system

Abstract

The system according to this embodiment aims to enable local businesses to generate content that effectively conveys the appeal of their products and services. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit provides an interface that allows local businesses to teach information to the AI ​​themselves, and also receives information input from a dedicated concierge. The analysis unit analyzes the information received by the reception unit and analyzes the specific challenges faced by local businesses. The generation unit generates stories and content for solving the challenges faced by local businesses based on the challenges analyzed by the analysis unit. The proposal unit generates successful case studies of regional development utilizing the products and services of local businesses based on the content generated by the generation unit.

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, local businesses lack the know-how and time to effectively convey the attractiveness of their own products and services, and there are problems in improving awareness.

[0005] The system according to the embodiment aims to generate content for local businesses to effectively convey the attractiveness of their own products and services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit provides an interface that allows local businesses to teach information to the AI ​​themselves, and also receives information input from a dedicated concierge. The analysis unit analyzes the information received by the reception unit and analyzes the specific challenges faced by local businesses. Based on the challenges analyzed by the analysis unit, the generation unit generates stories and content for solving the challenges faced by local businesses. Based on the content generated by the generation unit, the proposal unit generates successful case studies of regional development utilizing the products and services of local businesses. [Effects of the Invention]

[0007] The system according to this embodiment can generate content that effectively communicates the appeal of local businesses' products and services. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The content generation system according to an embodiment of the present invention is a system in which AI compiles the history and products of local businesses into text and videos to convey the charm of the local area. This system provides an interface that allows local businesses to teach information to the AI ​​themselves, or inputs information through a dedicated concierge. Next, the AI ​​analyzes the specific challenges faced by the local businesses from the information it has learned, and generates stories and content to solve those challenges. This content consists of text and videos introducing the history and products of the local businesses. Furthermore, the AI ​​can generate successful case studies of regional development utilizing the products and services of local businesses, which can be used as benchmarking material for other local businesses. For example, local businesses can provide information to the AI ​​themselves, or inputs information through a dedicated concierge. At this time, local businesses provide detailed information about their history and products. For example, they provide information such as the manufacturing process of their products, the raw materials used, and the characteristics of their products. This information is input into the AI. Next, the AI ​​analyzes the input information and analyzes the specific challenges faced by the local businesses. For example, it identifies challenges such as low product awareness or insufficient communication of the local charm. As a result, the AI ​​can grasp the challenges of local businesses and consider solutions. AI generates stories and content to address the challenges faced by local businesses. For example, it generates text and videos introducing the history and products of local businesses. This content is used to convey the appeal of local businesses' products. For instance, it generates videos introducing the product manufacturing process and text explaining the product's features. Furthermore, AI generates successful case studies of regional revitalization utilizing the products and services of local businesses. For example, it proposes event and workshop plans using local businesses' products. Such success stories are used as benchmarking material for other local businesses. This allows local businesses to refer to the success stories of other businesses and use them to improve their own products and services. Through this system, local businesses can easily generate content that promotes awareness and purchases among buyers. This is expected to contribute to increasing sales for local businesses.For example, local businesses can use generated content to showcase their products, attracting customer interest and increasing purchasing intent. Furthermore, other businesses can learn from successful examples of regional revitalization using local businesses' products and services to improve their own offerings. In this way, content generation systems can create content that introduces the history and products of local businesses, conveying the charm of the local area.

[0029] The content generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit provides an interface that allows local businesses to teach information to the AI ​​themselves, and also receives information input from a dedicated concierge. For example, the reception unit receives detailed information from local businesses about their history and products. For example, the reception unit provides information such as the product manufacturing process, the raw materials used, and the product's characteristics. This information is input into the AI. The analysis unit analyzes the information received by the reception unit and analyzes the specific challenges faced by the local businesses. For example, the analysis unit identifies challenges such as low product awareness and insufficient communication of local attractions. The analysis unit can use the AI ​​to understand the challenges faced by local businesses and devise solutions. Based on the challenges analyzed by the analysis unit, the generation unit generates stories and content for solving the challenges faced by local businesses. For example, the generation unit generates text and videos introducing the history and products of local businesses. The generation unit uses the AI ​​to generate content used to convey the appeal of the local businesses' products. For example, the generation unit generates videos introducing the product manufacturing process and text explaining the product's characteristics. The proposal unit generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the generation unit. For example, the proposal unit proposes plans for events and workshops using the products of local businesses. The proposal unit uses AI to generate successful case studies that can be used as benchmarking material for other local businesses. As a result, the content generation system according to the embodiment can generate content that introduces the history and products of local businesses and conveys the charm of the local area. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit inputs information provided by local businesses into the AI, and the AI ​​analyzes the information. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs information provided by local businesses into the AI, and the AI ​​analyzes the information. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs information provided by local businesses into the AI, the AI ​​analyzes the information, and generates content.Some or all of the above-mentioned processes in the proposal department are performed using AI. For example, the proposal department inputs the content generated by the generation department into the AI, and the AI ​​generates success stories.

[0030] The reception department provides an interface that allows local businesses to provide information to the AI ​​themselves, as well as inputting information through a dedicated concierge. Specifically, local businesses can input detailed information about their history and products through a dedicated web portal or application. This interface is user-friendly and designed to allow local businesses to easily input information. For example, it includes text input fields, image upload functions, and video embedding functions, allowing local businesses to input detailed information about their product manufacturing process, raw materials used, and product characteristics. It is also possible for a dedicated concierge to visit local businesses directly and collect information through interviews. The concierge plays a role in deeply understanding the needs and challenges of local businesses and inputting appropriate information into the AI. This allows the reception department to efficiently collect diverse information from local businesses and input it into the AI. Furthermore, the reception department can centrally manage the collected information and collaborate with other departments as needed. For example, the collected information is stored on a cloud server and made accessible to the analysis and generation departments. In addition, by adjusting the frequency and accuracy of information collection, flexible responses can be made according to specific situations and conditions. This allows the reception department to collect information efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes information received by the reception department to identify specific challenges faced by local businesses. Using AI, the analysis department can understand these challenges and develop solutions. Specifically, AI uses natural language processing technology to analyze text data provided by local businesses, identifying issues such as low product awareness or ineffective communication of local attractions. For example, AI can analyze reviews and feedback on local businesses' products to reveal consumer dissatisfaction. AI can also analyze data from local businesses' websites and social media to identify which content attracts the most attention. Furthermore, the analysis department can utilize historical data and statistics to analyze long-term challenges and trends. For instance, it can predict sales fluctuations during specific seasons or events based on past sales data, and develop future marketing strategies. Additionally, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The generation unit generates stories and content for solving the challenges faced by local businesses, based on the issues analyzed by the analysis unit. The generation unit uses AI to generate content used to convey the appeal of local businesses' products. Specifically, the AI ​​uses natural language generation technology to generate text and videos introducing the history and products of local businesses. For example, the AI ​​generates text that explains the product manufacturing process in detail, appealing to consumers about the product's quality and uniqueness. The AI ​​also generates catchy slogans and advertisements to highlight product features, supporting marketing activities. Furthermore, the generation unit can use AI to generate videos introducing local businesses' products. For example, it can edit footage of the product manufacturing process and add narration and subtitles to create visually appealing content. This allows the generation unit to generate diverse content that effectively conveys the appeal of local businesses' products and attracts consumer interest. Additionally, the generation unit centrally manages the generated content and can collaborate with other departments as needed. For example, the generated content is stored on a cloud server and made accessible to the proposal unit. Adjusting the frequency and accuracy of content generation allows for flexible responses to specific situations and conditions. This allows the generation unit to generate content efficiently and effectively, improving the overall performance of the system.

[0033] The Proposal Department generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the Generation Department. The Proposal Department uses AI to generate these success stories, which can be used as benchmarking material for other local businesses. Specifically, the AI ​​analyzes past success story data and proposes event and workshop plans utilizing the products and services of local businesses. For example, the AI ​​generates plans for cooking classes and hands-on workshops using local businesses' products, providing attractive experiences for local residents and tourists. Furthermore, the AI ​​proposes best practices that other local businesses can learn from, based on these successful case studies of regional development utilizing local businesses' products. This allows the Proposal Department to generate successful case studies of regional development utilizing local businesses' products and services, and provide them as benchmarking material for other local businesses. In addition, the Proposal Department centrally manages the generated success stories and can collaborate with other departments as needed. For example, the generated success stories are stored on a cloud server and made accessible to local businesses. Adjusting the frequency and accuracy of success story generation allows for flexible responses tailored to specific situations and conditions. This allows the proposal department to efficiently and effectively generate success stories and improve the overall system performance.

[0034] The generation unit can generate text and videos introducing the history and products of local businesses. For example, the generation unit can generate text introducing the history of a local business. For example, the generation unit can generate videos introducing the products of a local business. For example, the generation unit can generate text explaining the features of the products of a local business. In this way, the generation unit can generate content that effectively introduces the history and products of local businesses. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs information provided by the local business into the AI, the AI ​​analyzes the information, and generates text and videos.

[0035] The proposal department can propose event and workshop plans using products from local businesses. For example, the proposal department can propose experiential events using products from local businesses. For example, the proposal department can propose seminars using products from local businesses. For example, the proposal department can propose hands-on workshops using products from local businesses. In this way, the proposal department can propose event and workshop plans that utilize products from local businesses. Some or all of the above processing in the proposal department is performed using AI. For example, the proposal department inputs content generated by the generation department into the AI, and the AI ​​proposes event and workshop plans.

[0036] The proposal unit can generate experiential tourist routes using products from local businesses. For example, the proposal unit can suggest ways to visit tourist spots using products from local businesses. For example, the proposal unit can suggest the content of experiential activities using products from local businesses. For example, the proposal unit can suggest tourist routes using products from local businesses. In this way, the proposal unit can generate experiential tourist routes that utilize products from local businesses. Some or all of the above processing in the proposal unit is performed using AI. For example, the proposal unit inputs the content generated by the generation unit into the AI, and the AI ​​generates the tourist route.

[0037] The proposal department can establish a marketplace connecting local businesses and consumers, and utilize AI-generated content within it. For example, the proposal department could propose a marketplace for selling products from local businesses. For example, the proposal department could propose a review function for products from local businesses. For example, the proposal department could propose a payment function for purchasing products from local businesses. In this way, the proposal department can provide a marketplace connecting local businesses and consumers and utilize AI-generated content. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs content generated by the generation department into the AI, and the AI ​​proposes functions for the marketplace.

[0038] The generation unit can generate recipes using products from local businesses, as well as background stories about the creation of those products. For example, the generation unit can generate recipes for dishes using products from local businesses. For example, the generation unit can generate background stories about the creation of products from local businesses. For example, the generation unit can generate text explaining the development history of products from local businesses. In this way, the generation unit can generate recipes and background stories using products from local businesses. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs information provided by local businesses into the AI, and the AI ​​generates recipes and background stories.

[0039] The reception desk can analyze the local business's past input history and select the optimal input method. For example, the reception desk can automatically display as candidates information that the local business has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the local business has used in the past. For example, the reception desk can predict and suggest information to be used during a specific time period based on the local business's past input history. This allows the reception desk to select the optimal input method based on the local business's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the local business's past input data into a generating AI, and the generating AI can select the optimal input method.

[0040] The reception unit can filter input based on the local business operator's current business status and areas of interest. For example, the reception unit can display only relevant information based on the local business operator's current business status. For example, the reception unit can filter the input information based on the local business operator's areas of interest. For example, the reception unit can suggest the optimal input method based on the local business operator's business status and areas of interest. This allows the reception unit to filter information based on the local business operator's current business status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the local business operator's business status data into a generating AI, and the generating AI filters the information.

[0041] The reception unit can prioritize inputting highly relevant information by considering the geographical location information of local businesses during the input process. For example, the reception unit prioritizes inputting relevant information based on the location of local businesses. For example, the reception unit proposes the optimal input method by considering the geographical location information of local businesses. For example, the reception unit filters relevant information based on the geographical location information of local businesses. This allows the reception unit to prioritize inputting highly relevant information based on the geographical location information of local businesses. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the geographical location data of local businesses into a generating AI, and the generating AI inputs highly relevant information.

[0042] The reception unit can analyze the social media activities of local businesses and input relevant information during the input process. For example, the reception unit can analyze the social media activities of local businesses and prioritize inputting relevant information. For example, the reception unit can propose the optimal input method based on the social media activities of local businesses. For example, the reception unit can analyze the social media activities of local businesses and filter the relevant information. This allows the reception unit to input relevant information based on the social media activities of local businesses. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the social media data of local businesses into a generating AI, and the generating AI inputs relevant information.

[0043] The analysis unit can optimize its analysis algorithm by referring to the local business's past data during analysis. For example, the analysis unit selects the optimal analysis algorithm based on the local business's past data. For example, the analysis unit adjusts the analysis algorithm by referring to the local business's past data. For example, the analysis unit analyzes the local business's past data and proposes the optimal analysis method. This allows the analysis unit to optimize the analysis algorithm based on the local business's past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the local business's past data into a generating AI, and the generating AI optimizes the analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the business category of the local business operator during analysis. For example, the analysis unit selects the optimal analysis method based on the business category of the local business operator. For example, the analysis unit adjusts the analysis method according to the business category of the local business operator. For example, the analysis unit proposes different analysis methods based on the business category of the local business operator. This allows the analysis unit to apply the optimal analysis method based on the business category of the local business operator. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the business category data of the local business operator into a generating AI, and the generating AI applies the analysis method.

[0045] The analysis unit can perform analysis while considering the geographical distribution of local businesses. For example, the analysis unit selects the optimal analysis method based on the geographical distribution of local businesses. For example, the analysis unit adjusts the analysis method considering the geographical distribution of local businesses. For example, the analysis unit proposes different analysis methods based on the geographical distribution of local businesses. This allows the analysis unit to apply the optimal analysis method based on the geographical distribution of local businesses. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs geographical distribution data of local businesses into a generating AI, and the generating AI applies an analysis method.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on local businesses during the analysis. For example, the analysis unit optimizes the analysis algorithm by referring to relevant literature on local businesses. For example, the analysis unit adjusts the analysis method based on relevant literature on local businesses. For example, the analysis unit improves the accuracy of the analysis results by referring to relevant literature on local businesses. In this way, the analysis unit can improve the accuracy of its analysis based on relevant literature on local businesses. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs relevant literature data on local businesses into a generating AI, and the generating AI optimizes the analysis algorithm.

[0047] The generation unit can optimize its generation algorithm by referring to the local business's past content when generating content. For example, the generation unit selects the optimal generation algorithm based on the local business's past content. For example, the generation unit adjusts the generation algorithm by referring to the local business's past content. For example, the generation unit analyzes the local business's past content and proposes the optimal generation method. This allows the generation unit to optimize its generation algorithm based on the local business's past content. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the local business's past content data into a generation AI, and the generation AI optimizes the generation algorithm.

[0048] The generation unit can apply different generation methods depending on the business category of the local business operator when generating content. For example, the generation unit selects the optimal generation method based on the business category of the local business operator. For example, the generation unit adjusts the generation method depending on the business category of the local business operator. For example, the generation unit proposes different generation methods based on the business category of the local business operator. This allows the generation unit to apply the optimal generation method based on the business category of the local business operator. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs the business category data of the local business operator into a generation AI, and the generation AI applies a generation method.

[0049] The generation unit can perform content generation while considering the geographical distribution of local businesses. For example, the generation unit can prioritize the generation of relevant content based on the location of local businesses. For example, the generation unit can propose the optimal generation method considering the geographical distribution of local businesses. For example, the generation unit can filter relevant content based on the geographical distribution of local businesses. This allows the generation unit to generate optimal content based on the geographical distribution of local businesses. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs geographical distribution data of local businesses into a generation AI, and the generation AI generates the content.

[0050] The generation unit can improve the accuracy of content generation by referring to relevant literature from local businesses. For example, the generation unit optimizes the generation algorithm by referring to relevant literature from local businesses. For example, the generation unit adjusts the generation method based on relevant literature from local businesses. For example, the generation unit improves the accuracy of the generation results by referring to relevant literature from local businesses. In this way, the generation unit can improve the accuracy of generation based on relevant literature from local businesses. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs relevant literature data from local businesses into a generation AI, and the generation AI optimizes the generation algorithm.

[0051] The proposal unit can optimize its proposal algorithm by referring to the local business operator's past proposal history when making a proposal. For example, the proposal unit selects the optimal proposal algorithm based on the local business operator's past proposal history. For example, the proposal unit adjusts the proposal algorithm by referring to the local business operator's past proposal history. For example, the proposal unit analyzes the local business operator's past proposal history and proposes the optimal proposal method. In this way, the proposal unit can optimize its proposal algorithm based on the local business operator's past proposal history. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the local business operator's past proposal history data into a generating AI, and the generating AI optimizes the proposal algorithm.

[0052] The proposal unit can apply different proposal methods depending on the business category of the local business operator when making a proposal. For example, the proposal unit selects the optimal proposal method based on the business category of the local business operator. For example, the proposal unit adjusts the proposal method depending on the business category of the local business operator. For example, the proposal unit proposes different proposal methods based on the business category of the local business operator. This allows the proposal unit to apply the optimal proposal method based on the business category of the local business operator. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the business category data of the local business operator into a generating AI, and the generating AI applies a proposal method.

[0053] The proposal unit can make proposals considering the geographical distribution of local businesses. For example, the proposal unit can prioritize relevant proposals based on the location of local businesses. For example, the proposal unit can propose the optimal proposal method considering the geographical distribution of local businesses. For example, the proposal unit can filter relevant proposals based on the geographical distribution of local businesses. This allows the proposal unit to make optimal proposals based on the geographical distribution of local businesses. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs geographical distribution data of local businesses into a generating AI, and the generating AI makes proposals.

[0054] The proposal unit can improve the accuracy of its proposals by referring to relevant literature from local businesses during the proposal process. For example, the proposal unit optimizes the proposed algorithm by referring to relevant literature from local businesses. For example, the proposal unit adjusts the proposed method based on relevant literature from local businesses. For example, the proposal unit improves the accuracy of the proposed results by referring to relevant literature from local businesses. In this way, the proposal unit can improve the accuracy of its proposals based on relevant literature from local businesses. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit inputs relevant literature data from local businesses into a generating AI, and the generating AI optimizes the proposed algorithm.

[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 reception department can analyze the local business's past input history and select the optimal input method. For example, it can automatically display as suggestions information that the local business has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the local business has used in the past. Furthermore, it can predict and suggest information to be used during specific time periods based on the local business's past input history. This allows the reception department to select the optimal input method based on the local business's past input history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department inputs the local business's past input data into a generating AI, and the generating AI selects the optimal input method.

[0057] The analysis unit can optimize its analysis algorithm by referring to the local business's past data during analysis. For example, it can select the optimal analysis algorithm based on the local business's past data. It can also adjust the analysis algorithm by referring to the local business's past data. Furthermore, it can analyze the local business's past data and propose the optimal analysis method. In this way, the analysis unit can optimize the analysis algorithm based on the local business's past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the local business's past data into a generating AI, and the generating AI optimizes the analysis algorithm.

[0058] The generation unit can optimize its generation algorithm by referring to the local business's past content during content generation. For example, it can select the optimal generation algorithm based on the local business's past content. It can also adjust the generation algorithm by referring to the local business's past content. Furthermore, it can analyze the local business's past content and propose the optimal generation method. This allows the generation unit to optimize its generation algorithm based on the local business's past content. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the local business's past content data into a generation AI, and the generation AI optimizes the generation algorithm.

[0059] The proposal unit can optimize its proposal algorithm by referring to the local business operator's past proposal history when making a proposal. For example, it can select the optimal proposal algorithm based on the local business operator's past proposal history. It can also adjust the proposal algorithm by referring to the local business operator's past proposal history. Furthermore, it can analyze the local business operator's past proposal history and propose the optimal proposal method. In this way, the proposal unit can optimize the proposal algorithm based on the local business operator's past proposal history. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the local business operator's past proposal history data into a generating AI, and the generating AI optimizes the proposal algorithm.

[0060] The reception unit can filter input based on the local business operator's current business status and areas of interest. For example, it can display only relevant information based on the local business operator's current business status. It can also filter the input information based on the local business operator's areas of interest. Furthermore, it can suggest the optimal input method based on the local business operator's business status and areas of interest. This allows the reception unit to filter information based on the local business operator's current business status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the local business operator's business status data into a generating AI, and the generating AI filters the information.

[0061] The proposal unit can make proposals while considering the geographical distribution of local businesses. For example, it can prioritize relevant proposals based on the location of local businesses. It can also propose the optimal proposal method considering the geographical distribution of local businesses. Furthermore, it can filter relevant proposals based on the geographical distribution of local businesses. This allows the proposal unit to make optimal proposals based on the geographical distribution of local businesses. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs geographical distribution data of local businesses into a generating AI, and the generating AI makes proposals.

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

[0063] Step 1: The reception area provides an interface that allows local businesses to teach the AI ​​information themselves, as well as inputting information through a dedicated concierge. For example, local businesses provide detailed information about their company history and products, inputting information such as the product manufacturing process, raw materials used, and product characteristics into the AI. Step 2: The analysis department analyzes the information received by the reception department and identifies the specific challenges faced by local businesses. For example, it identifies issues such as low product awareness or ineffective communication of local attractions, and uses AI to devise solutions. Step 3: The generation unit generates stories and content for solving the challenges faced by local businesses, based on the issues analyzed by the analysis unit. For example, it generates text and videos introducing the history and products of local businesses, as well as videos introducing the product manufacturing process and text explaining the product's features. Step 4: The proposal department generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the generation department. For example, it proposes event and workshop plans using the products of local businesses, generating successful case studies that can be used as benchmarking material for other local businesses.

[0064] (Example of form 2) The content generation system according to an embodiment of the present invention is a system in which AI compiles the history and products of local businesses into text and videos to convey the charm of the local area. This system provides an interface that allows local businesses to teach information to the AI ​​themselves, or inputs information through a dedicated concierge. Next, the AI ​​analyzes the specific challenges faced by the local businesses from the information it has learned, and generates stories and content to solve those challenges. This content consists of text and videos introducing the history and products of the local businesses. Furthermore, the AI ​​can generate successful case studies of regional development utilizing the products and services of local businesses, which can be used as benchmarking material for other local businesses. For example, local businesses can provide information to the AI ​​themselves, or inputs information through a dedicated concierge. At this time, local businesses provide detailed information about their history and products. For example, they provide information such as the manufacturing process of their products, the raw materials used, and the characteristics of their products. This information is input into the AI. Next, the AI ​​analyzes the input information and analyzes the specific challenges faced by the local businesses. For example, it identifies challenges such as low product awareness or insufficient communication of the local charm. As a result, the AI ​​can grasp the challenges of local businesses and consider solutions. AI generates stories and content to address the challenges faced by local businesses. For example, it generates text and videos introducing the history and products of local businesses. This content is used to convey the appeal of local businesses' products. For instance, it generates videos introducing the product manufacturing process and text explaining the product's features. Furthermore, AI generates successful case studies of regional revitalization utilizing the products and services of local businesses. For example, it proposes event and workshop plans using local businesses' products. Such success stories are used as benchmarking material for other local businesses. This allows local businesses to refer to the success stories of other businesses and use them to improve their own products and services. Through this system, local businesses can easily generate content that promotes awareness and purchases among buyers. This is expected to contribute to increasing sales for local businesses.For example, local businesses can use generated content to showcase their products, attracting customer interest and increasing purchasing intent. Furthermore, other businesses can learn from successful examples of regional revitalization using local businesses' products and services to improve their own offerings. In this way, content generation systems can create content that introduces the history and products of local businesses, conveying the charm of the local area.

[0065] The content generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit provides an interface that allows local businesses to teach information to the AI ​​themselves, and also receives information input from a dedicated concierge. For example, the reception unit receives detailed information from local businesses about their history and products. For example, the reception unit provides information such as the product manufacturing process, the raw materials used, and the product's characteristics. This information is input into the AI. The analysis unit analyzes the information received by the reception unit and analyzes the specific challenges faced by the local businesses. For example, the analysis unit identifies challenges such as low product awareness and insufficient communication of local attractions. The analysis unit can use the AI ​​to understand the challenges faced by local businesses and devise solutions. Based on the challenges analyzed by the analysis unit, the generation unit generates stories and content for solving the challenges faced by local businesses. For example, the generation unit generates text and videos introducing the history and products of local businesses. The generation unit uses the AI ​​to generate content used to convey the appeal of the local businesses' products. For example, the generation unit generates videos introducing the product manufacturing process and text explaining the product's characteristics. The proposal unit generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the generation unit. For example, the proposal unit proposes plans for events and workshops using the products of local businesses. The proposal unit uses AI to generate successful case studies that can be used as benchmarking material for other local businesses. As a result, the content generation system according to the embodiment can generate content that introduces the history and products of local businesses and conveys the charm of the local area. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit inputs information provided by local businesses into the AI, and the AI ​​analyzes the information. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs information provided by local businesses into the AI, and the AI ​​analyzes the information. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs information provided by local businesses into the AI, the AI ​​analyzes the information, and generates content.Some or all of the above-mentioned processes in the proposal department are performed using AI. For example, the proposal department inputs the content generated by the generation department into the AI, and the AI ​​generates success stories.

[0066] The reception department provides an interface that allows local businesses to provide information to the AI ​​themselves, as well as inputting information through a dedicated concierge. Specifically, local businesses can input detailed information about their history and products through a dedicated web portal or application. This interface is user-friendly and designed to allow local businesses to easily input information. For example, it includes text input fields, image upload functions, and video embedding functions, allowing local businesses to input detailed information about their product manufacturing process, raw materials used, and product characteristics. It is also possible for a dedicated concierge to visit local businesses directly and collect information through interviews. The concierge plays a role in deeply understanding the needs and challenges of local businesses and inputting appropriate information into the AI. This allows the reception department to efficiently collect diverse information from local businesses and input it into the AI. Furthermore, the reception department can centrally manage the collected information and collaborate with other departments as needed. For example, the collected information is stored on a cloud server and made accessible to the analysis and generation departments. In addition, by adjusting the frequency and accuracy of information collection, flexible responses can be made according to specific situations and conditions. This allows the reception department to collect information efficiently and effectively, improving the overall performance of the system.

[0067] The analysis department analyzes information received by the reception department to identify specific challenges faced by local businesses. Using AI, the analysis department can understand these challenges and develop solutions. Specifically, AI uses natural language processing technology to analyze text data provided by local businesses, identifying issues such as low product awareness or ineffective communication of local attractions. For example, AI can analyze reviews and feedback on local businesses' products to reveal consumer dissatisfaction. AI can also analyze data from local businesses' websites and social media to identify which content attracts the most attention. Furthermore, the analysis department can utilize historical data and statistics to analyze long-term challenges and trends. For instance, it can predict sales fluctuations during specific seasons or events based on past sales data, and develop future marketing strategies. Additionally, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0068] The generation unit generates stories and content for solving the challenges faced by local businesses, based on the issues analyzed by the analysis unit. The generation unit uses AI to generate content used to convey the appeal of local businesses' products. Specifically, the AI ​​uses natural language generation technology to generate text and videos introducing the history and products of local businesses. For example, the AI ​​generates text that explains the product manufacturing process in detail, appealing to consumers about the product's quality and uniqueness. The AI ​​also generates catchy slogans and advertisements to highlight product features, supporting marketing activities. Furthermore, the generation unit can use AI to generate videos introducing local businesses' products. For example, it can edit footage of the product manufacturing process and add narration and subtitles to create visually appealing content. This allows the generation unit to generate diverse content that effectively conveys the appeal of local businesses' products and attracts consumer interest. Additionally, the generation unit centrally manages the generated content and can collaborate with other departments as needed. For example, the generated content is stored on a cloud server and made accessible to the proposal unit. Adjusting the frequency and accuracy of content generation allows for flexible responses to specific situations and conditions. This allows the generation unit to generate content efficiently and effectively, improving the overall performance of the system.

[0069] The Proposal Department generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the Generation Department. The Proposal Department uses AI to generate these success stories, which can be used as benchmarking material for other local businesses. Specifically, the AI ​​analyzes past success story data and proposes event and workshop plans utilizing the products and services of local businesses. For example, the AI ​​generates plans for cooking classes and hands-on workshops using local businesses' products, providing attractive experiences for local residents and tourists. Furthermore, the AI ​​proposes best practices that other local businesses can learn from, based on these successful case studies of regional development utilizing local businesses' products. This allows the Proposal Department to generate successful case studies of regional development utilizing local businesses' products and services, and provide them as benchmarking material for other local businesses. In addition, the Proposal Department centrally manages the generated success stories and can collaborate with other departments as needed. For example, the generated success stories are stored on a cloud server and made accessible to local businesses. Adjusting the frequency and accuracy of success story generation allows for flexible responses tailored to specific situations and conditions. This allows the proposal department to efficiently and effectively generate success stories and improve the overall system performance.

[0070] The generation unit can generate text and videos introducing the history and products of local businesses. For example, the generation unit can generate text introducing the history of a local business. For example, the generation unit can generate videos introducing the products of a local business. For example, the generation unit can generate text explaining the features of the products of a local business. In this way, the generation unit can generate content that effectively introduces the history and products of local businesses. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs information provided by the local business into the AI, the AI ​​analyzes the information, and generates text and videos.

[0071] The proposal department can propose event and workshop plans using products from local businesses. For example, the proposal department can propose experiential events using products from local businesses. For example, the proposal department can propose seminars using products from local businesses. For example, the proposal department can propose hands-on workshops using products from local businesses. In this way, the proposal department can propose event and workshop plans that utilize products from local businesses. Some or all of the above processing in the proposal department is performed using AI. For example, the proposal department inputs content generated by the generation department into the AI, and the AI ​​proposes event and workshop plans.

[0072] The proposal unit can generate experiential tourist routes using products from local businesses. For example, the proposal unit can suggest ways to visit tourist spots using products from local businesses. For example, the proposal unit can suggest the content of experiential activities using products from local businesses. For example, the proposal unit can suggest tourist routes using products from local businesses. In this way, the proposal unit can generate experiential tourist routes that utilize products from local businesses. Some or all of the above processing in the proposal unit is performed using AI. For example, the proposal unit inputs the content generated by the generation unit into the AI, and the AI ​​generates the tourist route.

[0073] The proposal department can establish a marketplace connecting local businesses and consumers, and utilize AI-generated content within it. For example, the proposal department could propose a marketplace for selling products from local businesses. For example, the proposal department could propose a review function for products from local businesses. For example, the proposal department could propose a payment function for purchasing products from local businesses. In this way, the proposal department can provide a marketplace connecting local businesses and consumers and utilize AI-generated content. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs content generated by the generation department into the AI, and the AI ​​proposes functions for the marketplace.

[0074] The generation unit can generate recipes using products from local businesses, as well as background stories about the creation of those products. For example, the generation unit can generate recipes for dishes using products from local businesses. For example, the generation unit can generate background stories about the creation of products from local businesses. For example, the generation unit can generate text explaining the development history of products from local businesses. In this way, the generation unit can generate recipes and background stories using products from local businesses. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs information provided by local businesses into the AI, and the AI ​​generates recipes and background stories.

[0075] The reception desk can estimate the emotions of local businesses and adjust the information input method based on the estimated emotions. For example, if a local business is tense, the reception desk provides a simple and intuitive interface and minimizes the input steps. For example, if a local business is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if a local business is in a hurry, the reception desk prioritizes voice input to allow for quick information input. This allows the reception desk to adjust the information input method according to the emotions of the local business. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the local business's facial expression data into a generative AI, which estimates emotions and adjusts the input method.

[0076] The reception desk can analyze the local business's past input history and select the optimal input method. For example, the reception desk can automatically display as candidates information that the local business has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the local business has used in the past. For example, the reception desk can predict and suggest information to be used during a specific time period based on the local business's past input history. This allows the reception desk to select the optimal input method based on the local business's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the local business's past input data into a generating AI, and the generating AI can select the optimal input method.

[0077] The reception unit can filter input based on the local business operator's current business status and areas of interest. For example, the reception unit can display only relevant information based on the local business operator's current business status. For example, the reception unit can filter the input information based on the local business operator's areas of interest. For example, the reception unit can suggest the optimal input method based on the local business operator's business status and areas of interest. This allows the reception unit to filter information based on the local business operator's current business status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the local business operator's business status data into a generating AI, and the generating AI filters the information.

[0078] The reception desk can estimate the emotions of local businesses and determine the priority of the information to be input based on the estimated emotions. For example, if a local business is stressed, the reception desk will prioritize inputting important information. For example, if a local business is relaxed, the reception desk will input detailed information. For example, if a local business is in a hurry, the reception desk will quickly input the most important information. In this way, the reception desk can determine the priority of the information to be input according to the emotions of the local business. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk inputs the local business's facial expression data into the generative AI, the generative AI estimates the emotions and determines the priority of the information to be input.

[0079] The reception unit can prioritize inputting highly relevant information by considering the geographical location information of local businesses during the input process. For example, the reception unit prioritizes inputting relevant information based on the location of local businesses. For example, the reception unit proposes the optimal input method by considering the geographical location information of local businesses. For example, the reception unit filters relevant information based on the geographical location information of local businesses. This allows the reception unit to prioritize inputting highly relevant information based on the geographical location information of local businesses. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the geographical location data of local businesses into a generating AI, and the generating AI inputs highly relevant information.

[0080] The reception unit can analyze the social media activities of local businesses and input relevant information during the input process. For example, the reception unit can analyze the social media activities of local businesses and prioritize inputting relevant information. For example, the reception unit can propose the optimal input method based on the social media activities of local businesses. For example, the reception unit can analyze the social media activities of local businesses and filter the relevant information. This allows the reception unit to input relevant information based on the social media activities of local businesses. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the social media data of local businesses into a generating AI, and the generating AI inputs relevant information.

[0081] The analysis unit can estimate the emotions of local businesses and adjust the problem analysis method based on the estimated emotions. For example, if a local business is tense, the analysis unit provides a simple and intuitive analysis method. For example, if a local business is relaxed, the analysis unit provides a detailed analysis method. For example, if a local business is in a hurry, the analysis unit provides analysis results quickly. This allows the analysis unit to adjust the problem analysis method according to the emotions of the local business. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs the local business's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the analysis method.

[0082] The analysis unit can optimize its analysis algorithm by referring to the local business's past data during analysis. For example, the analysis unit selects the optimal analysis algorithm based on the local business's past data. For example, the analysis unit adjusts the analysis algorithm by referring to the local business's past data. For example, the analysis unit analyzes the local business's past data and proposes the optimal analysis method. This allows the analysis unit to optimize the analysis algorithm based on the local business's past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the local business's past data into a generating AI, and the generating AI optimizes the analysis algorithm.

[0083] The analysis unit can apply different analysis methods depending on the business category of the local business operator during analysis. For example, the analysis unit selects the optimal analysis method based on the business category of the local business operator. For example, the analysis unit adjusts the analysis method according to the business category of the local business operator. For example, the analysis unit proposes different analysis methods based on the business category of the local business operator. This allows the analysis unit to apply the optimal analysis method based on the business category of the local business operator. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the business category data of the local business operator into a generating AI, and the generating AI applies the analysis method.

[0084] The analysis unit can estimate the emotions of local businesses and adjust the display method of the analysis results based on the estimated emotions. For example, if a local business is tense, the analysis unit provides a simple and highly visible display method. For example, if a local business is relaxed, the analysis unit provides a display method that includes detailed information. For example, if a local business is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the emotions of the local business. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs the local business's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the display method.

[0085] The analysis unit can perform analysis while considering the geographical distribution of local businesses. For example, the analysis unit selects the optimal analysis method based on the geographical distribution of local businesses. For example, the analysis unit adjusts the analysis method considering the geographical distribution of local businesses. For example, the analysis unit proposes different analysis methods based on the geographical distribution of local businesses. This allows the analysis unit to apply the optimal analysis method based on the geographical distribution of local businesses. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs geographical distribution data of local businesses into a generating AI, and the generating AI applies an analysis method.

[0086] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on local businesses during the analysis. For example, the analysis unit optimizes the analysis algorithm by referring to relevant literature on local businesses. For example, the analysis unit adjusts the analysis method based on relevant literature on local businesses. For example, the analysis unit improves the accuracy of the analysis results by referring to relevant literature on local businesses. In this way, the analysis unit can improve the accuracy of its analysis based on relevant literature on local businesses. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs relevant literature data on local businesses into a generating AI, and the generating AI optimizes the analysis algorithm.

[0087] The generation unit can estimate the emotions of local businesses and adjust the content generation method based on the estimated emotions. For example, if a local business is relaxed, the generation unit will generate content that proceeds at a leisurely pace. For example, if a local business is in a hurry, the generation unit will generate content that emphasizes the shortest route. For example, if a local business is excited, the generation unit will generate content with visually stimulating effects. In this way, the generation unit can adjust the content generation method according to the emotions of local businesses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit inputs the local business's facial expression data into the generation AI, the generation AI estimates the emotions, and adjusts the generation method.

[0088] The generation unit can optimize its generation algorithm by referring to the local business's past content when generating content. For example, the generation unit selects the optimal generation algorithm based on the local business's past content. For example, the generation unit adjusts the generation algorithm by referring to the local business's past content. For example, the generation unit analyzes the local business's past content and proposes the optimal generation method. This allows the generation unit to optimize its generation algorithm based on the local business's past content. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the local business's past content data into a generation AI, and the generation AI optimizes the generation algorithm.

[0089] The generation unit can apply different generation methods depending on the business category of the local business operator when generating content. For example, the generation unit selects the optimal generation method based on the business category of the local business operator. For example, the generation unit adjusts the generation method depending on the business category of the local business operator. For example, the generation unit proposes different generation methods based on the business category of the local business operator. This allows the generation unit to apply the optimal generation method based on the business category of the local business operator. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs the business category data of the local business operator into a generation AI, and the generation AI applies a generation method.

[0090] The generation unit can estimate the emotions of local businesses and determine the priority of content to generate based on the estimated emotions. For example, if a local business is stressed, the generation unit will prioritize generating important content. For example, if a local business is relaxed, the generation unit will generate detailed content. For example, if a local business is in a hurry, the generation unit will quickly generate the most important content. In this way, the generation unit can determine the priority of content to generate according to the emotions of local businesses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs the local business's facial expression data into the generation AI, which estimates emotions and determines the priority of content.

[0091] The generation unit can perform content generation while considering the geographical distribution of local businesses. For example, the generation unit can prioritize the generation of relevant content based on the location of local businesses. For example, the generation unit can propose the optimal generation method considering the geographical distribution of local businesses. For example, the generation unit can filter relevant content based on the geographical distribution of local businesses. This allows the generation unit to generate optimal content based on the geographical distribution of local businesses. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs geographical distribution data of local businesses into a generation AI, and the generation AI generates the content.

[0092] The generation unit can improve the accuracy of content generation by referring to relevant literature from local businesses. For example, the generation unit optimizes the generation algorithm by referring to relevant literature from local businesses. For example, the generation unit adjusts the generation method based on relevant literature from local businesses. For example, the generation unit improves the accuracy of the generation results by referring to relevant literature from local businesses. In this way, the generation unit can improve the accuracy of generation based on relevant literature from local businesses. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs relevant literature data from local businesses into a generation AI, and the generation AI optimizes the generation algorithm.

[0093] The proposal unit can estimate the emotions of local businesses and adjust its proposal method based on the estimated emotions. For example, if a local business is tense, the proposal unit provides a simple and intuitive proposal method. For example, if a local business is relaxed, the proposal unit provides a detailed proposal method. For example, if a local business is in a hurry, the proposal unit provides proposal results quickly. This allows the proposal unit to adjust its proposal method according to the emotions of the local business. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the local business's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the proposal method.

[0094] The proposal unit can optimize its proposal algorithm by referring to the local business operator's past proposal history when making a proposal. For example, the proposal unit selects the optimal proposal algorithm based on the local business operator's past proposal history. For example, the proposal unit adjusts the proposal algorithm by referring to the local business operator's past proposal history. For example, the proposal unit analyzes the local business operator's past proposal history and proposes the optimal proposal method. In this way, the proposal unit can optimize its proposal algorithm based on the local business operator's past proposal history. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the local business operator's past proposal history data into a generating AI, and the generating AI optimizes the proposal algorithm.

[0095] The proposal unit can apply different proposal methods depending on the business category of the local business operator when making a proposal. For example, the proposal unit selects the optimal proposal method based on the business category of the local business operator. For example, the proposal unit adjusts the proposal method depending on the business category of the local business operator. For example, the proposal unit proposes different proposal methods based on the business category of the local business operator. This allows the proposal unit to apply the optimal proposal method based on the business category of the local business operator. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the business category data of the local business operator into a generating AI, and the generating AI applies a proposal method.

[0096] The proposal department can estimate the emotions of local businesses and determine the priority of proposals based on the estimated emotions. For example, if a local business is feeling stressed, the proposal department will prioritize important proposals. For example, if a local business is relaxed, the proposal department will provide detailed proposals. For example, if a local business is in a hurry, the proposal department will quickly provide the most important proposals. In this way, the proposal department can determine the priority of proposals according to the emotions of local businesses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department inputs facial expression data of local businesses into a generative AI, the generative AI estimates emotions, and determines the priority of proposals.

[0097] The proposal unit can make proposals considering the geographical distribution of local businesses. For example, the proposal unit can prioritize relevant proposals based on the location of local businesses. For example, the proposal unit can propose the optimal proposal method considering the geographical distribution of local businesses. For example, the proposal unit can filter relevant proposals based on the geographical distribution of local businesses. This allows the proposal unit to make optimal proposals based on the geographical distribution of local businesses. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs geographical distribution data of local businesses into a generating AI, and the generating AI makes proposals.

[0098] The proposal unit can improve the accuracy of its proposals by referring to relevant literature from local businesses during the proposal process. For example, the proposal unit optimizes the proposed algorithm by referring to relevant literature from local businesses. For example, the proposal unit adjusts the proposed method based on relevant literature from local businesses. For example, the proposal unit improves the accuracy of the proposed results by referring to relevant literature from local businesses. In this way, the proposal unit can improve the accuracy of its proposals based on relevant literature from local businesses. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit inputs relevant literature data from local businesses into a generating AI, and the generating AI optimizes the proposed algorithm.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can estimate the emotions of local businesses and adjust the information input method based on the estimated emotions. For example, if a local business is tense, a simple and intuitive interface can be provided, minimizing the input steps. If a local business is relaxed, detailed input options can be provided, and a customizable input method can be suggested. Furthermore, if a local business is in a hurry, voice input can be prioritized to allow for quick information input. In this way, the reception desk can adjust the information input method according to the emotions of local businesses. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the local business's facial expression data into a generative AI, which estimates emotions and adjusts the input method.

[0101] The analysis unit can estimate the emotions of local businesses and adjust the problem analysis method based on the estimated emotions. For example, if a local business is tense, it can provide a simple and intuitive analysis method. If a local business is relaxed, it can provide a detailed analysis method. Furthermore, if a local business is in a hurry, it can provide analysis results quickly. In this way, the analysis unit can adjust the problem analysis method according to the emotions of local businesses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs the local business's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the analysis method.

[0102] The generation unit can estimate the emotions of local businesses and adjust the content generation method based on the estimated emotions. For example, if a local business is relaxed, it can generate content that proceeds at a leisurely pace. If a local business is in a hurry, it can generate content that emphasizes the shortest route. Furthermore, if a local business is excited, it can generate content with visually stimulating effects. In this way, the generation unit can adjust the content generation method according to the emotions of local businesses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs the local business's facial expression data into the generation AI, the generation AI estimates the emotions, and adjusts the generation method.

[0103] The proposal unit can estimate the emotions of local businesses and adjust its proposal method based on the estimated emotions. For example, if a local business is tense, it can provide a simple and intuitive proposal method. If the local business is relaxed, it can provide a detailed proposal method. Furthermore, if the local business is in a hurry, it can provide proposal results quickly. In this way, the proposal unit can adjust its proposal method according to the emotions of the local business. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not using AI. For example, the proposal unit inputs the local business's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the proposal method.

[0104] The reception department can analyze the local business's past input history and select the optimal input method. For example, it can automatically display as suggestions information that the local business has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the local business has used in the past. Furthermore, it can predict and suggest information to be used during specific time periods based on the local business's past input history. This allows the reception department to select the optimal input method based on the local business's past input history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department inputs the local business's past input data into a generating AI, and the generating AI selects the optimal input method.

[0105] The analysis unit can optimize its analysis algorithm by referring to the local business's past data during analysis. For example, it can select the optimal analysis algorithm based on the local business's past data. It can also adjust the analysis algorithm by referring to the local business's past data. Furthermore, it can analyze the local business's past data and propose the optimal analysis method. In this way, the analysis unit can optimize the analysis algorithm based on the local business's past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs the local business's past data into a generating AI, and the generating AI optimizes the analysis algorithm.

[0106] The generation unit can optimize its generation algorithm by referring to the local business's past content during content generation. For example, it can select the optimal generation algorithm based on the local business's past content. It can also adjust the generation algorithm by referring to the local business's past content. Furthermore, it can analyze the local business's past content and propose the optimal generation method. This allows the generation unit to optimize its generation algorithm based on the local business's past content. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the local business's past content data into a generation AI, and the generation AI optimizes the generation algorithm.

[0107] The proposal unit can optimize its proposal algorithm by referring to the local business operator's past proposal history when making a proposal. For example, it can select the optimal proposal algorithm based on the local business operator's past proposal history. It can also adjust the proposal algorithm by referring to the local business operator's past proposal history. Furthermore, it can analyze the local business operator's past proposal history and propose the optimal proposal method. In this way, the proposal unit can optimize the proposal algorithm based on the local business operator's past proposal history. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs the local business operator's past proposal history data into a generating AI, and the generating AI optimizes the proposal algorithm.

[0108] The reception unit can filter input based on the local business operator's current business status and areas of interest. For example, it can display only relevant information based on the local business operator's current business status. It can also filter the input information based on the local business operator's areas of interest. Furthermore, it can suggest the optimal input method based on the local business operator's business status and areas of interest. This allows the reception unit to filter information based on the local business operator's current business status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the local business operator's business status data into a generating AI, and the generating AI filters the information.

[0109] The proposal unit can make proposals while considering the geographical distribution of local businesses. For example, it can prioritize relevant proposals based on the location of local businesses. It can also propose the optimal proposal method considering the geographical distribution of local businesses. Furthermore, it can filter relevant proposals based on the geographical distribution of local businesses. This allows the proposal unit to make optimal proposals based on the geographical distribution of local businesses. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit inputs geographical distribution data of local businesses into a generating AI, and the generating AI makes proposals.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception area provides an interface that allows local businesses to teach the AI ​​information themselves, as well as inputting information through a dedicated concierge. For example, local businesses provide detailed information about their company history and products, inputting information such as the product manufacturing process, raw materials used, and product characteristics into the AI. Step 2: The analysis department analyzes the information received by the reception department and identifies the specific challenges faced by local businesses. For example, it identifies issues such as low product awareness or ineffective communication of local attractions, and uses AI to devise solutions. Step 3: The generation unit generates stories and content for solving the challenges faced by local businesses, based on the issues analyzed by the analysis unit. For example, it generates text and videos introducing the history and products of local businesses, as well as videos introducing the product manufacturing process and text explaining the product's features. Step 4: The proposal department generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the generation department. For example, it proposes event and workshop plans using the products of local businesses, generating successful case studies that can be used as benchmarking material for other local businesses.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where local businesses provide information about their history and products. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the provided information and identifies problems. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it generates stories and content for solving the problems. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where it generates success stories based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where local businesses provide information about their history and products. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the provided information and identifies the problem. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates stories and content for solving the problem. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates success stories based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where local businesses provide information about their history and products. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the provided information and identifies problems. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it generates stories and content for solving problems. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where it generates success stories based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where local businesses provide information about their history and products. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the provided information and identifies the problem. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates stories and content for solving the problem. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates success stories based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) An interface that allows local businesses to teach information to the AI ​​themselves, and a reception area where a dedicated concierge inputs information, The analysis unit analyzes the information received by the reception unit and analyzes the specific challenges faced by local businesses, Based on the issues analyzed by the aforementioned analysis unit, a generation unit generates stories and content for solving the problems of local businesses, The system comprises a proposal unit that generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate texts and videos introducing the history and products of local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose event and workshop planning using products from local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Generate experiential tourist routes using products from local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will establish a marketplace that connects local businesses with consumers and utilize AI-generated content within it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We generate recipes that utilize products from local businesses, as well as stories about the background behind the creation of those products. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the sentiments of local businesses and adjust the information input method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the past input history of local businesses and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During the input stage, filtering is performed based on the current business status and areas of interest of local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the sentiments of local businesses and prioritizes the information to be input based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting data, the system prioritizes inputting highly relevant information, taking into account the geographical location of local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During the input phase, the social media activities of local businesses are analyzed, and relevant information is input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the sentiments of local businesses and adjust the problem analysis method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past data of local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the business category of the local business operator. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We estimate the sentiments of local businesses and adjust the display method of the analysis results based on the estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of local businesses will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature from local businesses to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the sentiments of local businesses and adjust the content generation method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating content, the generation algorithm is optimized by referencing past content from local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating content, different generation methods are applied depending on the business category of the local business operator. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is We estimate the sentiments of local businesses and determine the priority of content to generate based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating content, the geographical distribution of local businesses is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating content, we improve the accuracy of the generation by referring to relevant literature from local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate the sentiments of local businesses and adjust the proposal method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting a proposal, the proposal algorithm is optimized by referring to the past proposal history of local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When submitting proposals, different proposal methods will be applied depending on the business category of the local business operator. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, We estimate the sentiments of local businesses and determine the priority of proposals based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, take into consideration the geographical distribution of local businesses. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we will refer to relevant literature from local businesses to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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. An interface that allows local businesses to teach information to the AI ​​themselves, and a reception area where a dedicated concierge inputs information, The analysis unit analyzes the information received by the reception unit and analyzes the specific challenges faced by local businesses, Based on the issues analyzed by the aforementioned analysis unit, a generation unit generates stories and content for solving the problems of local businesses, The system comprises a proposal unit that generates successful case studies of regional development utilizing the products and services of local businesses, based on the content generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate texts and videos introducing the history and products of local businesses. The system according to feature 1.

3. The aforementioned proposal section is, We propose event and workshop planning using products from local businesses. The system according to feature 1.

4. The aforementioned proposal section is, Generate experiential tourist routes using products from local businesses. The system according to feature 1.

5. The aforementioned proposal section is, We will establish a marketplace that connects local businesses with consumers and utilize AI-generated content within it. The system according to feature 1.

6. The generating unit is We generate recipes that utilize products from local businesses, as well as stories about the background behind the creation of those products. The system according to feature 1.

7. The aforementioned reception unit is We estimate the sentiments of local businesses and adjust the information input method based on those estimated sentiments. The system according to feature 1.

8. The aforementioned reception unit is Analyze the past input history of local businesses and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is During the input stage, filtering is performed based on the current business status and areas of interest of local businesses. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the sentiments of local businesses and prioritizes the information to be input based on those estimated sentiments. The system according to feature 1.

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