and reflect them in proposal materials for customers. For example, it can incorporate marketing strategies that have been successful in other industries into proposals. The information gathering department can also collect success stories and introduce them as specific examples in proposal materials for customers. For example, it can make proposals based on success stories from other industries. The information gathering department can also analyze data from different industries to provide new perspectives in proposal materials for customers. For example, it can reflect trends and technologies from other industries in proposals. This allows best practices and success stories from other industries to be reflected in proposal materials.

[0076] The information gathering department can collect information from the websites and news articles of the customer's competitors and conduct competitive analysis. For example, the generative AI monitors the websites of the customer's competitors and collects the latest product information and news. For example, it makes proposals based on competitors' new product announcements. The information gathering department also collects news articles about competitors and conducts competitive analysis. For example, it analyzes the competitors' market trends and strategies and reflects them in proposal materials. The information gathering department also makes proposals that emphasize competitive advantages to the customer based on the collected information about competitors. For example, it makes proposals that exploit the competitors' weaknesses. In this way, it is possible to collect information about the customer's competitors and conduct competitive analysis.

[0077] The information analysis department can automatically extract keywords and topics specific to the customer's industry and use them in the creation of materials. For example, the generative AI analyzes data related to the customer's industry and extracts specific keywords and topics. For example, the latest industry trends and technical terms can be reflected in the proposal materials. The information analysis department also automatically extracts keywords specific to the customer's industry and strengthens the content of the proposal materials. For example, it can include industry terminology and important topics. The information analysis department can also identify important topics related to the customer's industry based on the collected data and reflect them in the proposal materials. For example, it can introduce the latest industry news and research results. This allows the extraction of keywords and topics specific to the customer's industry and use them in the creation of materials.

[0078] The information gathering unit can use the emotion estimation function to infer customer emotions from customer websites and social media posts, and filter information based on those emotions. For example, the generation AI analyzes customer websites and social media posts and uses the emotion estimation function to infer customer emotions. For example, it prioritizes collecting posts with strong positive emotions. The information gathering unit also uses the emotion estimation function to calculate an emotion score from the content of customer posts and filter out posts with strong negative emotions. For example, it excludes posts expressing anger or dissatisfaction. The information gathering unit also selects information to be reflected in proposal materials based on customer emotion data. For example, it includes topics with strong positive emotions in proposal materials. This makes it possible to collect information based on customer emotions and reflect it in proposal materials.

[0079] The information analysis unit can extract data related to customer emotions from the information collected using the emotion estimation function and provide it to salespeople. For example, it analyzes the information collected by the generative AI and uses the emotion estimation function to extract data related to customer emotions. For example, it provides data with a strong positive emotion as a priority. The information analysis unit also uses the emotion estimation function to calculate a customer emotion score and filter out data with a strong negative emotion. For example, it excludes data of anger and dissatisfaction. The information analysis unit also selects information to be reflected in proposal materials based on the customer emotion data. For example, it includes topics with a strong positive emotion in the proposal materials. This allows data related to customer emotions to be extracted and provided to salespeople.

[0080] The material generation unit can use the emotion estimation function to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest. For example, the generation AI analyzes the customer's emotion data and automatically generates proposal content that elicits positive emotions. For example, it makes proposals that make the customer feel happy or excited. The material generation unit also uses the emotion estimation function to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest. For example, it emphasizes topics that are likely to interest the customer. The material generation unit also adjusts the content of the proposal material based on the customer's emotion data and creates materials that attract the customer's interest. For example, it makes proposals that focus on topics that the customer has positive emotions about. This makes it possible to automatically generate proposal content based on the customer's emotions and create materials that attract the customer's interest.

[0081] The document generation department customizes proposal materials based on past customer feedback, enabling more effective proposals. For example, the generation AI analyzes past customer feedback and customizes proposal materials. For example, it emphasizes proposal content that has been well received by customers in the past. The document generation department also adjusts the content of proposal materials based on customer feedback data. For example, it makes proposals that improve upon problems that customers have pointed out in the past. The document generation department also customizes the design and format of proposal materials based on the collected feedback data. For example, it adopts a layout and color usage that suits the customer's preferences. This allows proposal materials to be customized based on past customer feedback, enabling more effective proposals to be made.

[0082] The document generation unit can automatically apply designs and formats specific to the customer's industry to create proposal documents. For example, the generation AI automatically applies design templates related to the customer's industry to create proposal documents. For example, it uses industry-standard formats and designs. The document generation unit also reflects design elements specific to the customer's industry in the proposal documents. For example, it creates documents using industry logos and colors. The document generation unit also automatically applies industry-specific formats to maintain consistency in the proposal documents. For example, it uses industry-standard report formats. This makes it possible to automatically apply designs and formats specific to the customer's industry to create proposal documents.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The information collection department collects information from internal systems or customer websites. For example, the information collection department obtains past transaction history and customer purchase history from an internal database. It can also collect the latest news and product information from customer websites. It can also obtain information from external systems using API integration. For example, the information collection department obtains customer information from an internal CRM system and transaction data from an ERP system. It uses scraping technology to collect the latest news and product information from customer websites. Using API integration, it is possible to obtain information in real time from external data sources. Step 2: The information analysis department analyzes the information collected by the information collection department and organizes it in a format that is easy for salespeople to use. For example, the information analysis department organizes the collected information by category and displays it in dashboard format. It can also use text mining technology to analyze customer industry trends and information on competitors. It can also use data cleansing technology to improve the quality of the collected information. Step 3: The document generation unit automatically generates hypothesis proposal materials based on the information organized by the information analysis unit. For example, the document generation unit creates materials that include proposal content and solutions based on the customer's needs and issues. It can also create materials according to the format specified by the salesperson and automatically insert necessary graphs and charts. Furthermore, the content and design of the materials can be customized using generation AI.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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, in order to avoid confusion and to 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.

[0151] 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. [Explanation of symbols]

[0152] 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. an information collection department that collects information from internal systems or customer websites; an information analysis unit that analyzes the information collected by the information collection unit and organizes it in a format that is easy for salesmen to use; a material generation unit that automatically generates a hypothesis proposal material based on the information organized by the information analysis unit. A system characterized by:

2. The information collecting unit Collect the latest posts or comments from the customer's social media accounts to reflect the customer's real-time interests 2. The system of claim 1.

3. The information collecting unit Analyze the customer's past inquiry history or support tickets to extract the customer's potential needs and problems 2. The system of claim 1.

4. The information collecting unit Inferring the customer's sentiment from the customer's website or social media posts and filtering the information based on the sentiment.

2. The system of claim 1.

5. The information collecting unit Collect best practices or success stories from the different industries and incorporate them into proposal materials for the client.

2. The system of claim 1.

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