System

Abstract

An object of a system according to an embodiment is to efficiently create a material for a corporate sales to propose to a customer.SOLUTION: A system includes a customer information collection part, a proposal content generation part, and a proposal material creation part. The customer information collection unit collects data such as basic information, past transaction history, and current needs and desires of the customer. A proposal content generation part generates optimal proposal content on the basis of the data collected by the customer information collection part. The proposal material creation part creates a visually intelligible proposal material on the basis of the proposal content generated by the proposal content generation part.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 techniques have had the problem that it is difficult for corporate salespeople to efficiently create materials to propose to customers.

[0005] The system according to the embodiment aims to efficiently create materials for corporate sales representatives to propose to customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer information collection unit, a proposal content generation unit, and a proposal material creation unit. The customer information collection unit collects data such as basic customer information, past transaction history, and current needs and requests. The proposal content generation unit generates optimal proposal content based on the data collected by the customer information collection unit. The proposal material creation unit creates visually easy-to-understand proposal materials based on the proposal content generated by the proposal content generation unit. [Effects of the Invention]

[0007] The system according to the embodiment enables corporate sales personnel to efficiently create materials to propose to customers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A proposal material creation system according to an embodiment of the present invention is a system that automatically creates materials for corporate sales representatives to make proposals to customers. In this system, a generation AI generates proposal materials based on the needs and requests of customers. As a result, the proposal material creation system enables sales representatives to efficiently create proposal materials and make effective proposals to customers.

[0029] A proposal material creation system according to an embodiment includes a customer information collection unit, a proposal content generation unit, and a proposal material creation unit. The customer information collection unit collects data such as basic customer information, past transaction history, current needs and requests, etc. For example, the customer information collection unit collects information such as the customer's industry, size, location, past purchase history, current challenges and goals, etc. The customer information collection unit can also analyze the customer's social media activity to identify the latest interests and trends. The proposal content generation unit generates optimal proposal content based on the data collected by the customer information collection unit. For example, the proposal content generation unit proposes products and services that meet the customer's needs, explains implementation examples and benefits, and presents prices and contract terms. The proposal content generation unit can also analyze the trends of the customer's competitors and generate proposals that emphasize competitive advantages. The proposal material creation unit creates visually easy-to-understand proposal materials based on the proposal content generated by the proposal content generation unit. For example, the proposal material creation unit generates materials including presentation slides, PDF documents, graphs, charts, etc. The proposal material creation unit can also add interactive elements to the proposal material to enable customers to search for information on their own. As a result, the proposal material creation system according to the embodiment allows sales representatives to efficiently create proposal materials and make effective proposals to customers.

[0030] The customer information collection unit analyzes customers' social media activities to understand their latest interests and trends. For example, the customer information collection unit automatically scans customers' social media accounts and analyzes the content of posts and the reactions of followers. This identifies topics and trends that customers are currently interested in. The customer information collection unit also collects customer engagement data on social media and extracts keywords and hashtags that interest them. This identifies their latest interests. The customer information collection unit also analyzes customers' social media activities over time to track changes in their interests. For example, it analyzes reactions to specific events or campaigns and reflects this in proposal materials. This allows the system to understand customers' latest interests and trends and reflect them in proposal content.

[0031] The customer information collection unit analyzes the content of customers' past inquiries and support history, and can predict potential needs. For example, the customer information collection unit extracts the content of customers' past inquiries from a database and analyzes common problems and requests. This makes it possible to predict potential customer needs. The customer information collection unit also analyzes support history and identifies frequently occurring problems and requests. This allows it to propose solutions that customers may need in the future. The customer information collection unit also cross-references the content of customers' inquiries and support history to find patterns. For example, it identifies common problems with specific products or services and reflects these in proposal materials. This makes it possible to predict potential customer needs and reflect them in proposals.

[0032] The proposal content generation unit can analyze the trends of the customer's competitors and generate proposals that emphasize their competitive advantages. For example, the proposal content generation unit automatically scans the websites and news articles of the customer's competitors and analyzes their latest trends and strategies. This generates proposals that emphasize their competitive advantages. The proposal content generation unit also collects reviews of competitors' products and services and compares them with the customer's products and services. This creates proposals that emphasize the customer's strengths. The proposal content generation unit also analyzes competitors' marketing campaigns and promotional activities and makes differentiated proposals to the customer. For example, the proposal materials can reflect unique value that competitors do not offer. This makes it possible to understand the trends of the customer's competitors and generate proposals that emphasize their competitive advantages.

[0033] The proposal content generation unit can automatically collect industry news and market reports for the client and reflect the latest industry information in the proposal materials. The proposal content generation unit, for example, automatically collects news articles and market reports related to the client's industry and reflects them in the proposal materials. For example, it incorporates the latest technological trends and market changes into the proposal materials. The proposal content generation unit also analyzes industry news and market reports and extracts information that is important to the client. This allows the latest industry information to be reflected in the proposal materials. The proposal content generation unit also makes proposals tailored to the client's needs based on the automatically collected industry news and market reports. For example, it includes solutions that address industry trends and market needs in the proposal materials. This allows the latest industry information to be reflected in the proposal materials.

[0034] The proposal material creation department can add interactive elements to proposal materials to enable customers to explore information on their own. For example, the proposal material creation department adds interactive elements to proposal materials to enable customers to explore information on their own. For example, it installs clickable links and buttons. The proposal material creation department also incorporates interactive graphs and charts into proposal materials to enable customers to manipulate data. For example, it clicks on a data point to display detailed information. The proposal material creation department also adds interactive simulations to proposal materials to enable customers to try out scenarios on their own. For example, it simulates results under different conditions. This makes it possible to provide interactive proposal materials that enable customers to explore information on their own.

[0035] The proposal material creation department can incorporate past success stories of customers into proposal materials to increase their credibility. The proposal material creation department, for example, incorporates past success stories of customers into proposal materials to increase their credibility. For example, detailed explanations of the success stories and specific results are included. The proposal material creation department also visualizes the past success stories of customers and incorporates them into proposal materials. For example, graphs and charts of the success stories are used to provide visual explanations. The proposal material creation department also cites past success stories of customers to make the proposal materials more credible. For example, quotations from success stories and customer feedback are included. In this way, by incorporating past success stories of customers into proposal materials, credibility can be increased.

[0036] The proposal material creation unit can generate proposal materials in multiple formats and provide them according to customer preferences. For example, the proposal material creation unit generates proposal materials in video format to provide visually appealing presentations to customers. For example, product demos and implementation cases are explained using videos. The proposal material creation unit can also generate proposal materials in infographic format to convey information to customers in an easy-to-understand manner. For example, data and statistical information are visually displayed. The proposal material creation unit can also generate proposal materials in PDF or slide format to provide them according to customer preferences. For example, a PDF document or presentation slides containing detailed explanations are created. This allows proposal materials to be provided in a format that suits the customer's preferences.

[0037] The proposal material creation department can include comparative data with the client's competitors in the proposal materials to emphasize the client's competitive advantage. For example, the proposal material creation department can include comparative data with the client's competitors in the proposal materials to emphasize the client's competitive advantage. For example, the proposal material creation department can create a comparison table of product performance and price. The proposal material creation department can also collect data on competitors and compare it with the client's products and services. In this way, the proposal material creation department can create proposal materials that emphasize the client's strengths. The proposal material creation department can also include the weaknesses and challenges of competitors in the proposal materials to demonstrate the superiority of the client's products and services. For example, the proposal material creation department can compare competitors' product reviews and market shares. In this way, the proposal material creation department can emphasize the client's competitive advantage by including comparative data with the client's competitors in the proposal materials.

[0038] The proposal material creation unit can automatically analyze a client's brand guidelines and reflect a consistent design in the proposal materials. The proposal material creation unit, for example, automatically analyzes a client's brand guidelines and reflects a consistent design in the proposal materials. For example, it uses a logo and brand colors. The proposal material creation unit also adjusts the font and layout of the proposal materials based on the brand guidelines. This allows the creation of proposal materials that match the client's brand image. The proposal material creation unit also analyzes the client's brand guidelines and automatically generates a template to be applied to the proposal materials. For example, it creates a slide template that complies with the brand guidelines. This allows the proposal materials to reflect a consistent design based on the client's brand guidelines.

[0039] The proposal material creation unit can fine-tune the content of the proposal material based on past customer feedback. For example, the proposal material creation unit extracts past customer feedback from a database and fine-tunes the content of the proposal material. For example, it can reflect specific requests and areas for improvement. The proposal material creation unit also analyzes the feedback content and identifies frequently pointed out problems and requests. This improves the content of the proposal material. The proposal material creation unit also cross-references customer feedback data and finds patterns. For example, it can identify common areas for improvement for specific products or services and reflect them in the proposal material. This makes it possible to fine-tune the content of the proposal material based on past customer feedback.

[0040] The proposal material creation department can customize proposal materials according to the different departments and positions of the customer to meet the needs of each department. For example, the proposal material creation department customizes proposal materials according to the different departments and positions of the customer to meet the needs of each department. For example, it creates materials for the marketing department and materials for the technical department. The proposal material creation department also collects requests from the different departments and positions of the customer and customizes proposal materials based on those requests. For example, it makes proposals according to the challenges and goals of each department. The proposal material creation department also customizes proposal materials according to the different departments and positions of the customer to meet the needs of each department. For example, it includes data and statistical information for each department. This makes it possible to provide proposal materials customized according to the different departments and positions of the customer.

[0041] The proposal material creation department can include information about the customer's competitors in the proposal materials to emphasize their competitive advantage. For example, the proposal material creation department can include information about the customer's competitors in the proposal materials to emphasize their competitive advantage. For example, it can create a comparison table of product performance and price. The proposal material creation department can also collect data on competitors and compare it with the customer's products and services. This creates proposal materials that emphasize the customer's strengths. The proposal material creation department can also include information about competitors' weaknesses and challenges in the proposal materials to demonstrate the superiority of the customer's products and services. For example, it can compare product reviews and market shares of competitors. This allows the competitive advantage to be emphasized by including information about the customer's competitors in the proposal materials.

[0042] The proposal material creation unit can automatically record the update history of the proposal material, making it possible to track changes. The proposal material creation unit, for example, builds a system that can automatically record the update history of the proposal material and track changes. For example, it records the changes for each version and displays the history. The proposal material creation unit also analyzes the update history of the proposal material and identifies parts that are frequently changed. This identifies areas for improvement in the proposal material. The proposal material creation unit also automatically records the update history of the proposal material, making it possible to track changes. For example, it highlights the changes and makes it possible to check the history. This makes it possible to automatically record the update history of the proposal material, making it possible to track changes.

[0043] The proposal material creation department can automatically reflect improvements to proposal materials based on customer feedback. The proposal material creation department builds a system that automatically reflects improvements to proposal materials based on, for example, customer feedback. For example, it analyzes the feedback content and automatically updates the proposal materials. The proposal material creation department also analyzes the feedback content and identifies frequently pointed out problems and requests. This improves the content of the proposal materials. The proposal material creation department also cross-references customer feedback data to find patterns. For example, it identifies common improvements to specific products or services and reflects them in the proposal materials. This makes it possible to automatically reflect improvements to proposal materials based on customer feedback.

[0044] The proposal material creation department can manage proposal materials on the cloud, allowing multiple sales representatives to access and update them in real time. For example, the proposal material creation department can build a system that manages proposal materials on the cloud, allowing multiple sales representatives to access and update them in real time. For example, it can share proposal materials using cloud storage. The proposal material creation department can also manage proposal materials on the cloud, allowing sales representatives to access them from anywhere. This allows proposal materials to be updated even while working remotely or on a business trip. The proposal material creation department can also manage the update history of proposal materials on the cloud, allowing multiple sales representatives to work on them simultaneously. For example, it can provide a real-time collaborative editing function. This allows proposal materials to be managed on the cloud, allowing multiple sales representatives to access and update them in real time.

[0045] The proposal material creation unit automatically generates proposal material templates, allowing consistent proposals to be made to different customers. The proposal material creation unit, for example, automatically generates proposal material templates and builds a system that allows consistent proposals to be made to different customers. For example, it creates templates based on brand guidelines. The proposal material creation unit also automatically generates proposal material templates, allowing sales representatives to easily customize them. This reduces the time required to create proposal materials. The proposal material creation unit also automatically generates proposal material templates, allowing consistent proposals to be made to different customers. For example, it uses a common layout and design. This allows the proposal material templates to be automatically generated, allowing consistent proposals to be made to different customers.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The proposal material creation system may further include a prediction unit that analyzes a customer's purchasing history and predicts future needs based on past purchasing patterns. For example, it may identify products or services that a customer purchases regularly and predict when they will be replenished. The prediction unit may also analyze a customer's purchasing history and identify purchasing patterns according to seasons or events. This allows it to propose products or services that a customer will need at specific times in advance. The prediction unit may also combine a customer's purchasing history with market trends to predict future needs. For example, it may make proposals based on the release date of a new product or market fluctuations.

[0048] The proposal material creation system can further include a visit analysis unit that analyzes a customer's website visit history and identifies products and services in which the customer is interested. For example, it can analyze the pages the customer frequently visits and the links they click to identify products and services of high interest. The visit analysis unit can also analyze the customer's website visit history over time and track changes in interests. This can identify topics and trends that the customer is currently interested in and reflect them in the proposal content. The visit analysis unit can also combine the customer's website visit history with other data to make more accurate proposals. For example, it can combine it with social media activity and purchase history to identify overall interests.

[0049] The proposal material creation system can further include a legal compliance unit that automatically collects regulations and laws specific to the client's industry and reflects them in the proposal content. For example, the latest laws and regulations related to the client's industry can be automatically collected and reflected in the proposal material. The legal compliance unit can also analyze regulations and laws specific to the client's industry and check whether the proposal content complies with them. This allows the client to receive a proposal that complies with laws and regulations. The legal compliance unit can also automatically update the proposal content in response to changes in laws and regulations. For example, when new laws and regulations come into effect, the content of those laws and regulations can be reflected in the proposal material.

[0050] The proposal material creation system may further include a benchmark collection unit that collects benchmark data specific to the client's industry and reflects it in the proposal content. For example, average performance indicators and market share in the client's industry are collected and reflected in the proposal material. The benchmark collection unit may also analyze the benchmark data specific to the client's industry and compare it with the client's current situation. This makes it possible to clearly show where the client is in the industry. The benchmark collection unit may also make specific proposals to improve the client's performance based on the benchmark data. For example, industry best practices may be reflected in the proposal material.

[0051] The proposal material creation system can further include an internal analysis unit that analyzes the client's internal communication tools and collects internal opinions and feedback. For example, the internal chat tools and email contents can be analyzed to collect internal opinions and feedback from the client. The internal analysis unit can also analyze internal communication data to identify common problems and requests. This allows the client's internal opinions and feedback to be reflected in the proposal content. The internal analysis unit can also analyze internal communication data over time to track changes in opinions and feedback. For example, reactions to a specific project or campaign can be analyzed and reflected in proposal materials.

[0052] The proposal material creation system may further include a training data collection unit that collects training data specific to the client's industry and reflects it in the proposal content. For example, the latest training programs and educational materials in the client's industry are collected and reflected in the proposal material. The training data collection unit may also analyze the training data specific to the client's industry and apply it to the proposal content. This allows the skills and knowledge required by the client in the industry to be reflected in the proposal material. The training data collection unit may also make specific proposals to support the client's skill improvement based on the training data. For example, the latest training programs in the industry may be reflected in the proposal material.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The customer information collection department collects data such as basic customer information, past transaction history, and current needs and requests. For example, they collect information such as the customer's industry, size, location, past purchase history, and current challenges and goals. They can also analyze customers' social media activity to understand their latest interests and trends. Step 2: The proposal content generation unit generates optimal proposal content based on the data collected by the customer information collection unit. For example, it proposes products and services that meet the customer's needs, explains implementation examples and effects, and presents prices and contract terms. It can also analyze the trends of the customer's competitors and generate proposals that emphasize their competitive advantages. Step 3: The proposal material creation unit creates visually easy-to-understand proposal materials based on the proposal content generated by the proposal content generation unit. For example, it generates materials including presentation slides, PDF documents, graphs, charts, etc. It can also add interactive elements to the proposal materials to allow customers to explore information on their own.

[0055] (Example 2) A proposal material creation system according to an embodiment of the present invention is a system that automatically creates materials for corporate sales representatives to make proposals to customers. In this system, a generation AI generates proposal materials based on the needs and requests of customers. As a result, the proposal material creation system enables sales representatives to efficiently create proposal materials and make effective proposals to customers.

[0056] A proposal material creation system according to an embodiment includes a customer information collection unit, a proposal content generation unit, and a proposal material creation unit. The customer information collection unit collects data such as basic customer information, past transaction history, current needs and requests, etc. For example, the customer information collection unit collects information such as the customer's industry, size, location, past purchase history, current challenges and goals, etc. The customer information collection unit can also analyze the customer's social media activity to identify the latest interests and trends. The proposal content generation unit generates optimal proposal content based on the data collected by the customer information collection unit. For example, the proposal content generation unit proposes products and services that meet the customer's needs, explains implementation examples and benefits, and presents prices and contract terms. The proposal content generation unit can also analyze the trends of the customer's competitors and generate proposals that emphasize competitive advantages. The proposal material creation unit creates visually easy-to-understand proposal materials based on the proposal content generated by the proposal content generation unit. For example, the proposal material creation unit generates materials including presentation slides, PDF documents, graphs, charts, etc. The proposal material creation unit can also add interactive elements to the proposal material to enable customers to search for information on their own. As a result, the proposal material creation system according to the embodiment allows sales representatives to efficiently create proposal materials and make effective proposals to customers.

[0057] The customer information collection unit analyzes customers' social media activities to understand their latest interests and trends. For example, the customer information collection unit automatically scans customers' social media accounts and analyzes the content of posts and the reactions of followers. This identifies topics and trends that customers are currently interested in. The customer information collection unit also collects customer engagement data on social media and extracts keywords and hashtags that interest them. This identifies their latest interests. The customer information collection unit also analyzes customers' social media activities over time to track changes in their interests. For example, it analyzes reactions to specific events or campaigns and reflects this in proposal materials. This allows the system to understand customers' latest interests and trends and reflect them in proposal content.

[0058] The customer information collection unit analyzes the content of customers' past inquiries and support history, and can predict potential needs. For example, the customer information collection unit extracts the content of customers' past inquiries from a database and analyzes common problems and requests. This makes it possible to predict potential customer needs. The customer information collection unit also analyzes support history and identifies frequently occurring problems and requests. This allows it to propose solutions that customers may need in the future. The customer information collection unit also cross-references the content of customers' inquiries and support history to find patterns. For example, it identifies common problems with specific products or services and reflects these in proposal materials. This makes it possible to predict potential customer needs and reflect them in proposals.

[0059] The customer information collection unit can use the emotion estimation function to analyze changes in emotions from past customer communications and reflect them in proposal content. The customer information collection unit, for example, analyzes the content of past emails and chats with the customer and uses the emotion estimation function to identify changes in emotions. For example, it identifies periods when positive emotions were strong and periods when negative emotions were strong. The customer information collection unit also analyzes the customer's past communication data using an emotion estimation algorithm to grasp emotional trends. This allows the proposal content to be adjusted to match the customer's emotional state. The customer information collection unit also analyzes changes in the customer's emotions over time to identify emotional reactions to specific events or situations. For example, it analyzes emotional reactions to the release of a new product or a change in a service and reflects this in proposal materials. This allows changes in the customer's emotions to be understood and reflected in the proposal content.

[0060] The proposal content generation unit can analyze the trends of the customer's competitors and generate proposals that emphasize their competitive advantages. For example, the proposal content generation unit automatically scans the websites and news articles of the customer's competitors and analyzes their latest trends and strategies. This generates proposals that emphasize their competitive advantages. The proposal content generation unit also collects reviews of competitors' products and services and compares them with the customer's products and services. This creates proposals that emphasize the customer's strengths. The proposal content generation unit also analyzes competitors' marketing campaigns and promotional activities and makes differentiated proposals to the customer. For example, the proposal materials can reflect unique value that competitors do not offer. This makes it possible to understand the trends of the customer's competitors and generate proposals that emphasize their competitive advantages.

[0061] The proposal content generation unit can automatically collect industry news and market reports for the client and reflect the latest industry information in the proposal materials. The proposal content generation unit, for example, automatically collects news articles and market reports related to the client's industry and reflects them in the proposal materials. For example, it incorporates the latest technological trends and market changes into the proposal materials. The proposal content generation unit also analyzes industry news and market reports and extracts information that is important to the client. This allows the latest industry information to be reflected in the proposal materials. The proposal content generation unit also makes proposals tailored to the client's needs based on the automatically collected industry news and market reports. For example, it includes solutions that address industry trends and market needs in the proposal materials. This allows the latest industry information to be reflected in the proposal materials.

[0062] The proposal content generation unit can use the emotion estimation function to propose a communication strategy according to the emotional state of the customer. The proposal content generation unit, for example, analyzes the emotional state of the customer in real time and proposes an optimal communication strategy based on the results. For example, if the customer has strong positive emotions, it makes proactive suggestions. The proposal content generation unit also uses the emotion estimation function to generate customized messages according to the customer's emotional state. For example, if the customer has strong negative emotions, it proposes a message that shows empathy. The proposal content generation unit also analyzes the customer's emotional state and compares it with past communication data to propose an optimal communication strategy. For example, it proposes follow-up at a time that corresponds to changes in emotions. This makes it possible to propose an optimal communication strategy according to the customer's emotional state.

[0063] The proposal material creation department can add interactive elements to proposal materials to enable customers to explore information on their own. For example, the proposal material creation department adds interactive elements to proposal materials to enable customers to explore information on their own. For example, it installs clickable links and buttons. The proposal material creation department also incorporates interactive graphs and charts into proposal materials to enable customers to manipulate data. For example, it clicks on a data point to display detailed information. The proposal material creation department also adds interactive simulations to proposal materials to enable customers to try out scenarios on their own. For example, it simulates results under different conditions. This makes it possible to provide interactive proposal materials that enable customers to explore information on their own.

[0064] The proposal material creation department can incorporate past success stories of customers into proposal materials to increase their credibility. The proposal material creation department, for example, incorporates past success stories of customers into proposal materials to increase their credibility. For example, detailed explanations of the success stories and specific results are included. The proposal material creation department also visualizes the past success stories of customers and incorporates them into proposal materials. For example, graphs and charts of the success stories are used to provide visual explanations. The proposal material creation department also cites past success stories of customers to make the proposal materials more credible. For example, quotations from success stories and customer feedback are included. In this way, by incorporating past success stories of customers into proposal materials, credibility can be increased.

[0065] The proposal material creation unit can use the emotion estimation function to reflect visuals and messages that appeal to the customer's emotions in the proposal material. The proposal material creation unit, for example, uses the emotion estimation function to reflect visuals and messages that appeal to the customer's emotions in the proposal material. For example, it uses images and catchphrases that evoke positive emotions. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, it uses colors and fonts that have a high emotion score. The proposal material creation unit also uses the emotion estimation function to include customized messages based on the customer's emotions in the proposal material. For example, it adds messages that empathize with the customer's emotions. This makes it possible to reflect visuals and messages that appeal to the customer's emotions in the proposal material.

[0066] The proposal material creation unit can generate proposal materials in multiple formats and provide them according to customer preferences. For example, the proposal material creation unit generates proposal materials in video format to provide visually appealing presentations to customers. For example, product demos and implementation cases are explained using videos. The proposal material creation unit can also generate proposal materials in infographic format to convey information to customers in an easy-to-understand manner. For example, data and statistical information are visually displayed. The proposal material creation unit can also generate proposal materials in PDF or slide format to provide them according to customer preferences. For example, a PDF document or presentation slides containing detailed explanations are created. This allows proposal materials to be provided in a format that suits the customer's preferences.

[0067] The proposal material creation department can include comparative data with the client's competitors in the proposal materials to emphasize the client's competitive advantage. For example, the proposal material creation department can include comparative data with the client's competitors in the proposal materials to emphasize the client's competitive advantage. For example, the proposal material creation department can create a comparison table of product performance and price. The proposal material creation department can also collect data on competitors and compare it with the client's products and services. In this way, the proposal material creation department can create proposal materials that emphasize the client's strengths. The proposal material creation department can also include the weaknesses and challenges of competitors in the proposal materials to demonstrate the superiority of the client's products and services. For example, the proposal material creation department can compare competitors' product reviews and market shares. In this way, the proposal material creation department can emphasize the client's competitive advantage by including comparative data with the client's competitors in the proposal materials.

[0068] The proposal material creation unit can use the emotion estimation function to include in the proposal material a customized message that corresponds to the customer's emotions. The proposal material creation unit, for example, uses the emotion estimation function to include in the proposal material a customized message that corresponds to the customer's emotions. For example, a message that elicits positive emotions is added. The proposal material creation unit also analyzes the customer's emotional state and adjusts the message in the proposal material based on the results. For example, words and phrases with a high emotion score are used. The proposal material creation unit also uses the emotion estimation function to include in the proposal material a customized message that corresponds to the customer's emotions. For example, a message that empathizes with the customer's emotions is added. This makes it possible to include in the proposal material a customized message that corresponds to the customer's emotions.

[0069] The proposal material creation unit can automatically analyze a client's brand guidelines and reflect a consistent design in the proposal materials. The proposal material creation unit, for example, automatically analyzes a client's brand guidelines and reflects a consistent design in the proposal materials. For example, it uses a logo and brand colors. The proposal material creation unit also adjusts the font and layout of the proposal materials based on the brand guidelines. This allows the creation of proposal materials that match the client's brand image. The proposal material creation unit also analyzes the client's brand guidelines and automatically generates a template to be applied to the proposal materials. For example, it creates a slide template that complies with the brand guidelines. This allows the proposal materials to reflect a consistent design based on the client's brand guidelines.

[0070] The proposal material creation unit can fine-tune the content of the proposal material based on past customer feedback. For example, the proposal material creation unit extracts past customer feedback from a database and fine-tunes the content of the proposal material. For example, it can reflect specific requests and areas for improvement. The proposal material creation unit also analyzes the feedback content and identifies frequently pointed out problems and requests. This improves the content of the proposal material. The proposal material creation unit also cross-references customer feedback data and finds patterns. For example, it can identify common areas for improvement for specific products or services and reflect them in the proposal material. This makes it possible to fine-tune the content of the proposal material based on past customer feedback.

[0071] The proposal material creation unit uses the emotion estimation function to customize the proposal based on the customer's emotions and provide a more personalized proposal. The proposal material creation unit, for example, uses the emotion estimation function to customize the proposal based on the customer's emotions and provide a more personalized proposal. For example, a message that elicits positive emotions is added. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, colors and fonts with high emotion scores are used. The proposal material creation unit also uses the emotion estimation function to include a customized message based on the customer's emotions in the proposal material. For example, a message that empathizes with the customer's emotions is added. This makes it possible to customize the proposal based on the customer's emotions and provide a more personalized proposal.

[0072] The proposal material creation department can customize proposal materials according to the different departments and positions of the customer to meet the needs of each department. For example, the proposal material creation department customizes proposal materials according to the different departments and positions of the customer to meet the needs of each department. For example, it creates materials for the marketing department and materials for the technical department. The proposal material creation department also collects requests from the different departments and positions of the customer and customizes proposal materials based on those requests. For example, it makes proposals according to the challenges and goals of each department. The proposal material creation department also customizes proposal materials according to the different departments and positions of the customer to meet the needs of each department. For example, it includes data and statistical information for each department. This makes it possible to provide proposal materials customized according to the different departments and positions of the customer.

[0073] The proposal material creation department can include information about the customer's competitors in the proposal materials to emphasize their competitive advantage. For example, the proposal material creation department can include information about the customer's competitors in the proposal materials to emphasize their competitive advantage. For example, it can create a comparison table of product performance and price. The proposal material creation department can also collect data on competitors and compare it with the customer's products and services. This creates proposal materials that emphasize the customer's strengths. The proposal material creation department can also include information about competitors' weaknesses and challenges in the proposal materials to demonstrate the superiority of the customer's products and services. For example, it can compare product reviews and market shares of competitors. This allows the competitive advantage to be emphasized by including information about the customer's competitors in the proposal materials.

[0074] The proposal material creation unit can use the emotion estimation function to include customized visuals and messages in the proposal material that correspond to the customer's emotions. The proposal material creation unit, for example, uses the emotion estimation function to include customized visuals and messages in the proposal material that correspond to the customer's emotions. For example, it uses images and catchphrases that evoke positive emotions. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, it uses colors and fonts that have a high emotion score. The proposal material creation unit also uses the emotion estimation function to include customized messages in the proposal material that correspond to the customer's emotions. For example, it adds messages that empathize with the customer's emotions. This makes it possible to include customized visuals and messages in the proposal material that correspond to the customer's emotions.

[0075] The proposal material creation unit can automatically record the update history of the proposal material, making it possible to track changes. The proposal material creation unit, for example, builds a system that can automatically record the update history of the proposal material and track changes. For example, it records the changes for each version and displays the history. The proposal material creation unit also analyzes the update history of the proposal material and identifies parts that are frequently changed. This identifies areas for improvement in the proposal material. The proposal material creation unit also automatically records the update history of the proposal material, making it possible to track changes. For example, it highlights the changes and makes it possible to check the history. This makes it possible to automatically record the update history of the proposal material, making it possible to track changes.

[0076] The proposal material creation department can automatically reflect improvements to proposal materials based on customer feedback. The proposal material creation department builds a system that automatically reflects improvements to proposal materials based on, for example, customer feedback. For example, it analyzes the feedback content and automatically updates the proposal materials. The proposal material creation department also analyzes the feedback content and identifies frequently pointed out problems and requests. This improves the content of the proposal materials. The proposal material creation department also cross-references customer feedback data to find patterns. For example, it identifies common improvements to specific products or services and reflects them in the proposal materials. This makes it possible to automatically reflect improvements to proposal materials based on customer feedback.

[0077] The proposal material creation unit uses the emotion estimation function to update the proposal material based on the customer's emotions, thereby enabling the provision of optimal proposal materials at all times. The proposal material creation unit, for example, uses the emotion estimation function to update the proposal material based on the customer's emotions, thereby enabling the provision of optimal proposal materials at all times. For example, a message that elicits positive emotions is added. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, colors and fonts with high emotion scores are used. The proposal material creation unit also uses the emotion estimation function to include a customized message based on the customer's emotions in the proposal material. For example, a message that empathizes with the customer's emotions is added. This allows the proposal material creation unit to update the proposal material based on the customer's emotions, thereby enabling the provision of optimal proposal materials at all times.

[0078] The proposal material creation department can manage proposal materials on the cloud, allowing multiple sales representatives to access and update them in real time. For example, the proposal material creation department can build a system that manages proposal materials on the cloud, allowing multiple sales representatives to access and update them in real time. For example, it can share proposal materials using cloud storage. The proposal material creation department can also manage proposal materials on the cloud, allowing sales representatives to access them from anywhere. This allows proposal materials to be updated even while working remotely or on a business trip. The proposal material creation department can also manage the update history of proposal materials on the cloud, allowing multiple sales representatives to work on them simultaneously. For example, it can provide a real-time collaborative editing function. This allows proposal materials to be managed on the cloud, allowing multiple sales representatives to access and update them in real time.

[0079] The proposal material creation unit automatically generates proposal material templates, allowing consistent proposals to be made to different customers. The proposal material creation unit, for example, automatically generates proposal material templates and builds a system that allows consistent proposals to be made to different customers. For example, it creates templates based on brand guidelines. The proposal material creation unit also automatically generates proposal material templates, allowing sales representatives to easily customize them. This reduces the time required to create proposal materials. The proposal material creation unit also automatically generates proposal material templates, allowing consistent proposals to be made to different customers. For example, it uses a common layout and design. This allows the proposal material templates to be automatically generated, allowing consistent proposals to be made to different customers.

[0080] The proposal material creation unit uses the emotion estimation function to update the proposal materials according to the customer's emotions, thereby enabling the provision of optimal proposal materials at all times. The proposal material creation unit, for example, uses the emotion estimation function to update the proposal materials according to the customer's emotions, thereby enabling the provision of optimal proposal materials at all times. For example, a message that elicits positive emotions is added. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal materials based on the results. For example, colors and fonts with high emotion scores are used. The proposal material creation unit also uses the emotion estimation function to include customized messages based on the customer's emotions in the proposal materials. For example, a message that empathizes with the customer's emotions is added. This allows the proposal material creation unit to update the proposal materials according to the customer's emotions, thereby enabling the provision of optimal proposal materials at all times.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The proposal material creation system may further include a prediction unit that analyzes a customer's purchasing history and predicts future needs based on past purchasing patterns. For example, it may identify products or services that a customer purchases regularly and predict when they will be replenished. The prediction unit may also analyze a customer's purchasing history and identify purchasing patterns according to seasons or events. This allows it to propose products or services that a customer will need at specific times in advance. The prediction unit may also combine a customer's purchasing history with market trends to predict future needs. For example, it may make proposals based on the release date of a new product or market fluctuations.

[0083] The proposal material creation system can further include a visit analysis unit that analyzes a customer's website visit history and identifies products and services in which the customer is interested. For example, it can analyze the pages the customer frequently visits and the links they click to identify products and services of high interest. The visit analysis unit can also analyze the customer's website visit history over time and track changes in interests. This can identify topics and trends that the customer is currently interested in and reflect them in the proposal content. The visit analysis unit can also combine the customer's website visit history with other data to make more accurate proposals. For example, it can combine it with social media activity and purchase history to identify overall interests.

[0084] The proposal material creation system can further include a legal compliance unit that automatically collects regulations and laws specific to the client's industry and reflects them in the proposal content. For example, the latest laws and regulations related to the client's industry can be automatically collected and reflected in the proposal material. The legal compliance unit can also analyze regulations and laws specific to the client's industry and check whether the proposal content complies with them. This allows the client to receive a proposal that complies with laws and regulations. The legal compliance unit can also automatically update the proposal content in response to changes in laws and regulations. For example, when new laws and regulations come into effect, the content of those laws and regulations can be reflected in the proposal material.

[0085] The proposal material creation system may further include a feedback collection unit that uses the customer emotion estimation function to collect feedback based on customer emotions and improve the proposal content. For example, the system may analyze the emotional reactions of customers who have viewed the proposal material and identify positive or negative reactions. The feedback collection unit may also collect feedback based on customer emotions and improve the proposal content based on the results. This allows the system to make optimal proposals based on customer emotions. The feedback collection unit may also analyze changes in customer emotions over time and identify areas for improvement in the proposal content. For example, the system may analyze what emotional reactions a specific proposal content evokes and identify areas for improvement.

[0086] The proposal material creation system can further use a customer emotion estimation function to include customized visuals and messages based on the customer's emotions in the proposal material. For example, it uses images and catchphrases that evoke positive emotions. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, it uses colors and fonts that have a high emotion score. The proposal material creation unit also uses the emotion estimation function to include customized messages based on the customer's emotions in the proposal material. For example, it adds messages that empathize with the customer's emotions. This makes it possible to include customized visuals and messages in the proposal material that correspond to the customer's emotions.

[0087] The proposal material creation system further uses a customer emotion estimation function to customize the proposal material based on the customer's emotions, allowing it to provide more personalized proposals. For example, it adds a message that elicits positive emotions. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, it uses colors and fonts that have a high emotion score. The proposal material creation unit also uses the emotion estimation function to include a customized message based on the customer's emotions in the proposal material. For example, it adds a message that empathizes with the customer's emotions. This allows it to customize the proposal material based on the customer's emotions and provide more personalized proposals.

[0088] The proposal material creation system further uses a customer emotion estimation function to update the proposal material based on the customer's emotions, allowing it to always provide the most optimal proposal material. For example, it adds a message that elicits positive emotions. The proposal material creation unit also analyzes the customer's emotional state and adjusts the design and layout of the proposal material based on the results. For example, it uses colors and fonts that have a high emotion score. The proposal material creation unit also uses the emotion estimation function to include customized messages based on the customer's emotions in the proposal material. For example, it adds a message that empathizes with the customer's emotions. This allows it to update the proposal material based on the customer's emotions and always provide the most optimal proposal material.

[0089] The proposal material creation system may further include a benchmark collection unit that collects benchmark data specific to the client's industry and reflects it in the proposal content. For example, average performance indicators and market share in the client's industry are collected and reflected in the proposal material. The benchmark collection unit may also analyze the benchmark data specific to the client's industry and compare it with the client's current situation. This makes it possible to clearly show where the client is in the industry. The benchmark collection unit may also make specific proposals to improve the client's performance based on the benchmark data. For example, industry best practices may be reflected in the proposal material.

[0090] The proposal material creation system can further include an internal analysis unit that analyzes the client's internal communication tools and collects internal opinions and feedback. For example, the internal chat tools and email contents can be analyzed to collect internal opinions and feedback from the client. The internal analysis unit can also analyze internal communication data to identify common problems and requests. This allows the client's internal opinions and feedback to be reflected in the proposal content. The internal analysis unit can also analyze internal communication data over time to track changes in opinions and feedback. For example, reactions to a specific project or campaign can be analyzed and reflected in proposal materials.

[0091] The proposal material creation system may further include a training data collection unit that collects training data specific to the client's industry and reflects it in the proposal content. For example, the latest training programs and educational materials in the client's industry are collected and reflected in the proposal material. The training data collection unit may also analyze the training data specific to the client's industry and apply it to the proposal content. This allows the skills and knowledge required by the client in the industry to be reflected in the proposal material. The training data collection unit may also make specific proposals to support the client's skill improvement based on the training data. For example, the latest training programs in the industry may be reflected in the proposal material.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The customer information collection department collects data such as basic customer information, past transaction history, and current needs and requests. For example, they collect information such as the customer's industry, size, location, past purchase history, and current challenges and goals. They can also analyze customers' social media activity to understand their latest interests and trends. Step 2: The proposal content generation unit generates optimal proposal content based on the data collected by the customer information collection unit. For example, it proposes products and services that meet the customer's needs, explains implementation examples and effects, and presents prices and contract terms. It can also analyze the trends of the customer's competitors and generate proposals that emphasize their competitive advantages. Step 3: The proposal material creation unit creates visually easy-to-understand proposal materials based on the proposal content generated by the proposal content generation unit. For example, it generates materials including presentation slides, PDF documents, graphs, charts, etc. It can also add interactive elements to the proposal materials to allow customers to explore information on their own.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A customer information collection department that collects data such as basic customer information, past transaction history, current needs and requests, etc. a proposal content generation unit that generates optimal proposal content based on the data collected by the customer information collection unit; a proposal material creation unit that creates a visually easy-to-understand proposal material based on the proposal content generated by the proposal content creation unit. A system characterized by:

2. The customer information collection unit Analyzing the social media activity of said customers to understand their latest interests and trends 2. The system of claim 1.

3. The customer information collection unit Analyze the customer's past inquiries and support history to predict potential needs 2. The system of claim 1.

4. The customer information collection unit Analyze changes in emotions from the customer's past communications and reflect them in the proposals 2. The system of claim 1.

5. The proposal content generation unit Analyze the trends of the client's competitors and generate proposals that emphasize their competitive advantages 2. The system of claim 1.

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