System

Abstract

An object of a system according to an embodiment is to quickly and accurately analyze the characteristics of corporate customers and propose appropriate products.SOLUTION: A system includes a collection unit, an analysis unit, a suggestion unit, and an brush-up unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit performs product proposal based on the analysis result obtained by the analysis unit. The brush-up unit performs brush-up of the suggestion material based on the content suggested by the suggestion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to quickly and accurately analyze customer characteristics and propose appropriate products in corporate sales.

[0005] The system according to the embodiment aims to quickly and accurately analyze the characteristics of corporate customers and propose appropriate products. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a brush-up unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes products based on the analysis results obtained by the analysis unit. The brush-up unit brushes up proposal materials based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately analyze the characteristics of corporate customers and propose appropriate products. [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 product proposal support system according to an embodiment of the present invention supports efficient product proposals in corporate sales. The product proposal support system inputs trends for individual companies and industries from reliable industry sources, and a generation AI analyzes the information to help optimize product proposals. Furthermore, the generation AI refines proposal materials. This system reduces analysis time for corporate customers and increases the number of sales proposals. It also enables proposals tailored to customer needs, improving customer satisfaction and reducing vendor turnover. For example, the product proposal support system collects detailed data such as corporate performance, market trends, and the latest industry news. Next, the generation AI analyzes the characteristics of corporate customers based on the collected data and optimizes product proposals. Furthermore, the generation AI optimizes the layout and content of proposal materials, enabling more attractive proposals to corporate customers. This allows the product proposal support system to reduce analysis time for corporate customers and increase the number of sales proposals. It also enables proposals tailored to customer needs, improving customer satisfaction and reducing vendor turnover. This allows the product proposal support system to reduce analysis time for corporate customers and increase the number of sales proposals. It also enables proposals that meet customer needs, improves customer satisfaction, and discourages transfers to other vendors.

[0029] A product proposal support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a polishing unit. The collection unit collects data. Examples of the data include, but are not limited to, numerical data, text data, and image data. The collection unit collects data such as corporate performance, market trends, and the latest industry news. The collection unit can also collect data from reliable sources. For example, the collection unit collects industry reports and news articles. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes characteristics of corporate customers based on the collected data. The analysis unit can also analyze the data using a generation AI. For example, the analysis unit inputs data into the generation AI to analyze the characteristics of corporate customers. The proposal unit makes product proposals based on the analysis results obtained by the analysis unit. Product proposals can be performed based on, for example, the type of product to be proposed and proposal criteria, but are not limited to these examples. For example, the proposal unit makes optimal product proposals based on the characteristics of corporate customers. The proposal unit can also use the generation AI to make product proposals. For example, the proposal unit inputs analysis results into the generation AI and makes optimal product proposals. The brush-up unit brushes up the proposal materials based on the content proposed by the proposal unit. Brush-up is performed, for example, based on adjusting the layout and correcting the content, but is not limited to such examples. For example, the brush-up unit optimizes the layout and content of the proposal materials. The brush-up unit can also brush up the proposal materials using the generation AI. For example, the brush-up unit inputs the proposal materials into the generation AI and optimizes the layout and content. As a result, the product proposal support system according to the embodiment can support efficient product proposals in corporate sales.

[0030] The collection unit can collect data on corporate performance, market trends, and the latest industry news. The collection unit, for example, collects corporate performance data. Corporate performance data includes, but is not limited to, sales revenue and profit margins. The collection unit can also collect market trend data. Market trend data includes, but is not limited to, market share and growth rate. The collection unit can also collect the latest industry news. The latest industry news includes, but is not limited to, industry reports and news articles. By collecting detailed corporate data, the characteristics of corporate customers can be identified. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input industry reports and news articles into the generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze the characteristics of corporate customers based on the collected data. The analysis unit, for example, analyzes the characteristics of corporate customers based on the collected data. The characteristics of corporate customers include, but are not limited to, company size, industry, and transaction history. The analysis unit can also analyze the data using a generation AI. For example, the analysis unit inputs data into the generation AI and analyzes the characteristics of corporate customers. By analyzing the characteristics of corporate customers, it becomes possible to propose optimal products. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit inputs the collected data into the generation AI and causes the generation AI to analyze the characteristics of corporate customers.

[0032] The proposal unit can make product proposals based on the characteristics of the corporate customer. The proposal unit, for example, makes product proposals based on the characteristics of the corporate customer. Product proposals include, for example, the type of product to be proposed and proposal criteria, but are not limited to these examples. The proposal unit can also make product proposals using a generation AI. For example, the proposal unit inputs analysis results into the generation AI and makes optimal product proposals. This enables proposals that meet the needs of the corporate customer. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit inputs analysis results into the generation AI and causes the generation AI to generate product proposals.

[0033] The brush-up unit can adjust the layout and content of the proposal materials. The brush-up unit, for example, adjusts the layout and content of the proposal materials. Layout adjustments include, but are not limited to, font size, color usage, and placement. Content adjustments include, but are not limited to, correcting text and adding charts and figures. Furthermore, the brush-up unit can also brush up the proposal materials using a generation AI. For example, the brush-up unit inputs the proposal materials into the generation AI and optimizes the layout and content. This allows the proposal materials to be completed more effectively. Some or all of the above-described processing in the brush-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the brush-up unit can input the proposal materials into the generation AI and have the generation AI adjust the layout and content.

[0034] The collection unit can analyze the company's past performance data and determine the priority of the data to be collected. The collection unit, for example, analyzes the company's past performance data and determines the priority of the data to be collected. Past performance data includes, for example, sales revenue and profit margins, but is not limited to these examples. For example, the collection unit analyzes the company's past sales data and prioritizes collecting data from companies with high sales. The collection unit can also analyze the company's past growth rate and prioritize collecting data from companies with high growth rates. The collection unit can also analyze the company's past profit margins and prioritize collecting data from companies with high profit margins. In this way, by analyzing the company's past performance data, important data can be prioritized. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past performance data to the generation AI and have the generation AI determine the priority of the data.

[0035] The collection unit may filter data based on the company's current projects and areas of interest when collecting data. For example, the collection unit may filter data based on the company's current projects and areas of interest when collecting data. Current projects include, but are not limited to, ongoing projects and planned projects. Areas of interest include, but are not limited to, technical fields and business fields. For example, the collection unit may prioritize collecting data related to the company's ongoing projects. The collection unit may also prioritize collecting data related to the company's areas of interest. The collection unit may also filter and collect necessary data based on the progress of the company's current projects. This allows necessary data to be collected based on the company's current projects and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the company's projects and areas of interest into the generation AI and have the generation AI filter the data.

[0036] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting data. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit collects data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect data using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect data using image recognition technology. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0037] The collection unit may prioritize collecting highly relevant data by taking into consideration the geographical location information of the company when collecting data. For example, the collection unit may prioritize collecting highly relevant data by taking into consideration the geographical location information of the company when collecting data. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the collection unit may prioritize collecting regional market trend data based on the company's location. The collection unit may also prioritize collecting data on nearby competitors based on the company's geographical location information. The collection unit may also prioritize collecting regional economic situation data by taking into consideration the company's geographical location information. This enables data collection tailored to regional characteristics by collecting highly relevant data by taking into consideration the company's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit may analyze the company's social media activities and collect related data during data collection. For example, the collection unit may analyze the company's social media activities and collect related data during data collection. Social media activities include, but are not limited to, post content, follower count, and engagement rate. For example, the collection unit may analyze the content of posts on the company's social media platforms and collect related data. The collection unit may also analyze responses from the company's followers on social media platforms and collect related data. The collection unit may also analyze campaign information on the company's social media platforms and collect related data. This allows for efficient collection of related data by analyzing the company's social media activities. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input social media activity data into a generation AI and cause the generation AI to collect related data.

[0039] The collection unit can customize the collection method by reflecting the company's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the company's past feedback when collecting data. Past feedback includes, but is not limited to, customer opinions, evaluations, and areas for improvement. For example, the collection unit customizes the type of data to be collected based on the company's past feedback. The collection unit can also customize the frequency of data collection based on the company's past feedback. The collection unit can also customize the data collection method based on the company's past feedback. In this way, the collection method can be optimized by reflecting the company's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of data includes, but is not limited to, the impact on business and the reliability of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also adjust the depth of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, numerical data, and image data. For example, the analysis unit applies a financial analysis algorithm to financial data. The analysis unit can also apply a market analysis algorithm to market trend data. The analysis unit can also apply a social media analysis algorithm to social media data. This enables highly accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past reports and analysis logs. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. By referring to the user's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. The time of data submission includes, for example, but is not limited to, the submission date and the submission time. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, but is not limited to, correlation and causal relationship. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have expert knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. This allows for analysis results that are easy to understand by providing analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the product when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the product when making the proposal. The importance of the product includes, but is not limited to, the impact on sales and market share. For example, the suggestion unit makes a detailed proposal for an important product. The suggestion unit can also make a simplified proposal for a product with low importance. The suggestion unit can also adjust the depth of the proposal based on the importance of the product. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the product to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the product when making a suggestion. Product categories include, but are not limited to, product categories and service categories. For example, the suggestion unit applies an IT suggestion algorithm to IT-related products. The suggestion unit can also apply a manufacturing suggestion algorithm to manufacturing-related products. The suggestion unit can also apply a service suggestion algorithm to service-related products. This enables highly accurate suggestions by applying a suggestion algorithm depending on the category of the product. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the category of the product to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0048] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, examples of the success rate of the suggestion and customer feedback. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and identify areas for improvement in the suggestion. As a result, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past suggestion results into a generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0049] The suggestion unit can determine the priority of proposals based on the submission date of the products when making the proposals. For example, the suggestion unit determines the priority of proposals based on the submission date of the products when making the proposals. The submission date of the products includes, but is not limited to, the submission date and the submission time. For example, the suggestion unit prioritizes the most recent products. The suggestion unit can also postpone the proposal of older submitted products. The suggestion unit can also adjust the order of proposals based on the submission date. Thus, by determining the priority of proposals based on the submission date of the products, the most recent products can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the submission date of the products to the generation AI and cause the generation AI to determine the priority of proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the products when making suggestions. The relevance of the products includes, but is not limited to, correlation and causal relationships. For example, the suggestion unit prioritizes suggesting highly relevant products. The suggestion unit can also suggest less relevant products later. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the products to a generation AI and cause the generation AI to adjust the order of suggestions.

[0051] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the suggestion unit may make a proposal that uses a lot of technical terms. Furthermore, if the user does not have expert knowledge, the suggestion unit may make a proposal in simple language. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. By providing a proposal according to the user's level of expertise, an easy-to-understand proposal can be obtained. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0052] The brush-up unit can select an optimal layout by referring to past success cases when brushing up the proposal materials. For example, the brush-up unit selects an optimal layout by referring to past success cases when brushing up the proposal materials. Past success cases include, but are not limited to, the content of successful proposals and the factors behind their success. For example, the brush-up unit selects an effective layout based on past success cases. The brush-up unit can also optimize the layout of the proposal materials by referring to past success cases. The brush-up unit can also analyze past success cases and propose an optimal layout. In this way, an effective layout can be selected by referring to past success cases. Some or all of the above-mentioned processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input past success cases into a generation AI and cause the generation AI to select an optimal layout.

[0053] The brush-up unit can customize the content of the proposal materials based on the user's current needs when brushing up the proposal materials. For example, the brush-up unit customizes the content based on the user's current needs when brushing up the proposal materials. Current needs include, but are not limited to, customer demands and industry trends. For example, the brush-up unit customizes the content of the proposal materials based on the user's current needs. The brush-up unit can also optimize the content of the proposal materials to reflect the user's current needs. The brush-up unit can also adjust the content of the proposal materials according to the user's current needs. This enables more effective proposals by customizing the content of the proposal materials based on the user's current needs. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input the user's current needs into a generation AI and have the generation AI customize the content.

[0054] The brush-up unit can improve the content of the proposal materials by reflecting user feedback when brushing them up. For example, the brush-up unit improves the content by reflecting user feedback when brushing up the proposal materials. User feedback includes, but is not limited to, customer opinions, evaluations, and areas for improvement. For example, the brush-up unit improves the content of the proposal materials based on user feedback. The brush-up unit can also optimize the content of the proposal materials by reflecting user feedback. The brush-up unit can also analyze user feedback and identify areas for improvement in the proposal materials. In this way, the content of the proposal materials can be optimized by reflecting user feedback. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input user feedback into a generation AI and cause the generation AI to improve the content.

[0055] The brush-up unit can select an optimal layout taking into account the geographical location information of a company when brushing up the proposal materials. For example, the brush-up unit selects an optimal layout taking into account the geographical location information of a company when brushing up the proposal materials. Geographical location information includes, but is not limited to, GPS data and address information. For example, the brush-up unit selects a layout that reflects regional characteristics based on the company's location. The brush-up unit can also propose an optimal layout based on the geographical location information of a company. The brush-up unit can also customize the layout of the proposal materials taking into account the geographical location information of a company. In this way, a layout that suits regional characteristics can be selected by taking into account the geographical location information of a company. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input geographical location information into a generation AI and cause the generation AI to select an optimal layout.

[0056] The brush-up unit can analyze the company's social media activities and suggest content when brushing up proposal materials. For example, when brushing up proposal materials, the brush-up unit can analyze the company's social media activities and suggest content. Social media activities include, but are not limited to, post content, number of followers, and engagement rate. For example, the brush-up unit can analyze the content of the company's posts on social media and suggest content for the proposal materials. The brush-up unit can also analyze the reactions of the company's followers on social media and suggest content for the proposal materials. The brush-up unit can also analyze campaign information on the company's social media and suggest content for the proposal materials. In this way, by analyzing the company's social media activities, relevant content can be reflected in the proposal materials. Some or all of the above-mentioned processing in the brush-up unit can be performed using, for example, AI, or can be performed without using AI. For example, the brush-up unit can input social media activity data into a generation AI and have the generation AI suggest content.

[0057] The brush-up unit can customize the content of proposal materials by reflecting the company's past feedback when brushing up the materials. For example, the brush-up unit customizes the content by reflecting the company's past feedback when brushing up the materials. Past feedback includes, but is not limited to, customer opinions, evaluations, and areas for improvement. For example, the brush-up unit customizes the content of the proposal materials based on the company's past feedback. The brush-up unit can also optimize the content of the proposal materials by reflecting the company's past feedback. The brush-up unit can also analyze the company's past feedback and identify areas for improvement in the proposal materials. In this way, the content of the proposal materials can be optimized by reflecting the company's past feedback. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input past feedback into a generation AI and have the generation AI customize the content.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When collecting data, the collection department can prioritize collecting relevant data by taking into account the skill sets of the company's employees. For example, if a company's employees have specific technical skills, the collection department can prioritize collecting market trend data related to those skills. The collection department can also collect the latest industry news and technology reports based on the employees' skill sets. Furthermore, the collection department can filter and collect company performance data according to the employees' skill sets. This allows the collection of necessary data based on the skill sets of the company's employees.

[0060] The suggestion unit can also improve the accuracy of suggestions by referring to the user's past purchase history when making suggestions. For example, similar products can be suggested based on data on products the user has previously purchased. The suggestion unit can also analyze the user's past purchase history to suggest products that suit the user's preferences and needs. Furthermore, the suggestion unit can also adjust the timing and method of suggestions by referring to the user's past purchase history. In this way, the accuracy of suggestions can be improved by referring to the user's past purchase history.

[0061] When collecting data, the collection department can also prioritize the collection of relevant data by taking into account the company's supply chain information. For example, data on a company's supply chain can be collected to evaluate the efficiency and risks of the supply chain. It can also collect performance data on supply chain partner companies to understand the performance of the entire supply chain. Furthermore, it can collect market trend data related to the supply chain and use it to optimize the supply chain. This makes it possible to collect necessary data by taking into account the company's supply chain information.

[0062] When making a proposal, the proposal unit can also customize the proposal content by taking into account regulations and laws specific to the user's industry. For example, the proposal unit can research regulations and laws related to the user's industry and adjust the proposal content based on that. The proposal unit can also propose products that comply with industry-specific regulations. Furthermore, the proposal content can be updated to reflect the latest information on legal amendments in the user's industry. This allows for more appropriate proposals by taking into account regulations and laws specific to the user's industry.

[0063] When collecting data, the collection department can prioritize collecting relevant data by taking into account information from the company's ecosystem partners. For example, the collection department can collect performance data from the company's ecosystem partners to evaluate the performance of the entire ecosystem. The collection department can also collect market trend data from the ecosystem partners to identify growth opportunities for the ecosystem. Furthermore, the collection department can collect technological innovation information from the ecosystem partners to enhance the competitiveness of the ecosystem. This allows the collection of necessary data by taking into account information from the company's ecosystem partners.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection department collects data. This data can include numerical data, text data, and image data. The collection department collects data such as company performance, market trends, and the latest industry news. It can also collect industry reports and news articles from reliable sources. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the characteristics of corporate customers based on the collected data. The analysis unit can also analyze the data using generative AI. Step 3: The proposal unit makes product proposals based on the analysis results obtained by the analysis unit. Product proposals are made based on the type of product to be proposed and the proposal criteria. For example, the proposal unit makes optimal product proposals based on the characteristics of corporate customers. Product proposals can also be made using generative AI. Step 4: The polishing department polishes the proposal materials based on the content proposed by the proposal department. The polishing is done by adjusting the layout and modifying the content. For example, the polishing department optimizes the layout and content of the proposal materials. It is also possible to polish the proposal materials using generative AI.

[0066] (Example 2) A product proposal support system according to an embodiment of the present invention supports efficient product proposals in corporate sales. The product proposal support system inputs trends for individual companies and industries from reliable industry sources, and a generation AI analyzes the information to help optimize product proposals. Furthermore, the generation AI refines proposal materials. This system reduces analysis time for corporate customers and increases the number of sales proposals. It also enables proposals tailored to customer needs, improving customer satisfaction and reducing vendor turnover. For example, the product proposal support system collects detailed data such as corporate performance, market trends, and the latest industry news. Next, the generation AI analyzes the characteristics of corporate customers based on the collected data and optimizes product proposals. Furthermore, the generation AI optimizes the layout and content of proposal materials, enabling more attractive proposals to corporate customers. This allows the product proposal support system to reduce analysis time for corporate customers and increase the number of sales proposals. It also enables proposals tailored to customer needs, improving customer satisfaction and reducing vendor turnover. This allows the product proposal support system to reduce analysis time for corporate customers and increase the number of sales proposals. It also enables proposals that meet customer needs, improves customer satisfaction, and discourages transfers to other vendors.

[0067] A product proposal support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a polishing unit. The collection unit collects data. Examples of the data include, but are not limited to, numerical data, text data, and image data. The collection unit collects data such as corporate performance, market trends, and the latest industry news. The collection unit can also collect data from reliable sources. For example, the collection unit collects industry reports and news articles. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes characteristics of corporate customers based on the collected data. The analysis unit can also analyze the data using a generation AI. For example, the analysis unit inputs data into the generation AI to analyze the characteristics of corporate customers. The proposal unit makes product proposals based on the analysis results obtained by the analysis unit. Product proposals can be performed based on, for example, the type of product to be proposed and proposal criteria, but are not limited to these examples. For example, the proposal unit makes optimal product proposals based on the characteristics of corporate customers. The proposal unit can also use the generation AI to make product proposals. For example, the proposal unit inputs analysis results into the generation AI and makes optimal product proposals. The brush-up unit brushes up the proposal materials based on the content proposed by the proposal unit. Brush-up is performed, for example, based on adjusting the layout and correcting the content, but is not limited to such examples. For example, the brush-up unit optimizes the layout and content of the proposal materials. The brush-up unit can also brush up the proposal materials using the generation AI. For example, the brush-up unit inputs the proposal materials into the generation AI and optimizes the layout and content. As a result, the product proposal support system according to the embodiment can support efficient product proposals in corporate sales.

[0068] The collection unit can collect data on corporate performance, market trends, and the latest industry news. The collection unit, for example, collects corporate performance data. Corporate performance data includes, but is not limited to, sales revenue and profit margins. The collection unit can also collect market trend data. Market trend data includes, but is not limited to, market share and growth rate. The collection unit can also collect the latest industry news. The latest industry news includes, but is not limited to, industry reports and news articles. By collecting detailed corporate data, the characteristics of corporate customers can be identified. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input industry reports and news articles into the generation AI and cause the generation AI to collect data.

[0069] The analysis unit can analyze the characteristics of corporate customers based on the collected data. The analysis unit, for example, analyzes the characteristics of corporate customers based on the collected data. The characteristics of corporate customers include, but are not limited to, company size, industry, and transaction history. The analysis unit can also analyze the data using a generation AI. For example, the analysis unit inputs data into the generation AI and analyzes the characteristics of corporate customers. By analyzing the characteristics of corporate customers, it becomes possible to propose optimal products. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or may be performed without using an AI. For example, the analysis unit inputs the collected data into the generation AI and causes the generation AI to analyze the characteristics of corporate customers.

[0070] The proposal unit can make product proposals based on the characteristics of the corporate customer. The proposal unit, for example, makes product proposals based on the characteristics of the corporate customer. Product proposals include, for example, the type of product to be proposed and proposal criteria, but are not limited to these examples. The proposal unit can also make product proposals using a generation AI. For example, the proposal unit inputs analysis results into the generation AI and makes optimal product proposals. This enables proposals that meet the needs of the corporate customer. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit inputs analysis results into the generation AI and causes the generation AI to generate product proposals.

[0071] The brush-up unit can adjust the layout and content of the proposal materials. The brush-up unit, for example, adjusts the layout and content of the proposal materials. Layout adjustments include, but are not limited to, font size, color usage, and placement. Content adjustments include, but are not limited to, correcting text and adding charts and figures. Furthermore, the brush-up unit can also brush up the proposal materials using a generation AI. For example, the brush-up unit inputs the proposal materials into the generation AI and optimizes the layout and content. This allows the proposal materials to be completed more effectively. Some or all of the above-described processing in the brush-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the brush-up unit can input the proposal materials into the generation AI and have the generation AI adjust the layout and content.

[0072] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important data and provide information quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0073] The collection unit can analyze the company's past performance data and determine the priority of the data to be collected. The collection unit, for example, analyzes the company's past performance data and determines the priority of the data to be collected. Past performance data includes, for example, sales revenue and profit margins, but is not limited to these examples. For example, the collection unit analyzes the company's past sales data and prioritizes collecting data from companies with high sales. The collection unit can also analyze the company's past growth rate and prioritize collecting data from companies with high growth rates. The collection unit can also analyze the company's past profit margins and prioritize collecting data from companies with high profit margins. In this way, by analyzing the company's past performance data, important data can be prioritized. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past performance data to the generation AI and have the generation AI determine the priority of the data.

[0074] The collection unit may filter data based on the company's current projects and areas of interest when collecting data. For example, the collection unit may filter data based on the company's current projects and areas of interest when collecting data. Current projects include, but are not limited to, ongoing projects and planned projects. Areas of interest include, but are not limited to, technical fields and business fields. For example, the collection unit may prioritize collecting data related to the company's ongoing projects. The collection unit may also prioritize collecting data related to the company's areas of interest. The collection unit may also filter and collect necessary data based on the progress of the company's current projects. This allows necessary data to be collected based on the company's current projects and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the company's projects and areas of interest into the generation AI and have the generation AI filter the data.

[0075] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting data. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit collects data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect data using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect data using image recognition technology. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0077] The collection unit may prioritize collecting highly relevant data by taking into consideration the geographical location information of the company when collecting data. For example, the collection unit may prioritize collecting highly relevant data by taking into consideration the geographical location information of the company when collecting data. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the collection unit may prioritize collecting regional market trend data based on the company's location. The collection unit may also prioritize collecting data on nearby competitors based on the company's geographical location information. The collection unit may also prioritize collecting regional economic situation data by taking into consideration the company's geographical location information. This enables data collection tailored to regional characteristics by collecting highly relevant data by taking into consideration the company's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0078] The collection unit may analyze the company's social media activities and collect related data during data collection. For example, the collection unit may analyze the company's social media activities and collect related data during data collection. Social media activities include, but are not limited to, post content, follower count, and engagement rate. For example, the collection unit may analyze the content of posts on the company's social media platforms and collect related data. The collection unit may also analyze responses from the company's followers on social media platforms and collect related data. The collection unit may also analyze campaign information on the company's social media platforms and collect related data. This allows for efficient collection of related data by analyzing the company's social media activities. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input social media activity data into a generation AI and cause the generation AI to collect related data.

[0079] The collection unit can customize the collection method by reflecting the company's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the company's past feedback when collecting data. Past feedback includes, but is not limited to, customer opinions, evaluations, and areas for improvement. For example, the collection unit customizes the type of data to be collected based on the company's past feedback. The collection unit can also customize the frequency of data collection based on the company's past feedback. The collection unit can also customize the data collection method based on the company's past feedback. In this way, the collection method can be optimized by reflecting the company's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user's emotion. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tensioned, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is hurrying, the analysis unit can provide a summary analysis result. By adjusting the way the analysis is presented based on the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of data includes, but is not limited to, the impact on business and the reliability of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also adjust the depth of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, numerical data, and image data. For example, the analysis unit applies a financial analysis algorithm to financial data. The analysis unit can also apply a market analysis algorithm to market trend data. The analysis unit can also apply a social media analysis algorithm to social media data. This enables highly accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past reports and analysis logs. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis. By referring to the user's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. User emotions include, but are not limited to, being in a hurry, being relaxed, or being excited. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotion, the analysis unit can provide an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. The time of data submission includes, for example, but is not limited to, the submission date and the submission time. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, but is not limited to, correlation and causal relationship. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have expert knowledge, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. This allows for analysis results that are easy to understand by providing analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0088] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tensioned, the suggestion unit can provide a simple, highly visible suggestion. If the user is relaxed, the suggestion unit can provide a detailed suggestion. If the user is hurrying, the suggestion unit can provide a suggestion that focuses on the main points. This enables more effective suggestions by adjusting the way the suggestion is expressed based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0089] The suggestion unit can adjust the level of detail of the proposal based on the importance of the product when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the product when making the proposal. The importance of the product includes, but is not limited to, the impact on sales and market share. For example, the suggestion unit makes a detailed proposal for an important product. The suggestion unit can also make a simplified proposal for a product with low importance. The suggestion unit can also adjust the depth of the proposal based on the importance of the product. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of the product to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0090] The suggestion unit can apply different suggestion algorithms depending on the category of the product when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the product when making a suggestion. Product categories include, but are not limited to, product categories and service categories. For example, the suggestion unit applies an IT suggestion algorithm to IT-related products. The suggestion unit can also apply a manufacturing suggestion algorithm to manufacturing-related products. The suggestion unit can also apply a service suggestion algorithm to service-related products. This enables highly accurate suggestions by applying a suggestion algorithm depending on the category of the product. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the category of the product to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0091] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, examples of the success rate of the suggestion and customer feedback. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and identify areas for improvement in the suggestion. As a result, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past suggestion results into a generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0092] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. User emotions include, but are not limited to, being in a hurry, being relaxed, being excited, etc. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a detailed suggestion. If the user is excited, the suggestion unit can provide a suggestion with a visually stimulating effect. This allows the suggestion length to be adjusted according to the user's emotion, thereby enabling the most suitable suggestion for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0093] The suggestion unit can determine the priority of proposals based on the submission date of the products when making the proposals. For example, the suggestion unit determines the priority of proposals based on the submission date of the products when making the proposals. The submission date of the products includes, but is not limited to, the submission date and the submission time. For example, the suggestion unit prioritizes the most recent products. The suggestion unit can also postpone the proposal of older submitted products. The suggestion unit can also adjust the order of proposals based on the submission date. Thus, by determining the priority of proposals based on the submission date of the products, the most recent products can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the submission date of the products to the generation AI and cause the generation AI to determine the priority of proposals.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the products when making suggestions. The relevance of the products includes, but is not limited to, correlation and causal relationships. For example, the suggestion unit prioritizes suggesting highly relevant products. The suggestion unit can also suggest less relevant products later. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of the products to a generation AI and cause the generation AI to adjust the order of suggestions.

[0095] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the suggestion unit may make a proposal that uses a lot of technical terms. Furthermore, if the user does not have expert knowledge, the suggestion unit may make a proposal in simple language. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. By providing a proposal according to the user's level of expertise, an easy-to-understand proposal can be obtained. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0096] The brush-up unit can estimate the user's emotions and adjust the layout of the proposal materials based on the estimated user emotions. The brush-up unit, for example, estimates the user's emotions and adjusts the layout of the proposal materials based on the estimated user emotions. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tensioned, the brush-up unit can provide a simple, highly visible layout. Furthermore, if the user is relaxed, the brush-up unit can provide a layout that includes detailed information. Furthermore, if the user is hurrying, the brush-up unit can provide a layout that focuses on the main points. By adjusting the layout of the proposal materials according to the user's emotions, more effective proposal materials can be created. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the brush-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the brush-up unit can input the user's emotional data into the generation AI and have the generation AI adjust the layout.

[0097] The brush-up unit can select an optimal layout by referring to past success cases when brushing up the proposal materials. For example, the brush-up unit selects an optimal layout by referring to past success cases when brushing up the proposal materials. Past success cases include, but are not limited to, the content of successful proposals and the factors behind their success. For example, the brush-up unit selects an effective layout based on past success cases. The brush-up unit can also optimize the layout of the proposal materials by referring to past success cases. The brush-up unit can also analyze past success cases and propose an optimal layout. In this way, an effective layout can be selected by referring to past success cases. Some or all of the above-mentioned processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input past success cases into a generation AI and cause the generation AI to select an optimal layout.

[0098] The brush-up unit can customize the content of the proposal materials based on the user's current needs when brushing up the proposal materials. For example, the brush-up unit customizes the content based on the user's current needs when brushing up the proposal materials. Current needs include, but are not limited to, customer demands and industry trends. For example, the brush-up unit customizes the content of the proposal materials based on the user's current needs. The brush-up unit can also optimize the content of the proposal materials to reflect the user's current needs. The brush-up unit can also adjust the content of the proposal materials according to the user's current needs. This enables more effective proposals by customizing the content of the proposal materials based on the user's current needs. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input the user's current needs into a generation AI and have the generation AI customize the content.

[0099] The brush-up unit can improve the content of the proposal materials by reflecting user feedback when brushing them up. For example, the brush-up unit improves the content by reflecting user feedback when brushing up the proposal materials. User feedback includes, but is not limited to, customer opinions, evaluations, and areas for improvement. For example, the brush-up unit improves the content of the proposal materials based on user feedback. The brush-up unit can also optimize the content of the proposal materials by reflecting user feedback. The brush-up unit can also analyze user feedback and identify areas for improvement in the proposal materials. In this way, the content of the proposal materials can be optimized by reflecting user feedback. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input user feedback into a generation AI and cause the generation AI to improve the content.

[0100] The brush-up unit can estimate the user's emotions and prioritize the proposal materials based on the estimated user emotions. The brush-up unit, for example, estimates the user's emotions and prioritizes the proposal materials based on the estimated user emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the brush-up unit can prioritize brushing up important proposal materials. Also, if the user is relaxed, the brush-up unit can prioritize brushing up detailed proposal materials. Also, if the user is in a hurry, the brush-up unit can prioritize processing proposal materials that can be quickly polished. In this way, by prioritizing the proposal materials according to the user's emotions, important proposal materials can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the polishing unit may be performed using, for example, AI, or may be performed without using AI. For example, the polishing unit may input user emotion data into the generation AI and have the generation AI determine the priority.

[0101] The brush-up unit can select an optimal layout taking into account the geographical location information of a company when brushing up the proposal materials. For example, the brush-up unit selects an optimal layout taking into account the geographical location information of a company when brushing up the proposal materials. Geographical location information includes, but is not limited to, GPS data and address information. For example, the brush-up unit selects a layout that reflects regional characteristics based on the company's location. The brush-up unit can also propose an optimal layout based on the geographical location information of a company. The brush-up unit can also customize the layout of the proposal materials taking into account the geographical location information of a company. In this way, a layout that suits regional characteristics can be selected by taking into account the geographical location information of a company. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input geographical location information into a generation AI and cause the generation AI to select an optimal layout.

[0102] The brush-up unit can analyze the company's social media activities and suggest content when brushing up proposal materials. For example, when brushing up proposal materials, the brush-up unit can analyze the company's social media activities and suggest content. Social media activities include, but are not limited to, post content, number of followers, and engagement rate. For example, the brush-up unit can analyze the content of the company's posts on social media and suggest content for the proposal materials. The brush-up unit can also analyze the reactions of the company's followers on social media and suggest content for the proposal materials. The brush-up unit can also analyze campaign information on the company's social media and suggest content for the proposal materials. In this way, by analyzing the company's social media activities, relevant content can be reflected in the proposal materials. Some or all of the above-mentioned processing in the brush-up unit can be performed using, for example, AI, or can be performed without using AI. For example, the brush-up unit can input social media activity data into a generation AI and have the generation AI suggest content.

[0103] The brush-up unit can customize the content of proposal materials by reflecting the company's past feedback when brushing up the materials. For example, the brush-up unit customizes the content by reflecting the company's past feedback when brushing up the materials. Past feedback includes, but is not limited to, customer opinions, evaluations, and areas for improvement. For example, the brush-up unit customizes the content of the proposal materials based on the company's past feedback. The brush-up unit can also optimize the content of the proposal materials by reflecting the company's past feedback. The brush-up unit can also analyze the company's past feedback and identify areas for improvement in the proposal materials. In this way, the content of the proposal materials can be optimized by reflecting the company's past feedback. Some or all of the above-described processing in the brush-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the brush-up unit can input past feedback into a generation AI and have the generation AI customize the content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and polishing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as corporate performance, market trends, and the latest industry news using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the characteristics of corporate customers based on the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal product proposals based on the analysis results. The polishing unit is realized by the control unit 46A of the smart device 14 and optimizes the layout and content of proposal materials. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and polishing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as corporate performance, market trends, and the latest industry news using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the characteristics of corporate customers based on the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal product proposals based on the analysis results. The polishing unit is realized by the control unit 46A of the smart glasses 214 and optimizes the layout and content of proposal materials. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and brush-up unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data such as corporate performance, market trends, and the latest industry news using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the characteristics of corporate customers based on the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal product proposals based on the analysis results. The brush-up unit is realized by the control unit 46A of the headset terminal 314 and optimizes the layout and content of proposal materials. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and brush-up unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as corporate performance, market trends, and the latest industry news using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the characteristics of corporate customers based on the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal product proposals based on the analysis results. The brush-up unit is realized by the control unit 46A of the robot 414 and optimizes the layout and content of proposal materials.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of suggestions can be delayed to reduce the burden on the user. Also, if the user is relaxed, the timing of suggestions can be advanced to provide prompt suggestions. Furthermore, if the user is in a hurry, important suggestions can be given priority. In this way, more effective suggestions can be made by adjusting the timing of suggestions according to the user's emotions.

[0106] When collecting data, the collection department can prioritize collecting relevant data by taking into account the skill sets of the company's employees. For example, if a company's employees have specific technical skills, the collection department can prioritize collecting market trend data related to those skills. The collection department can also collect the latest industry news and technology reports based on the employees' skill sets. Furthermore, the collection department can filter and collect company performance data according to the employees' skill sets. This allows the collection of necessary data based on the skill sets of the company's employees.

[0107] The analysis unit can also estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible graph or chart can be provided. Alternatively, if the user is relaxed, a complex graph or chart containing detailed data can be provided. Furthermore, if the user is in a hurry, a concise visual presentation that focuses on the main points can be provided. In this way, by adjusting the visual presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0108] The suggestion unit can also improve the accuracy of suggestions by referring to the user's past purchase history when making suggestions. For example, similar products can be suggested based on data on products the user has previously purchased. The suggestion unit can also analyze the user's past purchase history to suggest products that suit the user's preferences and needs. Furthermore, the suggestion unit can also adjust the timing and method of suggestions by referring to the user's past purchase history. In this way, the accuracy of suggestions can be improved by referring to the user's past purchase history.

[0109] The brush-up unit can estimate the user's emotions when brushing up the proposal materials and adjust the colors used based on the estimated user's emotions. For example, if the user is nervous, subdued colors can be used to reduce visual strain. If the user is relaxed, bright colors can be used to attract visual interest. Furthermore, if the user is in a hurry, high-contrast colors can be used to highlight important information. In this way, more effective proposal materials can be created by adjusting the colors used according to the user's emotions.

[0110] When collecting data, the collection department can also prioritize the collection of relevant data by taking into account the company's supply chain information. For example, data on a company's supply chain can be collected to evaluate the efficiency and risks of the supply chain. It can also collect performance data on supply chain partner companies to understand the performance of the entire supply chain. Furthermore, it can collect market trend data related to the supply chain and use it to optimize the supply chain. This makes it possible to collect necessary data by taking into account the company's supply chain information.

[0111] The analysis unit can also estimate the user's emotions and adjust the interactivity of the analysis based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, an interactive interface that allows detailed data to be explored can be provided. Furthermore, if the user is in a hurry, an interface that allows quick access to necessary information can be provided. In this way, by adjusting the interactivity of the analysis according to the user's emotions, an analysis environment that is easy for the user to use can be provided.

[0112] When making a proposal, the proposal unit can also customize the proposal content by taking into account regulations and laws specific to the user's industry. For example, the proposal unit can research regulations and laws related to the user's industry and adjust the proposal content based on that. The proposal unit can also propose products that comply with industry-specific regulations. Furthermore, the proposal content can be updated to reflect the latest information on legal amendments in the user's industry. This allows for more appropriate proposals by taking into account regulations and laws specific to the user's industry.

[0113] The brush-up unit can estimate the user's emotions when brushing up proposal materials and adjust the font style based on the estimated user emotions. For example, if the user is nervous, an easy-to-read font style can be used to reduce visual strain. If the user is relaxed, a stylish font style can be used to add visual interest. Furthermore, if the user is in a hurry, bold or italic fonts can be used to emphasize important information. This allows for the creation of more effective proposal materials by adjusting the font style according to the user's emotions.

[0114] When collecting data, the collection department can prioritize collecting relevant data by taking into account information from the company's ecosystem partners. For example, the collection department can collect performance data from the company's ecosystem partners to evaluate the performance of the entire ecosystem. The collection department can also collect market trend data from the ecosystem partners to identify growth opportunities for the ecosystem. Furthermore, the collection department can collect technological innovation information from the ecosystem partners to enhance the competitiveness of the ecosystem. This allows the collection of necessary data by taking into account information from the company's ecosystem partners.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection department collects data. This data can include numerical data, text data, and image data. The collection department collects data such as company performance, market trends, and the latest industry news. It can also collect industry reports and news articles from reliable sources. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the characteristics of corporate customers based on the collected data. The analysis unit can also analyze the data using generative AI. Step 3: The proposal unit makes product proposals based on the analysis results obtained by the analysis unit. Product proposals are made based on the type of product to be proposed and the proposal criteria. For example, the proposal unit makes optimal product proposals based on the characteristics of corporate customers. Product proposals can also be made using generative AI. Step 4: The polishing department polishes the proposal materials based on the content proposed by the proposal department. The polishing is done by adjusting the layout and modifying the content. For example, the polishing department optimizes the layout and content of the proposal materials. It is also possible to polish the proposal materials using generative AI.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes products based on the analysis results obtained by the analysis unit; a brush-up unit that brushes up the proposal materials based on the content proposed by the proposal unit. A system characterized by:

2. The collecting unit Collect data on company performance, market trends, and the latest industry news 2. The system of claim 1.

3. The analysis unit Analyze the characteristics of corporate customers based on collected data 2. The system of claim 1.

4. The proposal unit Propose products based on the characteristics of corporate customers 2. The system of claim 1.

5. The brush-up unit Adjust the layout and content of the proposal materials 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze the company's past performance data and determine the priorities of the data to be collected 2. The system of claim 1.

8. The collecting unit As data is collected, filtering is performed based on the company's current projects and areas of interest.

2. The system of claim 1.

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