system

Abstract

The system according to this embodiment aims to efficiently support employee skill development by automatically generating sales training materials and sales support tools. [Solution] The system according to the embodiment comprises a generation unit, a collection unit, and a reflection unit. The generation unit automatically generates sales training materials and sales support tools. The collection unit collects daily sales data and customer feedback. The reflection unit reflects the data collected by the collection unit into the training tools.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the creation of sales training materials and sales support tools depends on past experience values, and there is a problem that it is difficult to efficiently support skill improvement.

[0005] The system according to the embodiment aims to automatically generate sales training materials and sales support tools and efficiently support the skill improvement of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a collection unit, and a reflection unit. The generation unit automatically generates sales training materials and sales support tools. The collection unit collects daily sales data and customer feedback. The reflection unit reflects the data collected by the collection unit into the training tools. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate sales training materials and sales support tools, and efficiently support the skill development of employees. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system that efficiently supports employee skill development by automatically generating in-house training and educational materials for products to be promoted using AI. This educational support system automatically generates sales training materials and sales support tools, and collects daily sales data and customer feedback in real time, reflecting it in the educational tools. For example, the educational support system automatically generates sales training materials and sales support tools. In this process, it generates materials based on the latest market needs, competitor analysis, and trend predictions, rather than materials based on past experience. For example, the AI ​​automatically creates materials that include the features of new products, sales strategies, and comparisons with competing products. Next, after release, the educational support system collects daily sales data and customer feedback in real time and reflects it in the educational tools. For example, it analyzes how and in which regions a particular product is selling based on sales data, and updates the educational materials based on the results. It also collects customer feedback and reflects product improvements and new needs to provide educational materials that are more in line with reality. Furthermore, the educational support system provides content that is in line with reality by standardizing the necessary information while also taking into account specific factors such as sales areas. For example, while providing standardized basic sales strategies and product descriptions nationwide, the system adds customized information tailored to the specific characteristics and needs of each region. This enables effective sales support that is aligned with the realities of each region. As a result, the training support system efficiently supports employee skill development and improves the effectiveness of sales activities. For instance, new employees can acquire product knowledge in a short period and become productive immediately. Existing employees can also stay informed about the latest market trends and competitor information, enabling them to conduct effective sales activities. In this way, the training support system efficiently supports employee skill development and improves the effectiveness of sales activities.

[0029] The educational support system according to this embodiment comprises a generation unit, a collection unit, and a reflection unit. The generation unit automatically generates sales training materials and sales support tools. The generation unit automatically generates sales training materials and sales support tools using, for example, AI. The generation unit generates materials based on the latest market needs, competitive analysis, and trend forecasts. For example, the generation unit automatically creates materials that include the features and sales strategies of new products and comparisons of competing products. The generation unit analyzes market research reports, competitor trends, and consumer trends using, for example, AI, and generates materials based on these. The collection unit collects daily sales data and customer feedback. The collection unit collects, for example, sales data and customer feedback in real time. The collection unit collects, for example, sales figures, sales by product, customer comments, and survey results. The collection unit automatically collects sales data and customer feedback using, for example, AI, and stores it in a database. The reflection unit reflects the data collected by the collection unit into educational tools. The reflection unit updates educational tools based on the collected data. The data reflection unit updates training manuals, presentation materials, and sales guides based on collected data, for example. The data reflection unit also analyzes collected data using AI and incorporates the findings into educational tools. As a result, the educational support system according to this embodiment can automatically generate sales training materials and sales support tools, and efficiently support employee skill development by incorporating daily sales data and customer feedback.

[0030] The generation unit automatically generates sales training materials and sales support tools. For example, it uses AI to automatically generate sales training materials and sales support tools. Specifically, the generation unit utilizes natural language processing (NLP) technology to analyze vast amounts of text data and create sales training materials and sales support tools. For example, the generation unit generates materials based on the latest market needs, competitive analysis, and trend forecasts. To understand market needs, it collects data from online review sites, social media, and industry reports, and the AI ​​analyzes this data to extract trends. For competitive analysis, it collects product information and sales strategies of competitors, and the AI ​​compares and analyzes this information. For trend forecasting, it uses time-series analysis based on historical data and machine learning models to predict future market trends. The generation unit automatically creates materials that include, for example, the features of new products, sales strategies, and comparisons of competing products. Regarding new product features, it analyzes product specifications and user reviews, extracts key points, and reflects them in the materials. Regarding sales strategies, it proposes the optimal strategy based on past success stories and market reactions. In comparing competing products, the advantages and disadvantages of each product are clearly identified based on data such as price, features, and user reviews. The generation unit, for example, uses AI to analyze market research reports, competitor trends, and consumer trends, and generates materials based on this analysis. Market research reports include not only quantitative data but also qualitative insights, providing a comprehensive analysis. Regarding competitor trends, information from news articles, press releases, and industry events is collected, and AI analyzes this information to understand the trends. For consumer trends, posts on social media and online forums are analyzed to capture changes in consumer interests and preferences. As a result, the generation unit can quickly generate high-quality materials based on the latest information, effectively supporting sales training and support activities.

[0031] The data collection department collects daily sales data and customer feedback. For example, it collects sales data and customer feedback in real time. Specifically, it collects sales figures, sales by product, customer comments, and survey results from POS systems and online sales platforms. It obtains real-time sales data from stores using POS systems, and sales data from websites and apps using online sales platforms. For customer comments and survey results, it collects data from online review sites, social media, and customer survey forms. The data collection department uses AI to automatically collect sales data and customer feedback and store it in a database. The AI ​​automates the data collection process and performs filtering and cleansing to maintain data integrity and consistency. For example, it detects outliers and missing values ​​in sales data and processes them appropriately. Furthermore, it uses natural language processing technology to analyze text data from customer comments and survey results, classifying positive and negative feedback. This allows the data collection department to efficiently collect vast amounts of data and store it in a database in real time. In addition, the data collection department visualizes the collected data, making it easily accessible to administrators and staff. For example, a dashboard can be used to display sales trends and customer feedback in graphs and charts for intuitive understanding. This allows the data collection unit to streamline data collection and management, improving the overall system performance.

[0032] The Reflection Department incorporates data collected by the Collection Department into educational tools. For example, the Reflection Department updates educational tools based on the collected data. Specifically, it analyzes collected sales data and customer feedback to update training manuals, presentation materials, and sales guides. For instance, it analyzes how specific products are selling in which regions based on sales data and reflects the results in the training manual. Regarding customer feedback, it highlights positive feedback as success stories and incorporates negative feedback as areas for improvement into the educational tools. The Reflection Department also analyzes collected data using AI and incorporates it into educational tools. The AI ​​analyzes data patterns and trends and proposes optimal educational content. For example, it performs time-series analysis of sales data to understand seasonal sales fluctuations and updates sales strategies accordingly. It also analyzes customer feedback using text mining technology, extracts frequently occurring keywords and phrases, and incorporates them into the educational tools. This allows the Reflection Department to provide high-quality educational tools based on the latest data and efficiently support employee skill development. Furthermore, the Reflection Department evaluates the effectiveness of the educational tools and builds a feedback loop for continuous improvement. For example, the company monitors the usage and learning outcomes of educational tools and reviews the content as needed. It also collects feedback from employees to identify areas for improvement in the educational tools. This allows the feedback department to consistently provide high-quality educational tools based on the latest information, efficiently supporting employee skill development.

[0033] The educational support system includes a customization unit that customizes the system taking into account the characteristics of each sales area. The customization unit customizes the system taking into account the characteristics of each sales area. For example, the customization unit considers regional purchasing trends, competitive situations, and cultural backgrounds when performing customization. For example, the customization unit uses AI to analyze the characteristics of each region and performs customization based on that analysis. For example, the customization unit uses regional sales data and customer feedback when performing customization. This allows the system to provide content that is in line with reality by customizing the system while taking into account the characteristics of each sales area. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input regional sales data into the AI ​​and have the AI ​​perform the process of analyzing the characteristics of each region.

[0034] The educational support system includes a provisioning unit that provides basic sales strategies and product descriptions. The provisioning unit provides basic sales strategies and product descriptions. For example, the provisioning unit provides sales processes, product features, pricing, etc. The provisioning unit can, for example, automatically generate and provide basic sales strategies and product descriptions using AI. The provisioning unit can, for example, uniformly provide basic sales strategies and product descriptions that are common nationwide. This makes it possible to provide nationwide common information by providing basic sales strategies and product descriptions. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input basic sales strategies and product descriptions into AI and have the AI ​​perform the information provision.

[0035] The generation unit generates materials based on the latest market needs, competitive analysis, and trend forecasts. For example, the generation unit uses AI to analyze market research reports, competitor trends, and consumer trends, and generates materials based on that. For example, the generation unit analyzes the latest market needs and generates sales training materials and sales support tools based on that. For example, the generation unit analyzes competitor trends and generates materials based on that. For example, the generation unit performs trend forecasts and generates materials based on those forecasts. By generating materials based on the latest market needs, competitive analysis, and trend forecasts, it is possible to provide more effective sales training materials and sales support tools. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input market research reports, competitor trends, and consumer trends into a generation AI and have the generation AI perform the material generation.

[0036] The data collection unit collects sales data and customer feedback in real time. For example, the data collection unit collects sales figures, sales by product, customer comments, and survey results in real time. The data collection unit can, for example, use AI to automatically collect sales data and customer feedback and store it in a database. The data collection unit can, for example, collect sales data in real time and incorporate it into educational tools. The data collection unit can, for example, collect customer feedback in real time and incorporate it into educational tools. This allows for the provision of educational tools that reflect the latest information by collecting sales data and customer feedback in real time. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input sales data and customer feedback into AI and have the AI ​​perform the data collection.

[0037] The reflection unit updates educational tools based on collected data. For example, the reflection unit updates training manuals, presentation materials, and sales guides based on collected data. For example, the reflection unit uses AI to analyze collected data and reflect it in the educational tools. For example, the reflection unit periodically updates the educational tools based on collected data. For example, the reflection unit updates the educational tools based on user feedback. This allows for the provision of educational materials that are relevant to the actual situation by updating the educational tools based on collected data. Some or all of the above processes in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input collected data into AI and have the AI ​​perform the updates of the educational tools.

[0038] The generation unit analyzes past sales data and reflects the optimal sales strategy when generating materials. For example, the generation unit analyzes how and in which regions a particular product is selling based on past sales data and generates materials based on the results. For example, the generation unit generates materials that reflect seasonal sales trends based on past sales data. For example, the generation unit analyzes past sales data and generates materials that reflect the optimal sales strategy for a specific customer segment. By analyzing past sales data and reflecting the optimal sales strategy, more effective materials can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input past sales data into a generation AI and have the generation AI perform the reflection of the sales strategy.

[0039] The generation unit generates different materials based on specific sales scenarios during material generation. For example, the generation unit generates materials based on a sales scenario aligned with the launch of a new product. For example, the generation unit generates materials based on a sales scenario aligned with a specific campaign. For example, the generation unit generates materials based on a sales scenario for a specific customer segment. By generating different materials based on specific sales scenarios, more effective sales support becomes possible. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a specific sales scenario into a generation AI and have the generation AI perform the material generation.

[0040] The generation unit customizes materials based on specific sales campaigns when generating them. For example, the generation unit generates materials tailored to a specific seasonal campaign. For example, the generation unit generates materials tailored to a specific promotional event. For example, the generation unit generates materials tailored to a specific region-specific campaign. By customizing materials based on specific sales campaigns, more effective sales support becomes possible. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a specific sales campaign into the generation AI and have the generation AI perform the material customization.

[0041] The generation unit reflects the actions of competitors in real time when generating materials. For example, the generation unit generates materials based on information about new product launches by competitors. For example, the generation unit generates materials based on information about price changes by competitors. For example, the generation unit analyzes the marketing strategies of competitors and generates materials based on the results. This allows for more effective sales support by reflecting the actions of competitors in real time. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the actions of competitors into a generation AI and have the generation AI perform the material generation.

[0042] The data collection unit prioritizes data collection based on specific sales events. For example, the data collection unit collects data in conjunction with a new product launch event. For example, the data collection unit collects data in conjunction with a specific promotional event. For example, the data collection unit collects data in conjunction with a specific region-specific event. This allows for more effective data collection by prioritizing data collection based on specific sales events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input specific sales events into the AI ​​and have the AI ​​perform the priority collection of data.

[0043] The data collection unit analyzes past data collection history and selects the optimal collection method when collecting data. For example, the data collection unit selects the most effective collection method from past data collection history. For example, the data collection unit collects data at specific time periods based on past data collection history. For example, the data collection unit analyzes past data collection history and determines the optimal collection frequency. By analyzing past data collection history and selecting the optimal collection method, more effective data collection becomes possible. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0044] The data collection unit prioritizes collecting sales data from specific regions during data collection. For example, the data collection unit prioritizes collecting sales data from specific regions. For example, the data collection unit prioritizes collecting customer feedback from specific regions. For example, the data collection unit prioritizes collecting competitor activity from specific regions. By prioritizing the collection of sales data from specific regions, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sales data from specific regions into AI and have the AI ​​perform priority data collection.

[0045] The data collection unit collects feedback from social media when collecting data. For example, the data collection unit collects customer voices on social media. For example, the data collection unit collects trends on social media. For example, the data collection unit collects the activities of competitors on social media. By collecting feedback from social media, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data from social media into AI and have AI perform the collection of feedback.

[0046] The reflection unit customizes the content of educational tools based on specific sales data when updating them. For example, the reflection unit updates educational tools based on sales data for a specific product. For example, the reflection unit updates educational tools based on sales data for a specific region. For example, the reflection unit updates educational tools based on sales data for a specific customer segment. This allows for the provision of more effective educational tools by customizing the content based on specific sales data. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input specific sales data into AI and have AI perform the content customization.

[0047] The update unit analyzes past update history and selects the optimal update method when updating educational tools. For example, the update unit selects the most effective update method from past update history. For example, the update unit updates educational tools at a specific time based on past update history. For example, the update unit analyzes past update history and determines the optimal update frequency. By analyzing past update history and selecting the optimal update method, more effective educational tools can be provided. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update history into AI and have the AI ​​select the optimal update method.

[0048] The implementation unit customizes the content of educational tools based on specific sales campaigns when updating them. For example, the implementation unit updates educational tools to match a specific seasonal campaign. For example, the implementation unit updates educational tools to match a specific promotional event. For example, the implementation unit updates educational tools to match a specific region-specific campaign. This allows for the provision of more effective educational tools by customizing the content based on specific sales campaigns. Some or all of the above processes in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can input a specific sales campaign into AI and have AI perform the content customization.

[0049] The reflection unit reflects the actions of competitors in real time when updating educational tools. For example, the reflection unit updates educational tools based on information about new product launches by competitors. For example, the reflection unit updates educational tools based on information about price changes by competitors. For example, the reflection unit analyzes the marketing strategies of competitors and updates educational tools based on the results. This allows for the provision of more effective educational tools by reflecting the actions of competitors in real time. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input competitor actions into AI and have the AI ​​perform the update of the educational tools.

[0050] The customization unit adjusts the content based on sales data for a specific region during the customization process. For example, the customization unit performs customization based on sales data for a specific region. For example, the customization unit performs customization based on customer feedback in a specific region. For example, the customization unit performs customization based on the activities of competitors in a specific region. By adjusting the content based on sales data for a specific region, more effective customization becomes possible. Some or all of the above processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input sales data for a specific region into AI and have the AI ​​perform the content adjustments.

[0051] The customization unit adjusts the content based on a specific sales campaign during the customization process. For example, the customization unit can customize for a specific seasonal campaign. For example, the customization unit can customize for a specific promotional event. For example, the customization unit can customize for a specific region-specific campaign. By adjusting the content based on a specific sales campaign, more effective customization becomes possible. Some or all of the above processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input a specific sales campaign into the AI ​​and have the AI ​​perform the content adjustments.

[0052] The information provider customizes the content based on specific sales data when providing information. For example, the information provider provides information based on sales data for a specific product. For example, the information provider provides information based on sales data for a specific region. For example, the information provider provides information based on sales data for a specific customer segment. By customizing the content based on specific sales data, more effective information provision becomes possible. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input specific sales data into AI and have the AI ​​perform the content customization.

[0053] The information provider customizes the content based on specific sales campaigns when providing information. For example, the provider provides information tailored to a specific seasonal campaign. For example, the provider provides information tailored to a specific promotional event. For example, the provider provides information tailored to a specific region-specific campaign. By customizing the content based on specific sales campaigns, more effective information provision becomes possible. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input a specific sales campaign into AI and have the AI ​​perform the content customization.

[0054] The information provision department will reflect the actions of competitors in real time when providing information. For example, the information provision department will provide information based on information about new product launches by competitors. For example, the information provision department will provide information based on information about price changes by competitors. For example, the information provision department will analyze the marketing strategies of competitors and provide information based on the results. This will enable more effective information provision by reflecting the actions of competitors in real time. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department may input the actions of competitors into AI and have AI perform the information provision.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The educational support system can also include a progress tracking unit that tracks users' learning progress. For example, the progress tracking unit can monitor each employee's learning progress in real time and provide appropriate feedback based on their progress. It can also evaluate the degree of achievement towards specific learning objectives and suggest the next learning steps based on that achievement. Furthermore, it can provide additional support materials or training to employees who are falling behind. This enables personalized support tailored to each employee's learning progress, resulting in more effective educational support.

[0057] The educational support system may also include a learning style analysis unit that analyzes the user's learning style. For example, the learning style analysis unit might analyze whether the user prefers visual or text-based information. It might also analyze whether the user is a short-term, focused learner or a long-term, in-depth learner. Furthermore, it might analyze whether the user prefers interactive or passive learning. This allows for the provision of optimal educational materials tailored to the user's learning style, maximizing learning effectiveness.

[0058] The educational support system can also include a learning path suggestion unit that analyzes the user's learning history and proposes the optimal learning path. For example, the learning path suggestion unit analyzes the user's strengths based on past learning history. It can also identify areas where the user struggles and propose a learning path that focuses on those areas. Furthermore, it can propose an optimal learning path tailored to the user's career goals. This allows for the provision of learning paths that meet the user's individual needs, maximizing learning effectiveness.

[0059] The educational support system may also include an evaluation unit that assesses the user's learning outcomes. The evaluation unit may, for example, administer tests on the user's learned material and evaluate the results. It may also provide feedback based on the user's learning progress. Furthermore, it may compare the user's learning outcomes with those of other employees and provide a relative evaluation. This allows for an objective assessment of the user's learning outcomes and confirmation of the effectiveness of their learning.

[0060] The educational support system may also include an environment optimization unit that further optimizes the user's learning environment. For example, the environment optimization unit might reduce ambient noise to help the user concentrate on learning. It might also provide appropriate music to help the user relax. Furthermore, it might display a message prompting the user to take a break if they become tired. By optimizing the user's learning environment in this way, learning effectiveness can be improved.

[0061] The educational support system can also include a sharing section for sharing users' learning outcomes. This sharing section could, for example, allow users to share their achieved learning outcomes with other employees. It could also, for example, post users' learning outcomes on an internal bulletin board. Furthermore, it could allow users to report their learning outcomes to their supervisors. By sharing users' learning outcomes, this can increase motivation and improve learning effectiveness.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The generation unit automatically generates sales training materials and sales support tools. The generation unit uses AI to generate materials based on the latest market needs, competitor analysis, and trend forecasts. For example, it automatically creates materials that include the features of new products, sales strategies, and comparisons with competing products. Step 2: The data collection unit collects daily sales data and customer feedback. The data collection unit collects sales figures, sales by product, customer comments, survey results, etc., in real time and stores them in a database. The data collection unit uses AI to automatically collect this data. Step 3: The implementation unit incorporates the data collected by the collection unit into the educational tools. The implementation unit updates training manuals, presentation materials, sales guides, etc., based on the collected data. The implementation unit uses AI to analyze the collected data and incorporates it into the educational tools.

[0064] (Example of form 2) The educational support system according to an embodiment of the present invention is a system that efficiently supports employee skill development by automatically generating in-house training and educational materials for products to be promoted using AI. This educational support system automatically generates sales training materials and sales support tools, and collects daily sales data and customer feedback in real time, reflecting it in the educational tools. For example, the educational support system automatically generates sales training materials and sales support tools. In this process, it generates materials based on the latest market needs, competitor analysis, and trend predictions, rather than materials based on past experience. For example, the AI ​​automatically creates materials that include the features of new products, sales strategies, and comparisons with competing products. Next, after release, the educational support system collects daily sales data and customer feedback in real time and reflects it in the educational tools. For example, it analyzes how and in which regions a particular product is selling based on sales data, and updates the educational materials based on the results. It also collects customer feedback and reflects product improvements and new needs to provide educational materials that are more in line with reality. Furthermore, the educational support system provides content that is in line with reality by standardizing the necessary information while also taking into account specific factors such as sales areas. For example, while providing standardized basic sales strategies and product descriptions nationwide, the system adds customized information tailored to the specific characteristics and needs of each region. This enables effective sales support that is aligned with the realities of each region. As a result, the training support system efficiently supports employee skill development and improves the effectiveness of sales activities. For instance, new employees can acquire product knowledge in a short period and become productive immediately. Existing employees can also stay informed about the latest market trends and competitor information, enabling them to conduct effective sales activities. In this way, the training support system efficiently supports employee skill development and improves the effectiveness of sales activities.

[0065] The educational support system according to this embodiment comprises a generation unit, a collection unit, and a reflection unit. The generation unit automatically generates sales training materials and sales support tools. The generation unit automatically generates sales training materials and sales support tools using, for example, AI. The generation unit generates materials based on the latest market needs, competitive analysis, and trend forecasts. For example, the generation unit automatically creates materials that include the features and sales strategies of new products and comparisons of competing products. The generation unit analyzes market research reports, competitor trends, and consumer trends using, for example, AI, and generates materials based on these. The collection unit collects daily sales data and customer feedback. The collection unit collects, for example, sales data and customer feedback in real time. The collection unit collects, for example, sales figures, sales by product, customer comments, and survey results. The collection unit automatically collects sales data and customer feedback using, for example, AI, and stores it in a database. The reflection unit reflects the data collected by the collection unit into educational tools. The reflection unit updates educational tools based on the collected data. The data reflection unit updates training manuals, presentation materials, and sales guides based on collected data, for example. The data reflection unit also analyzes collected data using AI and incorporates the findings into educational tools. As a result, the educational support system according to this embodiment can automatically generate sales training materials and sales support tools, and efficiently support employee skill development by incorporating daily sales data and customer feedback.

[0066] The generation unit automatically generates sales training materials and sales support tools. For example, it uses AI to automatically generate sales training materials and sales support tools. Specifically, the generation unit utilizes natural language processing (NLP) technology to analyze vast amounts of text data and create sales training materials and sales support tools. For example, the generation unit generates materials based on the latest market needs, competitive analysis, and trend forecasts. To understand market needs, it collects data from online review sites, social media, and industry reports, and the AI ​​analyzes this data to extract trends. For competitive analysis, it collects product information and sales strategies of competitors, and the AI ​​compares and analyzes this information. For trend forecasting, it uses time-series analysis based on historical data and machine learning models to predict future market trends. The generation unit automatically creates materials that include, for example, the features of new products, sales strategies, and comparisons of competing products. Regarding new product features, it analyzes product specifications and user reviews, extracts key points, and reflects them in the materials. Regarding sales strategies, it proposes the optimal strategy based on past success stories and market reactions. In comparing competing products, the advantages and disadvantages of each product are clearly identified based on data such as price, features, and user reviews. The generation unit, for example, uses AI to analyze market research reports, competitor trends, and consumer trends, and generates materials based on this analysis. Market research reports include not only quantitative data but also qualitative insights, providing a comprehensive analysis. Regarding competitor trends, information from news articles, press releases, and industry events is collected, and AI analyzes this information to understand the trends. For consumer trends, posts on social media and online forums are analyzed to capture changes in consumer interests and preferences. As a result, the generation unit can quickly generate high-quality materials based on the latest information, effectively supporting sales training and support activities.

[0067] The data collection department collects daily sales data and customer feedback. For example, it collects sales data and customer feedback in real time. Specifically, it collects sales figures, sales by product, customer comments, and survey results from POS systems and online sales platforms. It obtains real-time sales data from stores using POS systems, and sales data from websites and apps using online sales platforms. For customer comments and survey results, it collects data from online review sites, social media, and customer survey forms. The data collection department uses AI to automatically collect sales data and customer feedback and store it in a database. The AI ​​automates the data collection process and performs filtering and cleansing to maintain data integrity and consistency. For example, it detects outliers and missing values ​​in sales data and processes them appropriately. Furthermore, it uses natural language processing technology to analyze text data from customer comments and survey results, classifying positive and negative feedback. This allows the data collection department to efficiently collect vast amounts of data and store it in a database in real time. In addition, the data collection department visualizes the collected data, making it easily accessible to administrators and staff. For example, a dashboard can be used to display sales trends and customer feedback in graphs and charts for intuitive understanding. This allows the data collection unit to streamline data collection and management, improving the overall system performance.

[0068] The Reflection Department incorporates data collected by the Collection Department into educational tools. For example, the Reflection Department updates educational tools based on the collected data. Specifically, it analyzes collected sales data and customer feedback to update training manuals, presentation materials, and sales guides. For instance, it analyzes how specific products are selling in which regions based on sales data and reflects the results in the training manual. Regarding customer feedback, it highlights positive feedback as success stories and incorporates negative feedback as areas for improvement into the educational tools. The Reflection Department also analyzes collected data using AI and incorporates it into educational tools. The AI ​​analyzes data patterns and trends and proposes optimal educational content. For example, it performs time-series analysis of sales data to understand seasonal sales fluctuations and updates sales strategies accordingly. It also analyzes customer feedback using text mining technology, extracts frequently occurring keywords and phrases, and incorporates them into the educational tools. This allows the Reflection Department to provide high-quality educational tools based on the latest data and efficiently support employee skill development. Furthermore, the Reflection Department evaluates the effectiveness of the educational tools and builds a feedback loop for continuous improvement. For example, the company monitors the usage and learning outcomes of educational tools and reviews the content as needed. It also collects feedback from employees to identify areas for improvement in the educational tools. This allows the feedback department to consistently provide high-quality educational tools based on the latest information, efficiently supporting employee skill development.

[0069] The educational support system includes a customization unit that customizes the system taking into account the characteristics of each sales area. The customization unit customizes the system taking into account the characteristics of each sales area. For example, the customization unit considers regional purchasing trends, competitive situations, and cultural backgrounds when performing customization. For example, the customization unit uses AI to analyze the characteristics of each region and performs customization based on that analysis. For example, the customization unit uses regional sales data and customer feedback when performing customization. This allows the system to provide content that is in line with reality by customizing the system while taking into account the characteristics of each sales area. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input regional sales data into the AI ​​and have the AI ​​perform the process of analyzing the characteristics of each region.

[0070] The educational support system includes a provisioning unit that provides basic sales strategies and product descriptions. The provisioning unit provides basic sales strategies and product descriptions. For example, the provisioning unit provides sales processes, product features, pricing, etc. The provisioning unit can, for example, automatically generate and provide basic sales strategies and product descriptions using AI. The provisioning unit can, for example, uniformly provide basic sales strategies and product descriptions that are common nationwide. This makes it possible to provide nationwide common information by providing basic sales strategies and product descriptions. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input basic sales strategies and product descriptions into AI and have the AI ​​perform the information provision.

[0071] The generation unit generates materials based on the latest market needs, competitive analysis, and trend forecasts. For example, the generation unit uses AI to analyze market research reports, competitor trends, and consumer trends, and generates materials based on that. For example, the generation unit analyzes the latest market needs and generates sales training materials and sales support tools based on that. For example, the generation unit analyzes competitor trends and generates materials based on that. For example, the generation unit performs trend forecasts and generates materials based on those forecasts. By generating materials based on the latest market needs, competitive analysis, and trend forecasts, it is possible to provide more effective sales training materials and sales support tools. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input market research reports, competitor trends, and consumer trends into a generation AI and have the generation AI perform the material generation.

[0072] The data collection unit collects sales data and customer feedback in real time. For example, the data collection unit collects sales figures, sales by product, customer comments, and survey results in real time. The data collection unit can, for example, use AI to automatically collect sales data and customer feedback and store it in a database. The data collection unit can, for example, collect sales data in real time and incorporate it into educational tools. The data collection unit can, for example, collect customer feedback in real time and incorporate it into educational tools. This allows for the provision of educational tools that reflect the latest information by collecting sales data and customer feedback in real time. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input sales data and customer feedback into AI and have the AI ​​perform the data collection.

[0073] The reflection unit updates educational tools based on collected data. For example, the reflection unit updates training manuals, presentation materials, and sales guides based on collected data. For example, the reflection unit uses AI to analyze collected data and reflect it in the educational tools. For example, the reflection unit periodically updates the educational tools based on collected data. For example, the reflection unit updates the educational tools based on user feedback. This allows for the provision of educational materials that are relevant to the actual situation by updating the educational tools based on collected data. Some or all of the above processes in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input collected data into AI and have the AI ​​perform the updates of the educational tools.

[0074] The generation unit estimates the user's emotions and adjusts the presentation of the materials based on the estimated emotions. For example, if the user is stressed, the generation unit generates simple and visually easy-to-understand materials. If the user is relaxed, the generation unit generates materials containing detailed information. If the user is excited, the generation unit generates materials incorporating a visually stimulating design. This allows for the provision of more effective materials by adjusting the presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the presentation of the materials.

[0075] The generation unit analyzes past sales data and reflects the optimal sales strategy when generating materials. For example, the generation unit analyzes how and in which regions a particular product is selling based on past sales data and generates materials based on the results. For example, the generation unit generates materials that reflect seasonal sales trends based on past sales data. For example, the generation unit analyzes past sales data and generates materials that reflect the optimal sales strategy for a specific customer segment. By analyzing past sales data and reflecting the optimal sales strategy, more effective materials can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input past sales data into a generation AI and have the generation AI perform the reflection of the sales strategy.

[0076] The generation unit generates different materials based on specific sales scenarios during material generation. For example, the generation unit generates materials based on a sales scenario aligned with the launch of a new product. For example, the generation unit generates materials based on a sales scenario aligned with a specific campaign. For example, the generation unit generates materials based on a sales scenario for a specific customer segment. By generating different materials based on specific sales scenarios, more effective sales support becomes possible. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a specific sales scenario into a generation AI and have the generation AI perform the material generation.

[0077] The generation unit estimates the user's emotions and adjusts the length of the material based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise document. If the user is relaxed, the generation unit generates a longer document with detailed explanations. If the user is excited, the generation unit generates a document with visually stimulating effects. By adjusting the length of the material according to the user's emotions, more effective materials can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the material.

[0078] The generation unit customizes materials based on specific sales campaigns when generating them. For example, the generation unit generates materials tailored to a specific seasonal campaign. For example, the generation unit generates materials tailored to a specific promotional event. For example, the generation unit generates materials tailored to a specific region-specific campaign. By customizing materials based on specific sales campaigns, more effective sales support becomes possible. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a specific sales campaign into the generation AI and have the generation AI perform the material customization.

[0079] The generation unit reflects the actions of competitors in real time when generating materials. For example, the generation unit generates materials based on information about new product launches by competitors. For example, the generation unit generates materials based on information about price changes by competitors. For example, the generation unit analyzes the marketing strategies of competitors and generates materials based on the results. This allows for more effective sales support by reflecting the actions of competitors in real time. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the actions of competitors into a generation AI and have the generation AI perform the material generation.

[0080] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection. For example, if the user is relaxed, the data collection unit increases the frequency of data collection. For example, if the user is excited, the data collection unit collects data in real time. This allows for more effective data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0081] The data collection unit prioritizes data collection based on specific sales events. For example, the data collection unit collects data in conjunction with a new product launch event. For example, the data collection unit collects data in conjunction with a specific promotional event. For example, the data collection unit collects data in conjunction with a specific region-specific event. This allows for more effective data collection by prioritizing data collection based on specific sales events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input specific sales events into the AI ​​and have the AI ​​perform the priority collection of data.

[0082] The data collection unit analyzes past data collection history and selects the optimal collection method when collecting data. For example, the data collection unit selects the most effective collection method from past data collection history. For example, the data collection unit collects data at specific time periods based on past data collection history. For example, the data collection unit analyzes past data collection history and determines the optimal collection frequency. By analyzing past data collection history and selecting the optimal collection method, more effective data collection becomes possible. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0083] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. If the user is relaxed, the data collection unit prioritizes collecting detailed data. If the user is excited, the data collection unit collects data in real time. This allows for more effective data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of the data.

[0084] The data collection unit prioritizes collecting sales data from specific regions during data collection. For example, the data collection unit prioritizes collecting sales data from specific regions. For example, the data collection unit prioritizes collecting customer feedback from specific regions. For example, the data collection unit prioritizes collecting competitor activity from specific regions. By prioritizing the collection of sales data from specific regions, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sales data from specific regions into AI and have the AI ​​perform priority data collection.

[0085] The data collection unit collects feedback from social media when collecting data. For example, the data collection unit collects customer voices on social media. For example, the data collection unit collects trends on social media. For example, the data collection unit collects the activities of competitors on social media. By collecting feedback from social media, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data from social media into AI and have AI perform the collection of feedback.

[0086] The reflection unit estimates the user's emotions and adjusts the update frequency of the educational tool based on the estimated user emotions. For example, if the user is stressed, the reflection unit reduces the update frequency. For example, if the user is relaxed, the reflection unit increases the update frequency. For example, if the user is excited, the reflection unit updates in real time. This allows for the provision of more effective educational tools by adjusting the update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI or not using AI. For example, the reflection unit can input user emotion data into AI and have the AI ​​adjust the update frequency.

[0087] The reflection unit customizes the content of educational tools based on specific sales data when updating them. For example, the reflection unit updates educational tools based on sales data for a specific product. For example, the reflection unit updates educational tools based on sales data for a specific region. For example, the reflection unit updates educational tools based on sales data for a specific customer segment. This allows for the provision of more effective educational tools by customizing the content based on specific sales data. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input specific sales data into AI and have AI perform the content customization.

[0088] The update unit analyzes past update history and selects the optimal update method when updating educational tools. For example, the update unit selects the most effective update method from past update history. For example, the update unit updates educational tools at a specific time based on past update history. For example, the update unit analyzes past update history and determines the optimal update frequency. By analyzing past update history and selecting the optimal update method, more effective educational tools can be provided. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update history into AI and have the AI ​​select the optimal update method.

[0089] The reflection unit estimates the user's emotions and adjusts the display method of the educational tool based on the estimated user emotions. For example, if the user is nervous, the reflection unit provides a simple and highly visible display method. For example, if the user is relaxed, the reflection unit provides a display method that includes detailed information. For example, if the user is in a hurry, the reflection unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the educational tool according to the user's emotions, a more effective educational tool can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI, for example, or not using AI. For example, the reflection unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0090] The implementation unit customizes the content of educational tools based on specific sales campaigns when updating them. For example, the implementation unit updates educational tools to match a specific seasonal campaign. For example, the implementation unit updates educational tools to match a specific promotional event. For example, the implementation unit updates educational tools to match a specific region-specific campaign. This allows for the provision of more effective educational tools by customizing the content based on specific sales campaigns. Some or all of the above processes in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can input a specific sales campaign into AI and have AI perform the content customization.

[0091] The reflection unit reflects the actions of competitors in real time when updating educational tools. For example, the reflection unit updates educational tools based on information about new product launches by competitors. For example, the reflection unit updates educational tools based on information about price changes by competitors. For example, the reflection unit analyzes the marketing strategies of competitors and updates educational tools based on the results. This allows for the provision of more effective educational tools by reflecting the actions of competitors in real time. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input competitor actions into AI and have the AI ​​perform the update of the educational tools.

[0092] The customization unit estimates the user's emotions and adjusts the customization method based on the estimated emotions. For example, if the user is stressed, the customization unit will perform a simple and visually easy-to-understand customization. For example, if the user is relaxed, the customization unit will perform a customization that includes detailed information. For example, if the user is excited, the customization unit will perform a customization that incorporates a visually stimulating design. This allows for more effective customization by adjusting the customization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into AI and have the AI ​​perform the adjustment of the customization method.

[0093] The customization unit adjusts the content based on sales data for a specific region during the customization process. For example, the customization unit performs customization based on sales data for a specific region. For example, the customization unit performs customization based on customer feedback in a specific region. For example, the customization unit performs customization based on the activities of competitors in a specific region. By adjusting the content based on sales data for a specific region, more effective customization becomes possible. Some or all of the above processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input sales data for a specific region into AI and have the AI ​​perform the content adjustments.

[0094] The customization unit estimates the user's emotions and determines the priority of customizations based on the estimated emotions. For example, if the user is stressed, the customization unit prioritizes only important customizations. For example, if the user is relaxed, the customization unit prioritizes detailed customizations. For example, if the user is excited, the customization unit prioritizes visually stimulating customizations. This allows for more effective customization by determining the priority of customizations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into an AI and have the AI ​​determine the priority of customizations.

[0095] The customization unit adjusts the content based on a specific sales campaign during the customization process. For example, the customization unit can customize for a specific seasonal campaign. For example, the customization unit can customize for a specific promotional event. For example, the customization unit can customize for a specific region-specific campaign. By adjusting the content based on a specific sales campaign, more effective customization becomes possible. Some or all of the above processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input a specific sales campaign into the AI ​​and have the AI ​​perform the content adjustments.

[0096] The information provider estimates the user's emotions and adjusts the presentation of the information based on the estimated emotions. For example, if the user is stressed, the provider provides simple and visually easy-to-understand information. If the user is relaxed, the provider provides information in a way that includes detailed information. If the user is excited, the provider provides information with a visually stimulating design. By adjusting the presentation of information according to the user's emotions, more effective information delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into AI and have the AI ​​adjust the presentation of the information.

[0097] The information provider customizes the content based on specific sales data when providing information. For example, the information provider provides information based on sales data for a specific product. For example, the information provider provides information based on sales data for a specific region. For example, the information provider provides information based on sales data for a specific customer segment. By customizing the content based on specific sales data, more effective information provision becomes possible. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input specific sales data into AI and have the AI ​​perform the content customization.

[0098] The information provider estimates the user's emotions and determines the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the provider will prioritize providing only important information. For example, if the user is relaxed, the provider will prioritize providing detailed information. For example, if the user is excited, the provider will prioritize providing visually stimulating information. This allows for more effective information delivery by prioritizing the information provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into an AI and have the AI ​​determine the priority of information.

[0099] The information provider customizes the content based on specific sales campaigns when providing information. For example, the provider provides information tailored to a specific seasonal campaign. For example, the provider provides information tailored to a specific promotional event. For example, the provider provides information tailored to a specific region-specific campaign. By customizing the content based on specific sales campaigns, more effective information provision becomes possible. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input a specific sales campaign into AI and have the AI ​​perform the content customization.

[0100] The information provision department will reflect the actions of competitors in real time when providing information. For example, the information provision department will provide information based on information about new product launches by competitors. For example, the information provision department will provide information based on information about price changes by competitors. For example, the information provision department will analyze the marketing strategies of competitors and provide information based on the results. This will enable more effective information provision by reflecting the actions of competitors in real time. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department may input the actions of competitors into AI and have AI perform the information provision.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The educational support system can also include a progress tracking unit that tracks users' learning progress. For example, the progress tracking unit can monitor each employee's learning progress in real time and provide appropriate feedback based on their progress. It can also evaluate the degree of achievement towards specific learning objectives and suggest the next learning steps based on that achievement. Furthermore, it can provide additional support materials or training to employees who are falling behind. This enables personalized support tailored to each employee's learning progress, resulting in more effective educational support.

[0103] The educational support system may also include a learning style analysis unit that analyzes the user's learning style. For example, the learning style analysis unit might analyze whether the user prefers visual or text-based information. It might also analyze whether the user is a short-term, focused learner or a long-term, in-depth learner. Furthermore, it might analyze whether the user prefers interactive or passive learning. This allows for the provision of optimal educational materials tailored to the user's learning style, maximizing learning effectiveness.

[0104] The educational support system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the learning content based on those emotions. For example, if the user is feeling stressed, the emotion adjustment unit may provide learning content that helps them relax. For example, if the user is excited, the emotion adjustment unit may provide learning content that enhances their concentration. For example, if the user is tired, the emotion adjustment unit may display a message encouraging them to take a break. This allows for the provision of a learning environment tailored to the user's emotional state, thereby improving learning effectiveness.

[0105] The educational support system can also include a learning path suggestion unit that analyzes the user's learning history and proposes the optimal learning path. For example, the learning path suggestion unit analyzes the user's strengths based on past learning history. It can also identify areas where the user struggles and propose a learning path that focuses on those areas. Furthermore, it can propose an optimal learning path tailored to the user's career goals. This allows for the provision of learning paths that meet the user's individual needs, maximizing learning effectiveness.

[0106] The educational support system can also include a motivation enhancement unit that estimates the user's emotions and increases learning motivation based on those emotions. For example, if the user is losing motivation, the motivation enhancement unit displays an encouraging message. For example, if the user is feeling a sense of accomplishment, the motivation enhancement unit suggests the next challenge. For example, if the user is tired, the motivation enhancement unit suggests a short break for refreshment. This allows the learning motivation to be increased according to the user's emotional state, thereby improving learning effectiveness.

[0107] The educational support system may also include an evaluation unit that assesses the user's learning outcomes. The evaluation unit may, for example, administer tests on the user's learned material and evaluate the results. It may also provide feedback based on the user's learning progress. Furthermore, it may compare the user's learning outcomes with those of other employees and provide a relative evaluation. This allows for an objective assessment of the user's learning outcomes and confirmation of the effectiveness of their learning.

[0108] The educational support system may further include a learning pace adjustment unit that estimates the user's emotions and adjusts the learning pace based on the estimated emotions. For example, if the user is feeling stressed, the learning pace adjustment unit will slow down the learning pace. For example, if the user is relaxed, the learning pace adjustment unit will speed up the learning pace. For example, if the user is excited, the learning pace adjustment unit will adjust the learning pace to maintain concentration. This makes it possible to provide a learning pace that corresponds to the user's emotional state, thereby improving learning effectiveness.

[0109] The educational support system may also include an environment optimization unit that further optimizes the user's learning environment. For example, the environment optimization unit might reduce ambient noise to help the user concentrate on learning. It might also provide appropriate music to help the user relax. Furthermore, it might display a message prompting the user to take a break if they become tired. By optimizing the user's learning environment in this way, learning effectiveness can be improved.

[0110] The educational support system may further include a difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of the learning content based on those emotions. For example, the difficulty adjustment unit might lower the difficulty of the learning content if the user is feeling stressed. For example, it might raise the difficulty of the learning content if the user is relaxed. For example, it might provide challenging learning content if the user is excited. This allows the system to provide learning content with difficulty levels that match the user's emotional state, thereby improving learning effectiveness.

[0111] The educational support system can also include a sharing section for sharing users' learning outcomes. This sharing section could, for example, allow users to share their achieved learning outcomes with other employees. It could also, for example, post users' learning outcomes on an internal bulletin board. Furthermore, it could allow users to report their learning outcomes to their supervisors. By sharing users' learning outcomes, this can increase motivation and improve learning effectiveness.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The generation unit automatically generates sales training materials and sales support tools. The generation unit uses AI to generate materials based on the latest market needs, competitor analysis, and trend forecasts. For example, it automatically creates materials that include the features of new products, sales strategies, and comparisons with competing products. Step 2: The data collection unit collects daily sales data and customer feedback. The data collection unit collects sales figures, sales by product, customer comments, survey results, etc., in real time and stores them in a database. The data collection unit uses AI to automatically collect this data. Step 3: The implementation unit incorporates the data collected by the collection unit into the educational tools. The implementation unit updates training manuals, presentation materials, sales guides, etc., based on the collected data. The implementation unit uses AI to analyze the collected data and incorporates it into the educational tools.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the generation unit, collection unit, reflection unit, customization unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and is implemented by the control unit 46A or the specific processing unit 290 of the data processing unit 12. The reflection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as a processing unit that updates educational tools based on the collected data. The customization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes regional characteristics and performs customization. The provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as a processing unit that provides basic sales strategies and product descriptions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 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.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the generation unit, collection unit, reflection unit, customization unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and is implemented by the control unit 46A or the specific processing unit 290 of the data processing unit 12. The reflection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as a processing unit that updates educational tools based on the collected data. The customization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes regional characteristics and performs customization. The provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as a processing unit that provides basic sales strategies and product descriptions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the generation unit, collection unit, reflection unit, customization unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and is implemented by the control unit 46A or the specific processing unit 290 of the data processing unit 12. The reflection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as a processing unit that updates educational tools based on the collected data. The customization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes regional characteristics and performs customization. The provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as a processing unit that provides basic sales strategies and product descriptions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the generation unit, collection unit, reflection unit, customization unit, and provision unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The collection unit collects data using the camera 42 and microphone 238 of the robot 414 and is implemented by the control unit 46A or the specific processing unit 290 of the data processing unit 12. The reflection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as a processing unit that updates educational tools based on the collected data. The customization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes regional characteristics and performs customization. The provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as a processing unit that provides basic sales strategies and product descriptions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A generation unit that automatically generates sales training materials and sales support tools, The data collection department collects daily sales data and customer feedback, The collection unit includes a reflection unit that reflects the data collected by the collection unit into an educational tool. A system characterized by the following features. (Note 2) It includes a customization section that takes into account the characteristics of each sales area. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a sales department that provides basic sales strategies and product descriptions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We generate materials based on the latest market needs, competitive analysis, and trend forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect sales data and customer feedback in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reflection unit is, Update educational tools based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the way the material is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating materials, past sales data is analyzed to reflect the optimal sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating materials, different materials are generated based on specific sales scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the length of the material based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is Customize the materials based on a specific sales campaign when generating them. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating documents, the actions of competitors should be reflected in real time. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, prioritize data collection based on specific sales events. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting data, the system analyzes past data collection history and selects the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting data, prioritize the collection of sales data from specific regions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When collecting data, we will gather feedback from social media. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reflection unit is, It estimates user sentiment and adjusts the update frequency of educational tools based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reflection unit is, When updating educational tools, customize the content based on specific sales data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reflection unit is, When updating educational tools, we analyze past update history and select the optimal update method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reflection unit is, It estimates the user's emotions and adjusts how educational tools are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reflection unit is, Customize the content of educational tool updates based on specific sales campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reflection unit is, When updating educational tools, reflect the actions of competitors in real time. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned customization unit is During customization, the content is adjusted based on sales data for a specific region. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned customization unit is During customization, adjust the content based on specific sales campaigns. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, customize the content based on specific sales data. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, customize the content based on a specific sales campaign. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, reflect the actions of competitors in real time. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that automatically generates sales training materials and sales support tools, The data collection department collects daily sales data and customer feedback, The collection unit includes a reflection unit that reflects the data collected by the collection unit into an educational tool. A system characterized by the following features.

2. It includes a customization section that takes into account the characteristics of each sales area. The system according to feature 1.

3. It includes a sales department that provides basic sales strategies and product descriptions. The system according to feature 1.

4. The generating unit is We generate materials based on the latest market needs, competitive analysis, and trend forecasts. The system according to feature 1.

5. The aforementioned collection unit is Collect sales data and customer feedback in real time. The system according to feature 1.

6. The aforementioned reflection unit is, Update educational tools based on collected data. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the way the material is presented based on those estimated emotions. The system according to feature 1.

8. The generating unit is When generating materials, past sales data is analyzed to reflect the optimal sales strategy. The system according to feature 1.

9. The generating unit is When generating materials, different materials are generated based on specific sales scenarios. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the length of the material based on the estimated user emotions. The system according to feature 1.

Images