system

Abstract

The system according to this embodiment aims to discover a company's appeal and reflect it on its website. [Solution] The system according to this embodiment comprises an appeal discovery unit, an automatic update unit, and a data collection unit. The appeal discovery unit discovers the appeal of a company. The automatic update unit reflects the appeal discovered by the appeal discovery unit on the website. The data collection unit collects data on website visitors.

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, and includes 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 charm of a company has not been sufficiently discovered and reflected on the HP, and there is room for improvement.

[0005] The system according to the embodiment aims to discover the charm of a company and reflect it on the HP.

Means for Solving the Problems

[0006] The system according to the embodiment includes a charm discovery unit, an automatic update unit, and a data collection unit. The charm discovery unit discovers the charm of a company. The automatic update unit reflects the charm discovered by the charm discovery unit on the HP. The data collection unit collects data of HP viewers.

Effects of the Invention

[0007] The system according to this embodiment can discover a company's appeal and reflect it on its website. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 corporate appeal communication system according to an embodiment of the present invention is a system for effectively communicating the appeal of a corporation when the corporation does not have a website (HP) or when one exists but is not updated. This system uses a generative AI to discover the potential appeal of a corporation and writes that appeal on the HP. Next, it automatically updates the HP to always provide the latest information. It is also possible to collect data on HP visitors and utilize this as information products. First, a generative AI is used to discover the potential appeal of a corporation. The generative AI analyzes the corporation's internal and external data to extract its strengths and characteristics. For example, it analyzes the corporation's history, product features, employee testimonials, etc., to clarify the corporation's appeal. This information is written on the corporation's HP and promoted both internally and externally. Next, the HP is automatically updated. The generative AI regularly collects the latest information on the corporation and reflects it on the HP. For example, it automatically updates information on new products, event announcements, press releases, etc. This ensures that the corporation's HP always provides the latest information and offers attractive content to visitors. Furthermore, data on HP visitors is collected. The AI-generated content analyzes website visitor behavior data to understand which pages are frequently viewed and which content is popular. This allows companies to understand visitors' interests and develop more effective marketing strategies. The collected data can also be used as information products, contributing to increased company revenue. This system enables companies to effectively communicate their appeal and improve recruitment and inquiry numbers. Furthermore, automatic website updates ensure that the latest information is always provided, delivering engaging content to visitors. Collecting visitor data and utilizing it as information products also contributes to increased company revenue. In summary, this corporate appeal communication system effectively communicates a company's appeal, enables automatic website updates, and collects visitor data.

[0029] The corporate appeal communication system according to this embodiment comprises an appeal discovery unit, an automatic update unit, and a data collection unit. The appeal discovery unit discovers the appeal of a company. For example, the appeal discovery unit uses generative AI to analyze the company's internal and external data and extract the company's strengths and characteristics. For example, the appeal discovery unit analyzes the company's history, product features, employee voices, etc., to clarify the company's appeal. The appeal discovery unit can also use generative AI to analyze the company's past success stories and customer feedback to extract the company's appeal. For example, the appeal discovery unit analyzes past project data and customer survey data to extract success factors and the appeal that customers perceive. The automatic update unit reflects the appeal discovered by the appeal discovery unit on the website. For example, the automatic update unit uses generative AI to periodically collect the latest information on the company and reflect it on the website. For example, the automatic update unit automatically updates information on new products, event announcements, press releases, etc. The automatic update unit can also use generative AI to estimate the emotions of website visitors and adjust the update content based on the estimated emotions. For example, the automatic update unit analyzes the emotions of visitors and prioritizes updating content that visitors are likely to be interested in. The data collection unit collects data on website visitors. The data collection unit analyzes website visitor behavior data using, for example, generative AI to understand which pages are frequently viewed and which content is popular. For example, the data collection unit collects and analyzes behavioral data such as page views, click-through rates, and time spent on the site. The data collection unit can also utilize the collected data as information products. For example, the data collection unit can create marketing reports and customer analysis data based on the collected data and provide them as information products. As a result, the corporate appeal communication system enables effective communication of a company's appeal, automatic website updates, and collection of visitor data.

[0030] The Charm Discovery Department uncovers the appeal of companies. For example, it uses generative AI to analyze internal and external company data to extract the company's strengths and characteristics. Specifically, the generative AI utilizes natural language processing technology to analyze text data such as the company's history, product features, and employee testimonials. For instance, it analyzes the company's history from its founding to the present in detail to extract its growth process and important milestones. Regarding product features, it analyzes the technical advantages and market evaluation of products to clarify points of differentiation from competitors' products. Regarding employee testimonials, it analyzes data from internal surveys and interviews to extract the company's appeal and sense of fulfillment that employees feel. Furthermore, the Charm Discovery Department can also use generative AI to analyze past success stories and customer feedback to extract the company's appeal. For example, it analyzes past project data to clarify success factors and project outcomes. Regarding customer survey data, it extracts the company's strengths and appeal as perceived by customers and presents them as concrete examples. In this way, the Charm Discovery Department can comprehensively analyze internal and external company data to discover the company's appeal from multiple perspectives.

[0031] The automated update unit reflects the appeal discovered by the appeal discovery unit onto the website. For example, the automated update unit periodically collects the latest information about a company using generative AI and reflects it on the website. Specifically, the generative AI uses web scraping technology to collect the latest information from the company's official website, news sites, social media, etc. For example, it automatically collects information on new products, event announcements, press releases, etc., and reflects them in the relevant sections of the website. The automated update unit can also use generative AI to estimate the emotions of website visitors and adjust the update content based on the estimated emotions. For example, the generative AI analyzes visitor behavior data and prioritizes displaying content that visitors are likely to be interested in. Specifically, it estimates visitors' interests and concerns based on data such as pages they have previously viewed, links they have clicked, and time spent on the site. This allows the automated update unit to provide content that meets the needs of visitors and enhance the appeal of the website. Furthermore, the automated update unit can perform not only periodic updates but also real-time updates. For example, if important news or urgent announcements occur, they can be immediately reflected on the website. This allows the automated update unit to quickly and accurately disseminate the latest company information, keeping the website up-to-date.

[0032] The data collection department collects data on website visitors. For example, it uses generative AI to analyze visitor behavior data, identifying which pages are frequently viewed and which content is popular. Specifically, the generative AI uses web analytics tools to collect and analyze behavioral data such as page views, click-through rates, and time spent on the site. For instance, it can determine how frequently specific pages are viewed, which links are frequently clicked, and which pages visitors spend the longest time on. The data collection department can also utilize the collected data as information products. For example, it can create marketing reports and customer analysis data based on the collected data and offer them as information products. Specifically, it analyzes visitor behavior data to clarify visitors' interests and preferences. This allows companies to understand the needs of their target customers and develop effective marketing strategies. Furthermore, the data collection department can identify areas for improvement on the website based on the collected data, thereby improving its usability. For example, it can identify pages where visitors leave the site and links that are not clicked, and implement corrective measures. This allows the data collection department to effectively utilize website visitor data and support the promotion of the company's appeal.

[0033] The Charm Discovery Unit can analyze a company's internal and external data to extract its strengths and characteristics. For example, the Charm Discovery Unit can analyze employee feedback and internal survey results as internal data. It can also analyze market research data and customer feedback as external data. For example, the Charm Discovery Unit can extract a company's strengths based on employee feedback. It can also clarify a company's characteristics based on internal survey results. Furthermore, the Charm Discovery Unit can extract a company's competitive advantages based on market research data. For example, the Charm Discovery Unit can clarify a company's appeal based on customer feedback. By clarifying a company's strengths and characteristics, it can effectively communicate its appeal. Some or all of the above processing in the Charm Discovery Unit may be performed using or without a generative AI. For example, the Charm Discovery Unit can input a company's internal and external data into a generative AI and have the generative AI perform the extraction of the company's strengths and characteristics.

[0034] The automated update unit can automatically update information on new products, event announcements, press releases, and more. For example, the automated update unit can periodically collect the latest company information using a generation AI and reflect it on the website. For instance, the automated update unit can automatically update information on new products, event announcements, and press releases. For example, it can automatically update information such as specifications, release dates, and prices for new products. It can also automatically update information such as the date, time, location, and participation methods for events. Furthermore, it can automatically update information such as the content, date, and presenter of press releases. This ensures the website always provides the latest information and offers engaging content to visitors. Some or all of the above-described processes in the automated update unit may be performed using a generation AI, or they may not. For example, the automated update unit can input the latest company information into a generation AI and have the generation AI perform the task of reflecting it on the website.

[0035] The data collection unit can analyze website visitor behavior data to understand which pages are frequently viewed and which content is popular. For example, the data collection unit collects and analyzes behavioral data such as page views, click-through rates, and time spent on a page. For instance, it can determine which pages are frequently viewed based on page views. It can also determine which content is popular based on click-through rates. Furthermore, it can understand visitors' interests and preferences based on time spent on a page. For example, the data collection unit can identify pages with high page views and analyze their content. It can also identify content with high click-through rates and analyze its characteristics. Finally, it can identify pages with long dwell times and analyze their content. This allows for an understanding of visitors' interests and preferences, enabling the development of effective marketing strategies. Some or all of the above-described processes in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input website visitor behavior data into a generative AI and have the generative AI perform the analysis of the behavioral data.

[0036] The data collection unit can utilize the collected data as information products. For example, the data collection unit can create marketing reports and customer analysis data based on the collected data and provide them as information products. For example, the data collection unit can create marketing reports based on the collected data. The data collection unit can also create customer analysis data based on the collected data. The data collection unit can also create market research reports based on the collected data. For example, the data collection unit can create marketing reports based on the collected data and use them in a company's marketing strategy. The data collection unit can also create customer analysis data based on the collected data to understand customer needs. The data collection unit can also create market research reports based on the collected data to understand market trends. In this way, by utilizing the collected data as information products, it contributes to improving the company's profitability. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the collected data into a generation AI and have the generation AI create marketing reports and customer analysis data.

[0037] The Charm Discovery Unit can estimate employee emotions and extract the company's appeal based on those estimated emotions. For example, the Charm Discovery Unit can use generative AI to analyze employee emotions and extract projects and achievements that employees are proud of as the company's appeal. The Charm Discovery Unit can also analyze employee emotions and extract employee benefits and a work environment that employees are satisfied with as the company's appeal. The Charm Discovery Unit can also use generative AI to evaluate employee emotions and extract the company culture and teamwork that employees perceive as the company's appeal. For example, the generative AI analyzes employee emotions and extracts projects and achievements that employees are proud of as the company's appeal. The generative AI can also analyze employee emotions and extract employee benefits and a work environment that employees are satisfied with as the company's appeal. The generative AI can also evaluate employee emotions and extract the company culture and teamwork that employees perceive as the company's appeal. This allows for the more accurate communication of the company's appeal by extracting it based on employee emotions. Some or all of the above-described processes in the Charm Discovery Unit may be performed using generative AI or not. For example, the "Attractiveness Discovery Department" can input employee emotional data into a generating AI and have the AI ​​extract the company's attractiveness.

[0038] The Charm Discovery Unit can analyze a company's past success stories and extract the factors contributing to those successes as attractive features. For example, the Charm Discovery Unit can use generative AI to analyze a company's past project data and extract commonalities in successful projects as attractive features. The Charm Discovery Unit can also analyze a company's past sales data and extract factors contributing to periods of rapid sales growth as attractive features. The Charm Discovery Unit can also use generative AI to analyze a company's past customer feedback and extract factors contributing to high customer satisfaction as attractive features. For example, the generative AI can analyze a company's past project data and extract commonalities in successful projects as attractive features. The generative AI can also analyze a company's past sales data and extract factors contributing to periods of rapid sales growth as attractive features. The generative AI can also analyze a company's past customer feedback and extract factors contributing to high customer satisfaction as attractive features. This allows companies to effectively communicate their attractive features by analyzing past success stories. Some or all of the above-described processes in the Charm Discovery Unit may be performed using generative AI, or they may be performed without using generative AI. For example, the appeal discovery unit can input past project data, sales data, and customer feedback from a company into a generating AI, which can then extract the factors for success.

[0039] The appeal discovery unit can analyze customer feedback from a company and extract the appeal that customers perceive. For example, the appeal discovery unit can use generative AI to analyze customer survey data and extract products and services that customers particularly value as appealing. For example, the appeal discovery unit can use generative AI to analyze social media comments and extract company characteristics that customers find favorable as appealing. The appeal discovery unit can also use generative AI to analyze customer reviews and extract the reasons why customers make repeat purchases as appealing. For example, the generative AI can analyze customer survey data and extract products and services that customers particularly value as appealing. The generative AI can also analyze social media comments and extract company characteristics that customers find favorable as appealing. The generative AI can also analyze customer reviews and extract the reasons why customers make repeat purchases as appealing. This allows for the effective communication of the appeal that customers perceive by analyzing customer feedback. Some or all of the above-described processes in the appeal discovery unit may be performed using generative AI or without generative AI. For example, the appeal discovery unit can input customer survey data, social media comments, and customer reviews into a generating AI, which can then extract the appealing aspects that customers perceive.

[0040] The Charm Discovery Unit can estimate employee emotions and determine the priority of attractiveness based on those estimated emotions. For example, the Charm Discovery Unit can use generative AI to analyze employee emotions and prioritize the elements that employees are most proud of as attractiveness. The Charm Discovery Unit can also use generative AI to analyze employee emotions and prioritize the benefits that employees are most satisfied with as attractiveness. The Charm Discovery Unit can also use generative AI to evaluate employee emotions and prioritize the corporate culture that employees value most as attractiveness. For example, the generative AI analyzes employee emotions and prioritizes the elements that employees are most proud of as attractiveness. The generative AI can also analyze employee emotions and prioritize the benefits that employees are most satisfied with as attractiveness. The generative AI can also evaluate employee emotions and prioritize the corporate culture that employees value most as attractiveness. This makes it possible to communicate attractiveness more effectively by determining the priority of attractiveness based on employee emotions. Some or all of the above processing in the Charm Discovery Unit may be performed using generative AI or not. For example, the "Charm Discovery Department" can input employee emotional data into a generating AI and have the AI ​​determine the priority of attractiveness.

[0041] The appeal discovery unit can analyze data from a company's competitors and extract differentiating points as appealing features. For example, the appeal discovery unit can use generative AI to analyze competitors' product data and extract the advantages of its own products as appealing features. The appeal discovery unit can also use generative AI to analyze competitors' marketing strategies and extract its own unique features as appealing features. The appeal discovery unit can also use generative AI to analyze customer feedback from competitors and extract its own strengths as appealing features. For example, the generative AI can analyze competitors' product data and extract the advantages of its own products as appealing features. The generative AI can also analyze competitors' marketing strategies and extract its own unique features as appealing features. The generative AI can also analyze customer feedback from competitors and extract its own strengths as appealing features. This allows a company to clearly identify its differentiating points by analyzing competitor data. Some or all of the above-described processes in the appeal discovery unit may be performed using generative AI, or they may be performed without using generative AI. For example, the appeal discovery unit can input competitor product data, marketing strategies, and customer feedback into a generating AI, and have the AI ​​extract points of differentiation.

[0042] The Charm Discovery Unit can analyze a company's social media activities and extract its online reputation as a key attraction. For example, the Charm Discovery Unit can use generative AI to analyze social media posts and extract positive company evaluations as attractive features. Alternatively, it can use generative AI to analyze social media engagement data and extract popular company content as an attractive feature. Furthermore, it can use generative AI to analyze social media follower reactions and extract a company's strengths as attractive features. This allows for effective online communication by analyzing social media activities. Some or all of the above-described processes in the Charm Discovery Unit may be performed using generative AI, or they may not. For example, the Charm Discovery Unit can input social media posts, engagement data, and follower reactions into a generative AI and have the generative AI perform the extraction of online reputation.

[0043] The automated update unit can estimate the emotions of website visitors and adjust the update content based on the estimated emotions. For example, the automated update unit can use a generative AI to analyze visitors' emotions and prioritize updating content that visitors are likely to be interested in. For example, the automated update unit can use a generative AI to analyze visitors' emotions and improve and update parts that visitors are dissatisfied with. The automated update unit can also use a generative AI to evaluate visitors' emotions and add information that visitors will find satisfying. For example, the generative AI analyzes visitors' emotions and prioritizes updating content that visitors are likely to be interested in. The generative AI can also analyze visitors' emotions and improve and update parts that visitors are dissatisfied with. The generative AI can also evaluate visitors' emotions and add information that visitors will find satisfying. This allows for the provision of more attractive content by adjusting the update content based on visitors' emotions. Some or all of the above processes in the automated update unit may be performed using a generative AI or not. For example, the automated update unit can input visitors' emotion data into a generative AI and have the generative AI perform the adjustment of the update content.

[0044] The automatic update unit can integrate with a company's internal systems to automatically update inventory and pricing information in real time. For example, the automatic update unit can use a generation AI to integrate with a company's inventory management system and reflect inventory status on the website in real time. The automatic update unit can also use a generation AI to integrate with a company's pricing system and reflect price changes on the website in real time. The automatic update unit can also use a generation AI to analyze a company's sales data and prioritize displaying best-selling products on the website. For example, the generation AI can integrate with a company's inventory management system and reflect inventory status on the website in real time. The generation AI can also integrate with a company's pricing system and reflect price changes on the website in real time. The generation AI can also analyze a company's sales data and prioritize displaying best-selling products on the website. This allows for the provision of the latest information by updating inventory and pricing information in real time. Some or all of the above-described processes in the automatic update unit may be performed using a generation AI, or they may be performed without using a generation AI. For example, the automated update unit can input data from a company's inventory management system, pricing system, and sales data into a generating AI, allowing the AI ​​to perform real-time information updates.

[0045] The automated update unit can automatically collect company news and press releases and reflect them on the website. For example, the automated update unit can use a generation AI to analyze a company's news feed and automatically reflect the latest news on the website. For example, the automated update unit can use a generation AI to collect company press releases and automatically post important information on the website. The automated update unit can also use a generation AI to collect company event information and reflect the latest event information on the website. For example, the generation AI can analyze a company's news feed and automatically reflect the latest news on the website. The generation AI can also collect company press releases and automatically post important information on the website. The generation AI can also collect company event information and reflect the latest event information on the website. This allows for the provision of up-to-date information by automatically collecting company news and press releases. Some or all of the above processes in the automated update unit may be performed using a generation AI or not. For example, the automated update unit can input company news feeds, press releases, and event information into a generation AI and have the generation AI perform the task of reflecting them on the website.

[0046] The automatic update unit can estimate the emotions of website visitors and adjust the update frequency based on the estimated emotions. For example, the automatic update unit can use a generative AI to analyze visitors' emotions and adjust the update frequency at times when visitors are likely to be interested. Alternatively, the automatic update unit can use a generative AI to analyze visitors' emotions and increase the update frequency if visitors are feeling dissatisfied. The automatic update unit can also use a generative AI to evaluate visitors' emotions and update at a frequency that satisfies them. For example, the generative AI analyzes visitors' emotions and adjusts the update frequency at times when visitors are likely to be interested. The generative AI can also analyze visitors' emotions and increase the update frequency if visitors are feeling dissatisfied. The generative AI can also evaluate visitors' emotions and update at a frequency that satisfies them. This allows for more effective information provision by adjusting the update frequency based on visitors' emotions. Some or all of the above-described processes in the automatic update unit may be performed using a generative AI, or they may not. For example, the automatic update unit can input visitors' emotion data into a generative AI and have the generative AI adjust the update frequency.

[0047] The automated update unit can automatically collect a company's blog and social media posts and reflect them on its website. For example, the automated update unit can use a generation AI to analyze a company's blog feed and automatically reflect the latest posts on the website. The automated update unit can also use a generation AI to collect a company's social media posts and automatically post important information on the website. Furthermore, the automated update unit can use a generation AI to analyze a company's social media engagement data and reflect popular posts on the website. For example, the generation AI analyzes a company's blog feed and automatically reflects the latest posts on the website. The generation AI can also collect a company's social media posts and automatically post important information on the website. The generation AI can also analyze a company's social media engagement data and reflect popular posts on the website. This allows for the provision of up-to-date information by automatically collecting a company's blog and social media posts. Some or all of the above-described processes in the automated update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the automated update unit can input a company's blog feed, social media posts, and social media engagement data into a generation AI and have the generation AI perform the website update.

[0048] The automatic update unit can automatically update a company's event calendar and provide the latest event information. For example, the automatic update unit can use a generation AI to analyze the company's event calendar and automatically reflect the latest event information on the website. The automatic update unit can also use a generation AI to collect company event information and automatically post important events on the website. Furthermore, the automatic update unit can use a generation AI to analyze company event participation data and reflect popular event information on the website. For example, the generation AI analyzes the company's event calendar and automatically reflects the latest event information on the website. The generation AI can also collect company event information and automatically post important events on the website. The generation AI can also analyze company event participation data and reflect popular event information on the website. This allows the automatic update unit to provide the latest event information by automatically updating the company's event calendar. Some or all of the above-described processes in the automatic update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the automatic update unit can input the company's event calendar, event information, and event participation data into a generation AI and have the generation AI perform the website update.

[0049] The data collection unit can estimate the emotions of website visitors and adjust the data collection method based on the estimated emotions. For example, the data collection unit can use generative AI to analyze visitors' emotions and prioritize collecting data that visitors are likely to be interested in. Alternatively, the data collection unit can use generative AI to analyze visitors' emotions and focus on collecting data related to areas where visitors are dissatisfied. The data collection unit can also use generative AI to evaluate visitors' emotions and collect data that will satisfy them. For example, the generative AI analyzes visitors' emotions and prioritizes collecting data that visitors are likely to be interested in. The generative AI can also analyze visitors' emotions and focus on collecting data related to areas where visitors are dissatisfied. The generative AI can also evaluate visitors' emotions and collect data that will satisfy them. This allows for more effective data collection by adjusting the data collection method based on visitors' emotions. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input visitors' emotion data into the generative AI and have the generative AI adjust the data collection method.

[0050] The data collection unit can analyze the behavior patterns of website visitors and collect browsing data during specific time periods. For example, the data collection unit can use a generative AI to analyze visitor behavior patterns and focus on collecting data during peak hours. The data collection unit can also use a generative AI to analyze visitor behavior patterns and collect data for specific days of the week or time periods. The data collection unit can also use a generative AI to evaluate visitor behavior patterns and collect data for the time periods with the highest browsing activity. For example, the generative AI analyzes visitor behavior patterns and focuses on collecting data during peak hours. The generative AI can also analyze visitor behavior patterns and collect data for specific days of the week or time periods. The generative AI can also evaluate visitor behavior patterns and collect data for the time periods with the highest browsing activity. This allows for the effective collection of data during specific time periods by analyzing visitor behavior patterns. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may not. For example, the data collection unit can input visitor behavior pattern data into a generative AI and have the generative AI perform data collection during specific time periods.

[0051] The data collection unit can collect demographic information of website visitors and utilize it for targeted marketing. For example, the data collection unit can use generative AI to collect demographic information such as visitors' age and gender and utilize it for targeted marketing. The data collection unit can also use generative AI to collect visitors' regional information and develop regional marketing strategies. Furthermore, the data collection unit can use generative AI to collect visitors' occupations and interests and conduct marketing targeting specific groups. For example, the generative AI collects demographic information such as visitors' age and gender and utilizes it for targeted marketing. The generative AI can also collect visitors' regional information and develop regional marketing strategies. The generative AI can also collect visitors' occupations and interests and conduct marketing targeting specific groups. This makes targeted marketing possible by collecting demographic information. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' demographic information into the generative AI and have the generative AI perform data collection for targeted marketing.

[0052] The data collection unit can estimate the emotions of website visitors and prioritize data based on those estimated emotions. For example, the data collection unit can use generative AI to analyze visitors' emotions and prioritize collecting data that visitors are most likely to be interested in. Alternatively, the data collection unit can use generative AI to analyze visitors' emotions and prioritize collecting data on areas where visitors are dissatisfied. The data collection unit can also use generative AI to evaluate visitors' emotions and prioritize collecting data that visitors will find satisfying. For example, the generative AI analyzes visitors' emotions and prioritizes collecting data that visitors are most likely to be interested in. The generative AI can also analyze visitors' emotions and prioritize collecting data on areas where visitors are dissatisfied. The generative AI can also evaluate visitors' emotions and prioritize collecting data that visitors will find satisfying. This allows for more effective data collection by prioritizing data based on visitors' emotions. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' emotion data into the generative AI and have the generative AI determine the data prioritization.

[0053] The data collection unit can collect geographical location information of website visitors and understand their interests in each region. For example, the data collection unit can use generative AI to collect visitors' geographical location information and identify popular content in each region. The data collection unit can also use generative AI to analyze visitors' geographical location information and develop regional marketing strategies. The data collection unit can also use generative AI to evaluate visitors' geographical location information and understand their interests in each region. For example, the generative AI collects visitors' geographical location information and identifies popular content in each region. The generative AI can also analyze visitors' geographical location information and develop regional marketing strategies. The generative AI can also evaluate visitors' geographical location information and understand their interests in each region. Thus, by collecting geographical location information, it is possible to understand regional interests. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' geographical location information into the generative AI and have the generative AI perform the task of understanding regional interests.

[0054] The data collection unit can analyze the social media activities of website visitors and collect relevant data. For example, the data collection unit can use generative AI to analyze visitors' social media posts, understand their interests, and collect relevant data. The data collection unit can also use generative AI to analyze visitors' social media engagement data and collect data related to popular topics. The data collection unit can also use generative AI to analyze the reactions of visitors' social media followers and collect relevant data. For example, the generative AI analyzes visitors' social media posts, understands their interests, and collects relevant data. The generative AI can also analyze visitors' social media engagement data and collect data related to popular topics. The generative AI can also analyze the reactions of visitors' social media followers and collect relevant data. This allows for the effective collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' social media posts, engagement data, and follower reactions into the generative AI and have the generative AI collect the relevant data.

[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 corporate appeal communication system can also include a user feedback collection unit. This unit directly collects feedback from website visitors and analyzes it to further enhance the company's appeal. For example, the feedback collection unit can place a survey form on the website to collect visitors' opinions and impressions. It can also input the collected feedback into a generating AI to extract specific areas for improvement to enhance the company's appeal. Furthermore, based on the collected feedback, the feedback collection unit can propose new content and services that visitors are looking for. This allows companies to continuously provide attractive content that meets the needs of their visitors.

[0057] The corporate appeal communication system can also include a competitive analysis department. This department analyzes competitors' websites and marketing strategies, providing data to relatively strengthen the company's own appeal. For example, it can analyze the features of competitors' products and services, clearly highlighting the advantages of the company's own products and services. It can also analyze the effectiveness of competitors' marketing campaigns, providing insights to optimize the company's own marketing strategy. Furthermore, it can collect customer feedback from competitors and propose specific measures to improve the company's own customer satisfaction. This allows companies to differentiate themselves from competitors and establish themselves as more attractive businesses.

[0058] The corporate appeal communication system can also be equipped with a data visualization unit. This unit visually displays collected data, providing a tool for effectively communicating a company's appeal. For example, it can display a company's strengths and characteristics in graphs and charts, making them easy for viewers to understand. It can also display a company's history and product features as infographics. Furthermore, it can visualize a company's success stories and customer feedback to highlight its appeal. This allows for the effective communication of a company's appeal through visual data displays.

[0059] The corporate appeal communication system can also be equipped with a personalized content delivery unit. This unit provides content optimized for each individual visitor based on their website visitor behavior data. For example, it can analyze a visitor's past browsing history and prioritize displaying relevant content. It can also provide customized content based on the visitor's interests and preferences. Furthermore, it can provide content targeted at specific demographic groups based on the visitor's demographic information. This allows for improved visitor satisfaction by providing content optimized for each individual visitor.

[0060] The corporate appeal communication system can also be equipped with a real-time alert function. This function detects unusual activity on the company's website and social media and issues alerts for immediate response. For example, it can issue an alert if website traffic suddenly increases, prompting measures to reduce server load. It can also issue an alert if negative comments on social media surge, suggesting rapid response measures. Furthermore, the real-time alert function can detect security risks on the company's website and social media and issue alerts for immediate response. This allows for the protection of the company's online presence through real-time anomaly detection and response.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The Charm Discovery Department discovers the company's appeal. For example, it uses generative AI to analyze the company's internal and external data to extract its strengths and characteristics. Specifically, it analyzes the company's history, product features, employee voices, past success stories, and customer feedback to clarify the company's appeal. Step 2: The automated update unit reflects the appeal discovered by the appeal discovery unit onto the website. For example, it uses a generation AI to periodically collect the latest company information and reflect it on the website. Specifically, it automatically updates information on new products, event announcements, press releases, etc. It can also estimate the emotions of viewers and adjust the update content based on those estimated emotions. Step 3: The data collection unit collects data on website visitors. For example, it uses generated AI to analyze website visitor behavior data to understand which pages are frequently viewed and which content is popular. Specifically, it collects and analyzes behavioral data such as page views, click-through rates, and time spent on the site. It can also create marketing reports and customer analysis data based on the collected data and provide them as information products.

[0063] (Example of form 2) The corporate appeal communication system according to an embodiment of the present invention is a system for effectively communicating the appeal of a corporation when the corporation does not have a website (HP) or when one exists but is not updated. This system uses a generative AI to discover the potential appeal of a corporation and writes that appeal on the HP. Next, it automatically updates the HP to always provide the latest information. It is also possible to collect data on HP visitors and utilize this as information products. First, a generative AI is used to discover the potential appeal of a corporation. The generative AI analyzes the corporation's internal and external data to extract its strengths and characteristics. For example, it analyzes the corporation's history, product features, employee testimonials, etc., to clarify the corporation's appeal. This information is written on the corporation's HP and promoted both internally and externally. Next, the HP is automatically updated. The generative AI regularly collects the latest information on the corporation and reflects it on the HP. For example, it automatically updates information on new products, event announcements, press releases, etc. This ensures that the corporation's HP always provides the latest information and offers attractive content to visitors. Furthermore, data on HP visitors is collected. The AI-generated content analyzes website visitor behavior data to understand which pages are frequently viewed and which content is popular. This allows companies to understand visitors' interests and develop more effective marketing strategies. The collected data can also be used as information products, contributing to increased company revenue. This system enables companies to effectively communicate their appeal and improve recruitment and inquiry numbers. Furthermore, automatic website updates ensure that the latest information is always provided, delivering engaging content to visitors. Collecting visitor data and utilizing it as information products also contributes to increased company revenue. In summary, this corporate appeal communication system effectively communicates a company's appeal, enables automatic website updates, and collects visitor data.

[0064] The corporate appeal communication system according to this embodiment comprises an appeal discovery unit, an automatic update unit, and a data collection unit. The appeal discovery unit discovers the appeal of a company. For example, the appeal discovery unit uses generative AI to analyze the company's internal and external data and extract the company's strengths and characteristics. For example, the appeal discovery unit analyzes the company's history, product features, employee voices, etc., to clarify the company's appeal. The appeal discovery unit can also use generative AI to analyze the company's past success stories and customer feedback to extract the company's appeal. For example, the appeal discovery unit analyzes past project data and customer survey data to extract success factors and the appeal that customers perceive. The automatic update unit reflects the appeal discovered by the appeal discovery unit on the website. For example, the automatic update unit uses generative AI to periodically collect the latest information on the company and reflect it on the website. For example, the automatic update unit automatically updates information on new products, event announcements, press releases, etc. The automatic update unit can also use generative AI to estimate the emotions of website visitors and adjust the update content based on the estimated emotions. For example, the automatic update unit analyzes the emotions of visitors and prioritizes updating content that visitors are likely to be interested in. The data collection unit collects data on website visitors. The data collection unit analyzes website visitor behavior data using, for example, generative AI to understand which pages are frequently viewed and which content is popular. For example, the data collection unit collects and analyzes behavioral data such as page views, click-through rates, and time spent on the site. The data collection unit can also utilize the collected data as information products. For example, the data collection unit can create marketing reports and customer analysis data based on the collected data and provide them as information products. As a result, the corporate appeal communication system enables effective communication of a company's appeal, automatic website updates, and collection of visitor data.

[0065] The Charm Discovery Department uncovers the appeal of companies. For example, it uses generative AI to analyze internal and external company data to extract the company's strengths and characteristics. Specifically, the generative AI utilizes natural language processing technology to analyze text data such as the company's history, product features, and employee testimonials. For instance, it analyzes the company's history from its founding to the present in detail to extract its growth process and important milestones. Regarding product features, it analyzes the technical advantages and market evaluation of products to clarify points of differentiation from competitors' products. Regarding employee testimonials, it analyzes data from internal surveys and interviews to extract the company's appeal and sense of fulfillment that employees feel. Furthermore, the Charm Discovery Department can also use generative AI to analyze past success stories and customer feedback to extract the company's appeal. For example, it analyzes past project data to clarify success factors and project outcomes. Regarding customer survey data, it extracts the company's strengths and appeal as perceived by customers and presents them as concrete examples. In this way, the Charm Discovery Department can comprehensively analyze internal and external company data to discover the company's appeal from multiple perspectives.

[0066] The automated update unit reflects the appeal discovered by the appeal discovery unit onto the website. For example, the automated update unit periodically collects the latest information about a company using generative AI and reflects it on the website. Specifically, the generative AI uses web scraping technology to collect the latest information from the company's official website, news sites, social media, etc. For example, it automatically collects information on new products, event announcements, press releases, etc., and reflects them in the relevant sections of the website. The automated update unit can also use generative AI to estimate the emotions of website visitors and adjust the update content based on the estimated emotions. For example, the generative AI analyzes visitor behavior data and prioritizes displaying content that visitors are likely to be interested in. Specifically, it estimates visitors' interests and concerns based on data such as pages they have previously viewed, links they have clicked, and time spent on the site. This allows the automated update unit to provide content that meets the needs of visitors and enhance the appeal of the website. Furthermore, the automated update unit can perform not only periodic updates but also real-time updates. For example, if important news or urgent announcements occur, they can be immediately reflected on the website. This allows the automated update unit to quickly and accurately disseminate the latest company information, keeping the website up-to-date.

[0067] The data collection department collects data on website visitors. For example, it uses generative AI to analyze visitor behavior data, identifying which pages are frequently viewed and which content is popular. Specifically, the generative AI uses web analytics tools to collect and analyze behavioral data such as page views, click-through rates, and time spent on the site. For instance, it can determine how frequently specific pages are viewed, which links are frequently clicked, and which pages visitors spend the longest time on. The data collection department can also utilize the collected data as information products. For example, it can create marketing reports and customer analysis data based on the collected data and offer them as information products. Specifically, it analyzes visitor behavior data to clarify visitors' interests and preferences. This allows companies to understand the needs of their target customers and develop effective marketing strategies. Furthermore, the data collection department can identify areas for improvement on the website based on the collected data, thereby improving its usability. For example, it can identify pages where visitors leave the site and links that are not clicked, and implement corrective measures. This allows the data collection department to effectively utilize website visitor data and support the promotion of the company's appeal.

[0068] The Charm Discovery Unit can analyze a company's internal and external data to extract its strengths and characteristics. For example, the Charm Discovery Unit can analyze employee feedback and internal survey results as internal data. It can also analyze market research data and customer feedback as external data. For example, the Charm Discovery Unit can extract a company's strengths based on employee feedback. It can also clarify a company's characteristics based on internal survey results. Furthermore, the Charm Discovery Unit can extract a company's competitive advantages based on market research data. For example, the Charm Discovery Unit can clarify a company's appeal based on customer feedback. By clarifying a company's strengths and characteristics, it can effectively communicate its appeal. Some or all of the above processing in the Charm Discovery Unit may be performed using or without a generative AI. For example, the Charm Discovery Unit can input a company's internal and external data into a generative AI and have the generative AI perform the extraction of the company's strengths and characteristics.

[0069] The automated update unit can automatically update information on new products, event announcements, press releases, and more. For example, the automated update unit can periodically collect the latest company information using a generation AI and reflect it on the website. For instance, the automated update unit can automatically update information on new products, event announcements, and press releases. For example, it can automatically update information such as specifications, release dates, and prices for new products. It can also automatically update information such as the date, time, location, and participation methods for events. Furthermore, it can automatically update information such as the content, date, and presenter of press releases. This ensures the website always provides the latest information and offers engaging content to visitors. Some or all of the above-described processes in the automated update unit may be performed using a generation AI, or they may not. For example, the automated update unit can input the latest company information into a generation AI and have the generation AI perform the task of reflecting it on the website.

[0070] The data collection unit can analyze website visitor behavior data to understand which pages are frequently viewed and which content is popular. For example, the data collection unit collects and analyzes behavioral data such as page views, click-through rates, and time spent on a page. For instance, it can determine which pages are frequently viewed based on page views. It can also determine which content is popular based on click-through rates. Furthermore, it can understand visitors' interests and preferences based on time spent on a page. For example, the data collection unit can identify pages with high page views and analyze their content. It can also identify content with high click-through rates and analyze its characteristics. Finally, it can identify pages with long dwell times and analyze their content. This allows for an understanding of visitors' interests and preferences, enabling the development of effective marketing strategies. Some or all of the above-described processes in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input website visitor behavior data into a generative AI and have the generative AI perform the analysis of the behavioral data.

[0071] The data collection unit can utilize the collected data as information products. For example, the data collection unit can create marketing reports and customer analysis data based on the collected data and provide them as information products. For example, the data collection unit can create marketing reports based on the collected data. The data collection unit can also create customer analysis data based on the collected data. The data collection unit can also create market research reports based on the collected data. For example, the data collection unit can create marketing reports based on the collected data and use them in a company's marketing strategy. The data collection unit can also create customer analysis data based on the collected data to understand customer needs. The data collection unit can also create market research reports based on the collected data to understand market trends. In this way, by utilizing the collected data as information products, it contributes to improving the company's profitability. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the collected data into a generation AI and have the generation AI create marketing reports and customer analysis data.

[0072] The Charm Discovery Unit can estimate employee emotions and extract the company's appeal based on those estimated emotions. For example, the Charm Discovery Unit can use generative AI to analyze employee emotions and extract projects and achievements that employees are proud of as the company's appeal. The Charm Discovery Unit can also analyze employee emotions and extract employee benefits and a work environment that employees are satisfied with as the company's appeal. The Charm Discovery Unit can also use generative AI to evaluate employee emotions and extract the company culture and teamwork that employees perceive as the company's appeal. For example, the generative AI analyzes employee emotions and extracts projects and achievements that employees are proud of as the company's appeal. The generative AI can also analyze employee emotions and extract employee benefits and a work environment that employees are satisfied with as the company's appeal. The generative AI can also evaluate employee emotions and extract the company culture and teamwork that employees perceive as the company's appeal. This allows for the more accurate communication of the company's appeal by extracting it based on employee emotions. Some or all of the above-described processes in the Charm Discovery Unit may be performed using generative AI or not. For example, the "Attractiveness Discovery Department" can input employee emotional data into a generating AI and have the AI ​​extract the company's attractiveness.

[0073] The Charm Discovery Unit can analyze a company's past success stories and extract the factors contributing to those successes as attractive features. For example, the Charm Discovery Unit can use generative AI to analyze a company's past project data and extract commonalities in successful projects as attractive features. The Charm Discovery Unit can also analyze a company's past sales data and extract factors contributing to periods of rapid sales growth as attractive features. The Charm Discovery Unit can also use generative AI to analyze a company's past customer feedback and extract factors contributing to high customer satisfaction as attractive features. For example, the generative AI can analyze a company's past project data and extract commonalities in successful projects as attractive features. The generative AI can also analyze a company's past sales data and extract factors contributing to periods of rapid sales growth as attractive features. The generative AI can also analyze a company's past customer feedback and extract factors contributing to high customer satisfaction as attractive features. This allows companies to effectively communicate their attractive features by analyzing past success stories. Some or all of the above-described processes in the Charm Discovery Unit may be performed using generative AI, or they may be performed without using generative AI. For example, the appeal discovery unit can input past project data, sales data, and customer feedback from a company into a generating AI, which can then extract the factors for success.

[0074] The appeal discovery unit can analyze customer feedback from a company and extract the appeal that customers perceive. For example, the appeal discovery unit can use generative AI to analyze customer survey data and extract products and services that customers particularly value as appealing. For example, the appeal discovery unit can use generative AI to analyze social media comments and extract company characteristics that customers find favorable as appealing. The appeal discovery unit can also use generative AI to analyze customer reviews and extract the reasons why customers make repeat purchases as appealing. For example, the generative AI can analyze customer survey data and extract products and services that customers particularly value as appealing. The generative AI can also analyze social media comments and extract company characteristics that customers find favorable as appealing. The generative AI can also analyze customer reviews and extract the reasons why customers make repeat purchases as appealing. This allows for the effective communication of the appeal that customers perceive by analyzing customer feedback. Some or all of the above-described processes in the appeal discovery unit may be performed using generative AI or without generative AI. For example, the appeal discovery unit can input customer survey data, social media comments, and customer reviews into a generating AI, which can then extract the appealing aspects that customers perceive.

[0075] The Charm Discovery Unit can estimate employee emotions and determine the priority of attractiveness based on those estimated emotions. For example, the Charm Discovery Unit can use generative AI to analyze employee emotions and prioritize the elements that employees are most proud of as attractiveness. The Charm Discovery Unit can also use generative AI to analyze employee emotions and prioritize the benefits that employees are most satisfied with as attractiveness. The Charm Discovery Unit can also use generative AI to evaluate employee emotions and prioritize the corporate culture that employees value most as attractiveness. For example, the generative AI analyzes employee emotions and prioritizes the elements that employees are most proud of as attractiveness. The generative AI can also analyze employee emotions and prioritize the benefits that employees are most satisfied with as attractiveness. The generative AI can also evaluate employee emotions and prioritize the corporate culture that employees value most as attractiveness. This makes it possible to communicate attractiveness more effectively by determining the priority of attractiveness based on employee emotions. Some or all of the above processing in the Charm Discovery Unit may be performed using generative AI or not. For example, the "Charm Discovery Department" can input employee emotional data into a generating AI and have the AI ​​determine the priority of attractiveness.

[0076] The appeal discovery unit can analyze data from a company's competitors and extract differentiating points as appealing features. For example, the appeal discovery unit can use generative AI to analyze competitors' product data and extract the advantages of its own products as appealing features. The appeal discovery unit can also use generative AI to analyze competitors' marketing strategies and extract its own unique features as appealing features. The appeal discovery unit can also use generative AI to analyze customer feedback from competitors and extract its own strengths as appealing features. For example, the generative AI can analyze competitors' product data and extract the advantages of its own products as appealing features. The generative AI can also analyze competitors' marketing strategies and extract its own unique features as appealing features. The generative AI can also analyze customer feedback from competitors and extract its own strengths as appealing features. This allows a company to clearly identify its differentiating points by analyzing competitor data. Some or all of the above-described processes in the appeal discovery unit may be performed using generative AI, or they may be performed without using generative AI. For example, the appeal discovery unit can input competitor product data, marketing strategies, and customer feedback into a generating AI, and have the AI ​​extract points of differentiation.

[0077] The Charm Discovery Unit can analyze a company's social media activities and extract its online reputation as a key attraction. For example, the Charm Discovery Unit can use generative AI to analyze social media posts and extract positive company evaluations as attractive features. Alternatively, it can use generative AI to analyze social media engagement data and extract popular company content as an attractive feature. Furthermore, it can use generative AI to analyze social media follower reactions and extract a company's strengths as attractive features. This allows for effective online communication by analyzing social media activities. Some or all of the above-described processes in the Charm Discovery Unit may be performed using generative AI, or they may not. For example, the Charm Discovery Unit can input social media posts, engagement data, and follower reactions into a generative AI and have the generative AI perform the extraction of online reputation.

[0078] The automated update unit can estimate the emotions of website visitors and adjust the update content based on the estimated emotions. For example, the automated update unit can use a generative AI to analyze visitors' emotions and prioritize updating content that visitors are likely to be interested in. For example, the automated update unit can use a generative AI to analyze visitors' emotions and improve and update parts that visitors are dissatisfied with. The automated update unit can also use a generative AI to evaluate visitors' emotions and add information that visitors will find satisfying. For example, the generative AI analyzes visitors' emotions and prioritizes updating content that visitors are likely to be interested in. The generative AI can also analyze visitors' emotions and improve and update parts that visitors are dissatisfied with. The generative AI can also evaluate visitors' emotions and add information that visitors will find satisfying. This allows for the provision of more attractive content by adjusting the update content based on visitors' emotions. Some or all of the above processes in the automated update unit may be performed using a generative AI or not. For example, the automated update unit can input visitors' emotion data into a generative AI and have the generative AI perform the adjustment of the update content.

[0079] The automatic update unit can integrate with a company's internal systems to automatically update inventory and pricing information in real time. For example, the automatic update unit can use a generation AI to integrate with a company's inventory management system and reflect inventory status on the website in real time. The automatic update unit can also use a generation AI to integrate with a company's pricing system and reflect price changes on the website in real time. The automatic update unit can also use a generation AI to analyze a company's sales data and prioritize displaying best-selling products on the website. For example, the generation AI can integrate with a company's inventory management system and reflect inventory status on the website in real time. The generation AI can also integrate with a company's pricing system and reflect price changes on the website in real time. The generation AI can also analyze a company's sales data and prioritize displaying best-selling products on the website. This allows for the provision of the latest information by updating inventory and pricing information in real time. Some or all of the above-described processes in the automatic update unit may be performed using a generation AI, or they may be performed without using a generation AI. For example, the automated update unit can input data from a company's inventory management system, pricing system, and sales data into a generating AI, allowing the AI ​​to perform real-time information updates.

[0080] The automated update unit can automatically collect company news and press releases and reflect them on the website. For example, the automated update unit can use a generation AI to analyze a company's news feed and automatically reflect the latest news on the website. For example, the automated update unit can use a generation AI to collect company press releases and automatically post important information on the website. The automated update unit can also use a generation AI to collect company event information and reflect the latest event information on the website. For example, the generation AI can analyze a company's news feed and automatically reflect the latest news on the website. The generation AI can also collect company press releases and automatically post important information on the website. The generation AI can also collect company event information and reflect the latest event information on the website. This allows for the provision of up-to-date information by automatically collecting company news and press releases. Some or all of the above processes in the automated update unit may be performed using a generation AI or not. For example, the automated update unit can input company news feeds, press releases, and event information into a generation AI and have the generation AI perform the task of reflecting them on the website.

[0081] The automatic update unit can estimate the emotions of website visitors and adjust the update frequency based on the estimated emotions. For example, the automatic update unit can use a generative AI to analyze visitors' emotions and adjust the update frequency at times when visitors are likely to be interested. Alternatively, the automatic update unit can use a generative AI to analyze visitors' emotions and increase the update frequency if visitors are feeling dissatisfied. The automatic update unit can also use a generative AI to evaluate visitors' emotions and update at a frequency that satisfies them. For example, the generative AI analyzes visitors' emotions and adjusts the update frequency at times when visitors are likely to be interested. The generative AI can also analyze visitors' emotions and increase the update frequency if visitors are feeling dissatisfied. The generative AI can also evaluate visitors' emotions and update at a frequency that satisfies them. This allows for more effective information provision by adjusting the update frequency based on visitors' emotions. Some or all of the above-described processes in the automatic update unit may be performed using a generative AI, or they may not. For example, the automatic update unit can input visitors' emotion data into a generative AI and have the generative AI adjust the update frequency.

[0082] The automated update unit can automatically collect a company's blog and social media posts and reflect them on its website. For example, the automated update unit can use a generation AI to analyze a company's blog feed and automatically reflect the latest posts on the website. The automated update unit can also use a generation AI to collect a company's social media posts and automatically post important information on the website. Furthermore, the automated update unit can use a generation AI to analyze a company's social media engagement data and reflect popular posts on the website. For example, the generation AI analyzes a company's blog feed and automatically reflects the latest posts on the website. The generation AI can also collect a company's social media posts and automatically post important information on the website. The generation AI can also analyze a company's social media engagement data and reflect popular posts on the website. This allows for the provision of up-to-date information by automatically collecting a company's blog and social media posts. Some or all of the above-described processes in the automated update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the automated update unit can input a company's blog feed, social media posts, and social media engagement data into a generation AI and have the generation AI perform the website update.

[0083] The automatic update unit can automatically update a company's event calendar and provide the latest event information. For example, the automatic update unit can use a generation AI to analyze the company's event calendar and automatically reflect the latest event information on the website. The automatic update unit can also use a generation AI to collect company event information and automatically post important events on the website. Furthermore, the automatic update unit can use a generation AI to analyze company event participation data and reflect popular event information on the website. For example, the generation AI analyzes the company's event calendar and automatically reflects the latest event information on the website. The generation AI can also collect company event information and automatically post important events on the website. The generation AI can also analyze company event participation data and reflect popular event information on the website. This allows the automatic update unit to provide the latest event information by automatically updating the company's event calendar. Some or all of the above-described processes in the automatic update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the automatic update unit can input the company's event calendar, event information, and event participation data into a generation AI and have the generation AI perform the website update.

[0084] The data collection unit can estimate the emotions of website visitors and adjust the data collection method based on the estimated emotions. For example, the data collection unit can use generative AI to analyze visitors' emotions and prioritize collecting data that visitors are likely to be interested in. Alternatively, the data collection unit can use generative AI to analyze visitors' emotions and focus on collecting data related to areas where visitors are dissatisfied. The data collection unit can also use generative AI to evaluate visitors' emotions and collect data that will satisfy them. For example, the generative AI analyzes visitors' emotions and prioritizes collecting data that visitors are likely to be interested in. The generative AI can also analyze visitors' emotions and focus on collecting data related to areas where visitors are dissatisfied. The generative AI can also evaluate visitors' emotions and collect data that will satisfy them. This allows for more effective data collection by adjusting the data collection method based on visitors' emotions. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input visitors' emotion data into the generative AI and have the generative AI adjust the data collection method.

[0085] The data collection unit can analyze the behavior patterns of website visitors and collect browsing data during specific time periods. For example, the data collection unit can use a generative AI to analyze visitor behavior patterns and focus on collecting data during peak hours. The data collection unit can also use a generative AI to analyze visitor behavior patterns and collect data for specific days of the week or time periods. The data collection unit can also use a generative AI to evaluate visitor behavior patterns and collect data for the time periods with the highest browsing activity. For example, the generative AI analyzes visitor behavior patterns and focuses on collecting data during peak hours. The generative AI can also analyze visitor behavior patterns and collect data for specific days of the week or time periods. The generative AI can also evaluate visitor behavior patterns and collect data for the time periods with the highest browsing activity. This allows for the effective collection of data during specific time periods by analyzing visitor behavior patterns. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may not. For example, the data collection unit can input visitor behavior pattern data into a generative AI and have the generative AI perform data collection during specific time periods.

[0086] The data collection unit can collect demographic information of website visitors and utilize it for targeted marketing. For example, the data collection unit can use generative AI to collect demographic information such as visitors' age and gender and utilize it for targeted marketing. The data collection unit can also use generative AI to collect visitors' regional information and develop regional marketing strategies. Furthermore, the data collection unit can use generative AI to collect visitors' occupations and interests and conduct marketing targeting specific groups. For example, the generative AI collects demographic information such as visitors' age and gender and utilizes it for targeted marketing. The generative AI can also collect visitors' regional information and develop regional marketing strategies. The generative AI can also collect visitors' occupations and interests and conduct marketing targeting specific groups. This makes targeted marketing possible by collecting demographic information. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' demographic information into the generative AI and have the generative AI perform data collection for targeted marketing.

[0087] The data collection unit can estimate the emotions of website visitors and prioritize data based on those estimated emotions. For example, the data collection unit can use generative AI to analyze visitors' emotions and prioritize collecting data that visitors are most likely to be interested in. Alternatively, the data collection unit can use generative AI to analyze visitors' emotions and prioritize collecting data on areas where visitors are dissatisfied. The data collection unit can also use generative AI to evaluate visitors' emotions and prioritize collecting data that visitors will find satisfying. For example, the generative AI analyzes visitors' emotions and prioritizes collecting data that visitors are most likely to be interested in. The generative AI can also analyze visitors' emotions and prioritize collecting data on areas where visitors are dissatisfied. The generative AI can also evaluate visitors' emotions and prioritize collecting data that visitors will find satisfying. This allows for more effective data collection by prioritizing data based on visitors' emotions. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' emotion data into the generative AI and have the generative AI determine the data prioritization.

[0088] The data collection unit can collect geographical location information of website visitors and understand their interests in each region. For example, the data collection unit can use generative AI to collect visitors' geographical location information and identify popular content in each region. The data collection unit can also use generative AI to analyze visitors' geographical location information and develop regional marketing strategies. The data collection unit can also use generative AI to evaluate visitors' geographical location information and understand their interests in each region. For example, the generative AI collects visitors' geographical location information and identifies popular content in each region. The generative AI can also analyze visitors' geographical location information and develop regional marketing strategies. The generative AI can also evaluate visitors' geographical location information and understand their interests in each region. Thus, by collecting geographical location information, it is possible to understand regional interests. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' geographical location information into the generative AI and have the generative AI perform the task of understanding regional interests.

[0089] The data collection unit can analyze the social media activities of website visitors and collect relevant data. For example, the data collection unit can use generative AI to analyze visitors' social media posts, understand their interests, and collect relevant data. The data collection unit can also use generative AI to analyze visitors' social media engagement data and collect data related to popular topics. The data collection unit can also use generative AI to analyze the reactions of visitors' social media followers and collect relevant data. For example, the generative AI analyzes visitors' social media posts, understands their interests, and collects relevant data. The generative AI can also analyze visitors' social media engagement data and collect data related to popular topics. The generative AI can also analyze the reactions of visitors' social media followers and collect relevant data. This allows for the effective collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may not. For example, the data collection unit can input visitors' social media posts, engagement data, and follower reactions into the generative AI and have the generative AI collect the relevant data.

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

[0091] The corporate appeal communication system can also include a user feedback collection unit. This unit directly collects feedback from website visitors and analyzes it to further enhance the company's appeal. For example, the feedback collection unit can place a survey form on the website to collect visitors' opinions and impressions. It can also input the collected feedback into a generating AI to extract specific areas for improvement to enhance the company's appeal. Furthermore, based on the collected feedback, the feedback collection unit can propose new content and services that visitors are looking for. This allows companies to continuously provide attractive content that meets the needs of their visitors.

[0092] The corporate appeal communication system can also include a competitive analysis department. This department analyzes competitors' websites and marketing strategies, providing data to relatively strengthen the company's own appeal. For example, it can analyze the features of competitors' products and services, clearly highlighting the advantages of the company's own products and services. It can also analyze the effectiveness of competitors' marketing campaigns, providing insights to optimize the company's own marketing strategy. Furthermore, it can collect customer feedback from competitors and propose specific measures to improve the company's own customer satisfaction. This allows companies to differentiate themselves from competitors and establish themselves as more attractive businesses.

[0093] The corporate appeal communication system can further enhance a company's appeal by analyzing employee emotions using its emotion estimation function. For example, by analyzing employee emotions using the emotion estimation function, projects and achievements that employees are proud of can be extracted as corporate appeal. It can also analyze employee emotions using the emotion estimation function and extract employee satisfaction with benefits and the work environment as corporate appeal. Furthermore, by evaluating employee emotions using the emotion estimation function, the company culture and teamwork that employees perceive as positive can be extracted as corporate appeal. In this way, by enhancing corporate appeal based on employee emotions, a more accurate appeal can be communicated.

[0094] The corporate appeal communication system can further analyze the emotions of website visitors using emotion estimation functionality, and optimize content based on their interests and preferences. For example, by using emotion estimation to analyze visitors' emotions, it can prioritize displaying content that visitors are likely to be interested in. It can also analyze visitors' emotions using emotion estimation functionality and update content to improve areas where visitors are dissatisfied. Furthermore, it can evaluate visitors' emotions using emotion estimation functionality and provide content by adding information that will satisfy them. In this way, a more attractive website can be provided by optimizing content based on visitors' emotions.

[0095] The corporate appeal communication system can further analyze the emotions of a company's customers using its emotion estimation function and propose measures to improve customer satisfaction. For example, by using the emotion estimation function to analyze customer emotions, it is possible to strengthen products and services that customers are particularly satisfied with. It is also possible to use the emotion estimation function to analyze customer emotions and propose specific measures to improve areas where customers are dissatisfied. Furthermore, it is possible to use the emotion estimation function to evaluate customer emotions and provide insights for developing new products and services that customers desire. In this way, customer satisfaction can be improved by proposing measures based on customer emotions.

[0096] The corporate appeal communication system can further analyze the emotions of employees using its emotion estimation function and propose measures to improve employee satisfaction. For example, by using the emotion estimation function to analyze employee emotions, it is possible to strengthen employee benefits and work environments that employees are particularly satisfied with. It is also possible to use the emotion estimation function to analyze employee emotions and propose specific measures to improve areas where employees are dissatisfied. Furthermore, by using the emotion estimation function to evaluate employee emotions, it is possible to provide insights for offering new employee benefits and work environments that employees desire. In this way, by proposing measures based on employee emotions, employee satisfaction can be improved.

[0097] The corporate appeal communication system can further analyze a company's social media activities using sentiment estimation functionality and propose measures to strengthen its online reputation. For example, it can analyze social media posts using sentiment estimation functionality and propose measures to enhance the company's positive image. It can also analyze social media engagement data using sentiment estimation functionality and propose measures to strengthen the company's popular content. Furthermore, it can analyze the reactions of social media followers using sentiment estimation functionality and propose measures to strengthen the company's strengths. In this way, by analyzing social media activities, it is possible to effectively strengthen the company's online reputation.

[0098] The corporate appeal communication system can also be equipped with a data visualization unit. This unit visually displays collected data, providing a tool for effectively communicating a company's appeal. For example, it can display a company's strengths and characteristics in graphs and charts, making them easy for viewers to understand. It can also display a company's history and product features as infographics. Furthermore, it can visualize a company's success stories and customer feedback to highlight its appeal. This allows for the effective communication of a company's appeal through visual data displays.

[0099] The corporate appeal communication system can also be equipped with a personalized content delivery unit. This unit provides content optimized for each individual visitor based on their website visitor behavior data. For example, it can analyze a visitor's past browsing history and prioritize displaying relevant content. It can also provide customized content based on the visitor's interests and preferences. Furthermore, it can provide content targeted at specific demographic groups based on the visitor's demographic information. This allows for improved visitor satisfaction by providing content optimized for each individual visitor.

[0100] The corporate appeal communication system can also be equipped with a real-time alert function. This function detects unusual activity on the company's website and social media and issues alerts for immediate response. For example, it can issue an alert if website traffic suddenly increases, prompting measures to reduce server load. It can also issue an alert if negative comments on social media surge, suggesting rapid response measures. Furthermore, the real-time alert function can detect security risks on the company's website and social media and issue alerts for immediate response. This allows for the protection of the company's online presence through real-time anomaly detection and response.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The Charm Discovery Department discovers the company's appeal. For example, it uses generative AI to analyze the company's internal and external data to extract its strengths and characteristics. Specifically, it analyzes the company's history, product features, employee voices, past success stories, and customer feedback to clarify the company's appeal. Step 2: The automated update unit reflects the appeal discovered by the appeal discovery unit onto the website. For example, it uses a generation AI to periodically collect the latest company information and reflect it on the website. Specifically, it automatically updates information on new products, event announcements, press releases, etc. It can also estimate the emotions of viewers and adjust the update content based on those estimated emotions. Step 3: The data collection unit collects data on website visitors. For example, it uses generated AI to analyze website visitor behavior data to understand which pages are frequently viewed and which content is popular. Specifically, it collects and analyzes behavioral data such as page views, click-through rates, and time spent on the site. It can also create marketing reports and customer analysis data based on the collected data and provide them as information products.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the appeal discovery unit, automatic update unit, and data collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the appeal discovery unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and analyzes the company's internal and external data to extract the company's strengths and characteristics. The automatic update unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and periodically collects the latest information on the company and reflects it on the website. The data collection unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and analyzes the behavioral data of website visitors to understand which pages are frequently viewed and which content is popular. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] Each of the multiple elements described above, including the appeal discovery unit, automatic update unit, and data collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the appeal discovery unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and analyzes the company's internal and external data to extract the company's strengths and characteristics. The automatic update unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and periodically collects the latest information on the company and reflects it on the website. The data collection unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and analyzes the behavioral data of website visitors to understand which pages are frequently viewed and which content is popular. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0138] Each of the multiple elements described above, including the appeal discovery unit, automatic update unit, and data collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the appeal discovery unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and analyzes the company's internal and external data to extract the company's strengths and characteristics. The automatic update unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and periodically collects the latest information on the company and reflects it on the website. The data collection unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and analyzes the behavioral data of website visitors to understand which pages are frequently viewed and which content is popular. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0153] 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.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0155] Each of the multiple elements described above, including the appeal discovery unit, automatic update unit, and data collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the appeal discovery unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and analyzes the company's internal and external data to extract the company's strengths and characteristics. The automatic update unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and periodically collects the latest information on the company and reflects it on the website. The data collection unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and analyzes the behavioral data of website visitors to understand which pages are frequently viewed and which content is popular. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) The company's appeal discovery department, An automatic update unit that reflects the attractions discovered by the aforementioned attraction discovery unit onto the website, It includes a data collection unit that collects data on HP visitors. A system characterized by the following features. (Note 2) The aforementioned attraction discovery unit is Analyze a company's internal and external data to extract its strengths and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned automatic update unit, Automatically updates information on new products, event announcements, press releases, and more. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data acquisition unit, By analyzing the behavioral data of website visitors, we can understand which pages are viewed most frequently and which content is popular. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data acquisition unit, The collected data will be used as an information product. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned attraction discovery unit is Estimate employee sentiment and extract the company's attractiveness based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned attraction discovery unit is Analyze past success stories of companies and extract the factors that contribute to their success as attractive features. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned attraction discovery unit is Analyze customer feedback from companies to extract what customers find appealing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned attraction discovery unit is The system estimates employee sentiment and determines attractiveness priorities based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned attraction discovery unit is Analyze data from a company's competitors and extract their differentiating points as attractive features. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned attraction discovery unit is Analyze a company's social media activities and extract its online reputation as an attractive feature. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned automatic update unit, The system estimates the emotions of website visitors and adjusts update content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned automatic update unit, It integrates with the company's internal systems and automatically updates inventory and pricing information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned automatic update unit, Automatically collects company news and press releases and displays them on the website. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned automatic update unit, The system estimates the sentiment of website visitors and adjusts the update frequency based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned automatic update unit, Automatically collects posts from company blogs and social media and displays them on the website. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned automatic update unit, Automatically updates the company's event calendar and provides the latest event information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned data acquisition unit, We estimate the emotions of website visitors and adjust the data collection method based on the estimated emotions of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data acquisition unit, Analyze the behavior patterns of website visitors and collect browsing data during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data acquisition unit, Collect demographic information from website visitors and use it for targeted marketing. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data acquisition unit, This system estimates the emotions of HP visitors and prioritizes data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data acquisition unit, Collect geographical location information of website visitors to understand their interests and preferences in different regions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned data acquisition unit, Analyze the social media activity of HP visitors and collect relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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. The company's appeal discovery department, An automatic update unit that reflects the attractions discovered by the aforementioned attraction discovery unit onto the website, It includes a data collection unit that collects data on HP visitors. A system characterized by the following features.

2. The aforementioned charm discovery unit is Analyze a company's internal and external data to extract its strengths and characteristics. The system according to feature 1.

3. The aforementioned automatic update unit, Automatically updates information on new products, event announcements, press releases, and more. The system according to feature 1.

4. The aforementioned data acquisition unit is By analyzing the behavioral data of website visitors, we can understand which pages are viewed most frequently and which content is popular. The system according to feature 1.

5. The aforementioned data acquisition unit is The collected data will be used as an information product. The system according to feature 1.

6. The aforementioned charm discovery unit is Estimate employee sentiment and extract the company's attractiveness based on that estimated sentiment. The system according to feature 1.

7. The aforementioned charm discovery unit is Analyze past success stories of companies and extract the factors that contribute to their success as attractive features. The system according to feature 1.

8. The aforementioned charm discovery unit is Analyze customer feedback from companies to extract what customers find appealing. The system according to feature 1.

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