System

Abstract

An object of the system according to the embodiment is to effectively transmit information on regional cultures and local companies to the world and automatically translate the information into languages of various countries.SOLUTION: A system includes an information input unit, an analysis unit, a transmission unit, a translation unit, and a recommendation unit. The information input unit inputs information on a regional culture or a local company. The analysis unit analyzes the information input by the information input unit. The transmission unit transmits the information analyzed by the analysis unit throughout the world. The translation unit automatically translates the information transmitted by the transmission unit into a language of each country. When information on a technology desired to be developed is input, the recommendation unit recommends an excellent company in the region of Japan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not yet been able to effectively disseminate information about local culture and local businesses around the world and automatically translate it into various languages, leaving room for improvement.

[0005] The system according to the embodiment aims to effectively disseminate information about local cultures and local businesses around the world and automatically translate it into various languages. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an analysis unit, a transmission unit, a translation unit, and a recommendation unit. The information input unit inputs information about local culture or local companies. The analysis unit analyzes the information input by the information input unit. The transmission unit transmits the information analyzed by the analysis unit to the world. The translation unit automatically translates the information transmitted by the transmission unit into various languages. The recommendation unit recommends excellent companies in local Japanese regions when information about technology desired for development is input. [Effects of the Invention]

[0007] The system according to the embodiment can effectively disseminate information about local culture and local businesses around the world and automatically translate it into various languages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The regional culture promotion system according to an embodiment of the present invention automatically analyzes information about regional culture and local businesses, and uses a generation AI to disseminate the information worldwide and automatically translate it into various languages. As a result, the regional culture promotion system effectively disseminates information about regional culture and local businesses worldwide, increasing inbound tourists and revitalizing regional economies through corporate recommendations for technological development.

[0029] A regional culture promotion system according to an embodiment includes an information input unit, an analysis unit, a transmission unit, a translation unit, and a recommendation unit. The information input unit inputs information about regional culture or local businesses. For example, information about regional traditional crafts and specialty products, or the technologies and products of local businesses is input. The analysis unit analyzes the information input by the information input unit. For example, the generation AI analyzes the information using text mining technology. The analysis unit can also analyze the information using data mining technology. The analysis unit can also analyze the information using statistical analysis technology. The transmission unit transmits the information analyzed by the analysis unit worldwide. For example, the generation AI transmits the information through social media or a website. The transmission unit can also transmit the information through a newsletter. The transmission unit can also transmit the information through a blog. The translation unit automatically translates the information transmitted by the transmission unit into various languages. For example, the generation AI translates the information using machine translation technology. The translation unit can also translate the information using neural network translation technology. The translation unit can also translate the information using statistical machine translation technology. When information about a technology desired for development is input, the recommendation unit recommends excellent companies in the Japanese region. For example, the generation AI analyzes patent information to recommend companies. The recommendation unit can also recommend companies by analyzing technical specifications. The recommendation unit can also recommend companies by analyzing research results. As a result, the regional culture promotion system according to the embodiment can effectively disseminate information about regional culture and local companies worldwide, increasing inbound tourists and revitalizing regional economies through company recommendations in technological development.

[0030] The analysis unit can automatically link information to related images and videos to generate visually appealing content. For example, the analysis unit allows the generation AI to automatically search for and link related images and videos based on input information about local culture and local businesses. For example, it automatically links images and videos related to information about local festivals. The analysis unit also allows the generation AI to analyze the input information and automatically generate related images and videos. For example, when information about local specialty products is input, it automatically generates a video showing the manufacturing process of those specialty products. The analysis unit also builds a system in which the generation AI automatically links related images and videos based on the input information to generate visually appealing content. For example, when information about local tourist attractions is input, it automatically links images and videos of those famous places. This generates visually appealing content, improving the appeal of the information.

[0031] When analyzing information, the analysis unit can compare it with past data to identify trends and changes. For example, when analyzing information on local culture or local businesses, the analysis unit will add a function to compare it with past data to identify trends and changes. For example, it will analyze current trends based on past numbers of tourists and event participants. The analysis unit will also identify trends and changes by having the generation AI analyze past data and compare it with current information. For example, it will analyze current trends based on past festival participant numbers and sales data. The analysis unit will also build a system that will add a function to compare past data with current information to identify trends and changes. For example, it will analyze current trends based on the past popularity and number of visitors of tourist attractions. This will make it possible to identify trends and changes by comparing with past data, enabling more effective information dissemination.

[0032] The information input unit can accommodate voice input and handwritten input, providing a variety of input methods. The information input unit builds a system that accommodates voice input and handwritten input, for example, when entering information about local culture or local businesses. For example, it uses voice recognition technology to automatically convert what the user says into text. In addition, to accommodate handwritten input, the information input unit introduces handwritten character recognition technology and automatically analyzes information entered by the user by hand. For example, it automatically converts handwritten information about local specialty products into text. In addition, the information input unit develops a system that offers a variety of input methods by supporting voice input and handwritten input. For example, it automatically analyzes information entered by the user by voice and links to related images and videos. This provides a variety of input methods, improving user convenience.

[0033] The analysis unit can compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, the analysis unit adds a function to compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, it compares the characteristics of festivals and events in each region and identifies the similarities and differences. The analysis unit also uses the generation AI to analyze information from different regions and automatically extract the similarities and differences. For example, it compares the characteristics of local specialties and traditional crafts in each region and identifies the similarities and differences. The analysis unit also builds a system to compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, it compares the tourism resources and tourist characteristics of each region and identifies the similarities and differences. This makes it possible to identify the similarities and differences by comparing the cultures and corporate information of different regions, enabling more effective information dissemination.

[0034] The translation department can customize the translated information to suit the culture and customs of each country. For example, the translation department adds a function that allows the generation AI to automatically customize the translated information to suit the culture and customs of each country. For example, the translated text may be modified to suit the culture and customs of each country. The translation department also analyzes the information translated by the generation AI and customizes it to suit the culture and customs of each country. For example, the translated text may be adjusted to suit the culture and customs of each country. The translation department also builds a system that adds a function that customizes the translated information to suit the culture and customs of each country. For example, the translated text may be modified to suit the culture and customs of each country. This enables more effective information dissemination by customizing information to suit the culture and customs of each country.

[0035] The translation unit can learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, the translation unit adds a function that allows the generation AI to learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. The translation unit also analyzes past translation data and learns to improve the accuracy of the translation content. For example, the translation algorithm is optimized based on past translation data. The translation unit also builds a system that allows the generation AI to learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. As a result, the accuracy of the translation content is improved by learning from past translation data and continuously improving.

[0036] The transmission unit can transmit translated information in audio or video format, thereby providing content that appeals to the visual and auditory senses. The transmission unit, for example, builds a system that enables translated information to be transmitted in audio or video format. For example, the translated text is converted into audio using speech synthesis technology. The transmission unit also uses a generation AI to automatically generate videos to transmit the translated information in video format. For example, a video is generated by combining related images and videos based on the translated text. The transmission unit also develops a system that provides content that appeals to the visual and auditory senses by transmitting translated information in audio or video format. For example, the translated text is converted into audio and transmitted in video format. This makes it possible to provide content that appeals to the visual and auditory senses by transmitting information in audio or video format.

[0037] The information dissemination department can promote the spread of information by adding a function to automatically post to social media and media platforms in each country. For example, the information dissemination department builds a system that adds a function to automatically post to social media and media platforms in each country. For example, translated information is automatically posted to Twitter and Facebook. The information dissemination department also uses the generative AI to automatically post translated information to social media and media platforms in each country. For example, translated information is automatically posted to Instagram and WeChat. The information dissemination department also develops a system that adds a function to automatically post to social media and media platforms in each country. For example, translated information is automatically posted to YouTube and TikTok. This allows information to be automatically posted to social media and media platforms in each country, promoting the spread of information.

[0038] The recommendation unit can analyze the travel history and interests of overseas customers and propose individually customized travel plans. For example, the generation AI in the recommendation unit analyzes the past travel history and interests of overseas customers and proposes individually customized travel plans. For example, it creates a travel plan based on tourist spots visited in the past and events in which the customer is interested. The recommendation unit also analyzes the travel history and interests of overseas customers and builds a system that proposes individually customized travel plans. For example, it proposes the most suitable tourist spots and events based on the customer's interests. The recommendation unit also analyzes the past travel history and interests of overseas customers and proposes individually customized travel plans. For example, it proposes the most suitable tourist spots and events based on the customer's interests. In this way, customer satisfaction is improved by analyzing the past travel history and interests of overseas customers and proposing individually customized travel plans.

[0039] The recommendation unit can automatically convert information about culture, festivals, and tourist resources into videos and interactive content to enhance visual appeal. For example, the recommendation unit uses a generation AI to automatically convert information about local culture, festivals, and tourist resources into videos and interactive content. For example, a video showing a local festival can be created to enhance visual appeal. The recommendation unit also uses a generation AI to analyze information about local culture and tourist resources and automatically convert it into videos and interactive content. For example, a video introducing tourist attractions can be generated. The recommendation unit also builds a system in which the generation AI automatically converts information about local culture, festivals, and tourist resources into videos and interactive content to enhance visual appeal. For example, a video can be created to introduce the manufacturing process of a local specialty product. This provides visually appealing content, thereby improving the appeal of the information.

[0040] The recommendation unit can provide local weather and event information and suggest the best time to travel when overseas customers plan a trip. For example, when overseas customers plan a trip, the recommendation unit's generation AI automatically provides local weather and event information and suggests the best time to travel. For example, it suggests the best time to travel based on weather forecasts and event schedules. The recommendation unit also builds a system in which the generation AI analyzes local weather and event information and suggests the best time to travel. For example, it suggests the best time to travel based on weather forecasts and event schedules. The recommendation unit also builds a system in which the generation AI automatically provides local weather and event information and suggests the best time to travel when overseas customers plan a trip. For example, it suggests the best time to travel based on weather forecasts and event schedules. The recommendation unit's generation AI automatically provides local weather and event information and suggests the best time to travel. For example, it suggests the best time to travel based on weather forecasts and event schedules. This makes it possible to suggest the best time to travel by providing local weather and event information.

[0041] The recommendation unit can analyze the reviews and ratings of other travelers and provide highly reliable information. The recommendation unit adds a function, for example, in which the generation AI analyzes the reviews and ratings of other travelers and provides highly reliable information. For example, it provides ratings of tourist destinations and accommodations based on traveler reviews. The recommendation unit also builds a system that analyzes the reviews and ratings of other travelers and provides highly reliable information. For example, it provides ratings of tourist destinations and accommodations based on traveler reviews. The recommendation unit also builds a function in which the generation AI analyzes the reviews and ratings of other travelers and provides highly reliable information. For example, it provides ratings of tourist destinations and accommodations based on traveler reviews. This makes it possible to provide highly reliable information by analyzing the reviews and ratings of other travelers.

[0042] The recommendation unit can have the generation AI automatically generate a detailed report on the technologies and products of the recommended companies and provide it to the user. For example, the recommendation unit can have the generation AI automatically generate a detailed report on the technologies and products of the recommended companies and provide it to the user. For example, the recommendation unit can generate a report that details the technical capabilities of the companies and the features of the products. The recommendation unit also builds a system in which the generation AI automatically generates a detailed report on the technologies and products of the recommended companies and provides it to the user. For example, the recommendation unit can generate a report that details the technical capabilities of the companies and the features of the products. The recommendation unit can have the generation AI automatically generate a detailed report on the technologies and products of the recommended companies and provide it to the user. For example, the recommendation unit can generate a report that details the technical capabilities of the companies and the features of the products. In this way, by automatically generating a detailed report, more specific information can be provided to the user.

[0043] The recommendation unit can add a function that analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, the recommendation unit adds a function where the generation AI analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, it recommends companies based on the latest technology trends. The recommendation unit also builds a system that analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, it recommends companies based on the latest technology trends. The recommendation unit also adds a function where the generation AI analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, it recommends companies based on the latest technology trends. This improves the efficiency of technology development by recommending companies that will meet future technology needs.

[0044] The recommendation unit can add a function to analyze companies in different industries across the board and propose technical cooperation between the two industries. For example, the recommendation unit adds a function whereby the generation AI analyzes companies in different industries across the board and proposes technical cooperation between the two industries. For example, it proposes new products and services that combine technologies from different industries. The recommendation unit also builds a system whereby it analyzes companies in different industries and proposes technical cooperation between the two industries. For example, it proposes new products and services that combine technologies from different industries. The recommendation unit also builds a function whereby the generation AI analyzes companies in different industries across the board and proposes technical cooperation between the two industries. For example, it proposes new products and services that combine technologies from different industries. This promotes the development of new products and services by proposing technical cooperation between the two industries.

[0045] The recommendation unit can add a function whereby the generation AI automatically generates contact information and proposals to promote collaboration with recommended companies. The recommendation unit, for example, adds a function whereby the generation AI automatically generates contact information and proposals to promote collaboration with recommended companies. For example, it automatically generates company contact information and proposal templates. The recommendation unit also builds a system whereby the generation AI automatically collects contact information for recommended companies and generates proposals. For example, it automatically generates company contact information and proposal templates. The recommendation unit also adds a function whereby the generation AI automatically generates contact information and proposals to promote collaboration with recommended companies. For example, it automatically generates company contact information and proposal templates. This automatically generates contact information and proposals, making collaboration between companies smoother.

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

[0047] When analyzing information about local culture and local businesses, the analysis unit uses natural language processing technology to understand the context of the information and perform more accurate analysis. For example, when analyzing information about local traditional crafts, the analysis unit understands their historical background and cultural significance. The analysis unit also uses natural language processing technology to understand the context of the information and automatically link related information. For example, when analyzing information about local business technology, the analysis unit understands how that technology is applied to other industries. The analysis unit also uses natural language processing technology to understand the context of the information and evaluate its reliability. For example, when analyzing information about local specialty products, the analysis unit evaluates whether the information is reliable. This allows the reliability of the information to be improved by understanding the context of the information and performing more accurate analysis.

[0048] The analysis unit can automatically link information to related images and videos to generate visually appealing content. For example, based on input information about local culture and local businesses, the generation AI automatically searches for and links related images and videos. For example, it automatically links related images and videos to information about local festivals. The analysis unit also allows the generation AI to analyze the input information and automatically generate related images and videos. For example, when information about local specialty products is input, it automatically generates a video showing the manufacturing process of those specialty products. The analysis unit also builds a system in which the generation AI automatically links related images and videos based on the input information to generate visually appealing content. For example, when information about local tourist attractions is input, it automatically links images and videos of those famous places. This generates visually appealing content, improving the appeal of the information.

[0049] When analyzing information, the analysis unit can compare it with past data to identify trends and changes. For example, when analyzing information on local culture or local businesses, a function can be added to compare it with past data to identify trends and changes. For example, current trends can be analyzed based on past numbers of tourists and event participants. The analysis unit also identifies trends and changes by having the generation AI analyze past data and compare it with current information. For example, current trends can be analyzed based on past festival participant numbers and sales data. The analysis unit also builds a system that adds a function to compare past data with current information to identify trends and changes. For example, current trends can be analyzed based on the past popularity and number of visitors of tourist attractions. This makes it possible to identify trends and changes by comparing with past data, enabling more effective information dissemination.

[0050] The information input unit can also accommodate voice input and handwriting input, providing a variety of input methods. For example, a system can be built that accommodates voice input and handwriting input when entering information about regional culture or local businesses. For example, speech recognition technology can be used to automatically convert what the user says into text. In addition, to accommodate handwriting input, the information input unit can introduce handwriting recognition technology and automatically analyze information entered by the user through handwriting. For example, information about regional specialty products entered by hand can be automatically converted into text. In addition, by supporting voice input and handwriting input, a system can be developed that offers a variety of input methods. For example, information entered by the user through voice can be automatically analyzed and related images and videos can be linked. This provides a variety of input methods, improving user convenience.

[0051] The analysis unit can compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, a function can be added to compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, the characteristics of each region's festivals and events can be compared and the similarities and differences identified. The analysis unit also uses the generation AI to analyze information from different regions and automatically extract the similarities and differences. For example, the analysis unit compares the characteristics of each region's specialty products and traditional crafts and identifies the similarities and differences. The analysis unit also builds a system that compares the cultures and corporate information of different regions and automatically analyzes the similarities and differences. For example, the analysis unit compares the tourism resources and tourist characteristics of each region and identifies the similarities and differences. This makes it possible to identify the similarities and differences by comparing the cultures and corporate information of different regions, enabling more effective information dissemination.

[0052] The translation department can customize the translated information to suit the culture and customs of each country. For example, a function can be added that allows the generation AI to automatically customize the translated information to suit the culture and customs of each country. For example, the translated text can be modified to suit the culture and customs of each country. The translation department can also analyze the information translated by the generation AI and customize it to suit the culture and customs of each country. For example, the translated text can be adjusted to suit the culture and customs of each country. The translation department can also build a system that adds a function to customize the translated information to suit the culture and customs of each country. For example, the translated text can be modified to suit the culture and customs of each country. This makes it possible to disseminate information more effectively by customizing it to suit the culture and customs of each country.

[0053] The translation unit can learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, a function is added in which the generation AI learns from past translation data and continuously improves in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. The translation unit also analyzes past translation data and learns to improve the accuracy of the translation content. For example, the translation algorithm is optimized based on past translation data. The translation unit also builds a system in which the generation AI learns from past translation data and continuously improves in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. In this way, the accuracy of the translation content is improved by learning from past translation data and continuously improving.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information input unit inputs information about local culture or local businesses, such as traditional crafts and specialties of the region, or the technologies and products of local businesses. Step 2: The analysis unit analyzes the information input by the information input unit. For example, the generation AI analyzes the information using text mining technology, data mining technology, or statistical analysis technology. Step 3: The dissemination unit disseminates the information analyzed by the analysis unit to the world. For example, the generation AI disseminates the information through social media, websites, newsletters, and blogs. Step 4: The translation unit automatically translates the information sent by the sending unit into each country's language. For example, the generation AI translates the information using machine translation technology, neural network translation technology, or statistical machine translation technology. Step 5: The recommendation department will recommend excellent companies in the region of Japan based on input of information about the technology desired for development. For example, the generative AI will analyze patent information, technical specifications, or research results to recommend companies.

[0056] (Example 2) The regional culture promotion system according to an embodiment of the present invention automatically analyzes information about regional culture and local businesses, and uses a generation AI to disseminate the information worldwide and automatically translate it into various languages. As a result, the regional culture promotion system effectively disseminates information about regional culture and local businesses worldwide, increasing inbound tourists and revitalizing regional economies through corporate recommendations for technological development.

[0057] A regional culture promotion system according to an embodiment includes an information input unit, an analysis unit, a transmission unit, a translation unit, and a recommendation unit. The information input unit inputs information about regional culture or local businesses. For example, information about regional traditional crafts and specialty products, or the technologies and products of local businesses is input. The analysis unit analyzes the information input by the information input unit. For example, the generation AI analyzes the information using text mining technology. The analysis unit can also analyze the information using data mining technology. The analysis unit can also analyze the information using statistical analysis technology. The transmission unit transmits the information analyzed by the analysis unit worldwide. For example, the generation AI transmits the information through social media or a website. The transmission unit can also transmit the information through a newsletter. The transmission unit can also transmit the information through a blog. The translation unit automatically translates the information transmitted by the transmission unit into various languages. For example, the generation AI translates the information using machine translation technology. The translation unit can also translate the information using neural network translation technology. The translation unit can also translate the information using statistical machine translation technology. When information about a technology desired for development is input, the recommendation unit recommends excellent companies in the Japanese region. For example, the generation AI analyzes patent information to recommend companies. The recommendation unit can also recommend companies by analyzing technical specifications. The recommendation unit can also recommend companies by analyzing research results. As a result, the regional culture promotion system according to the embodiment can effectively disseminate information about regional culture and local companies worldwide, increasing inbound tourists and revitalizing regional economies through company recommendations in technological development.

[0058] The information input unit can analyze the user's emotions in real time and prioritize analysis of information that elicits positive emotions. For example, when inputting information about local culture or local businesses, the information input unit analyzes the user's emotions in real time and prioritizes analysis of information that elicits positive emotions. For example, it prioritizes processing of information that makes the user feel excited or happy. The information input unit also uses an emotion estimation function to calculate an emotion score for the information input by the user and prioritizes analysis of information with a high positive emotion. For example, if information about local festivals or events has a high emotion score, it prioritizes analysis of that information. The information input unit also builds a system that analyzes the user's emotions and prioritizes analysis of information that elicits positive emotions. For example, it prioritizes analysis of information that elicits positive emotions based on the emotion score of the information input by the user. This enables more effective information dissemination by prioritizing analysis of information that elicits positive emotions from the user.

[0059] The analysis unit can automatically link information to related images and videos to generate visually appealing content. For example, the analysis unit allows the generation AI to automatically search for and link related images and videos based on input information about local culture and local businesses. For example, it automatically links images and videos related to information about local festivals. The analysis unit also allows the generation AI to analyze the input information and automatically generate related images and videos. For example, when information about local specialty products is input, it automatically generates a video showing the manufacturing process of those specialty products. The analysis unit also builds a system in which the generation AI automatically links related images and videos based on the input information to generate visually appealing content. For example, when information about local tourist attractions is input, it automatically links images and videos of those famous places. This generates visually appealing content, improving the appeal of the information.

[0060] When analyzing information, the analysis unit can compare it with past data to identify trends and changes. For example, when analyzing information on local culture or local businesses, the analysis unit will add a function to compare it with past data to identify trends and changes. For example, it will analyze current trends based on past numbers of tourists and event participants. The analysis unit will also identify trends and changes by having the generation AI analyze past data and compare it with current information. For example, it will analyze current trends based on past festival participant numbers and sales data. The analysis unit will also build a system that will add a function to compare past data with current information to identify trends and changes. For example, it will analyze current trends based on the past popularity and number of visitors of tourist attractions. This will make it possible to identify trends and changes by comparing with past data, enabling more effective information dissemination.

[0061] The information input unit can accommodate voice input and handwritten input, providing a variety of input methods. The information input unit builds a system that accommodates voice input and handwritten input, for example, when entering information about local culture or local businesses. For example, it uses voice recognition technology to automatically convert what the user says into text. In addition, to accommodate handwritten input, the information input unit introduces handwritten character recognition technology and automatically analyzes information entered by the user by hand. For example, it automatically converts handwritten information about local specialty products into text. In addition, the information input unit develops a system that offers a variety of input methods by supporting voice input and handwritten input. For example, it automatically analyzes information entered by the user by voice and links to related images and videos. This provides a variety of input methods, improving user convenience.

[0062] The analysis unit can compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, the analysis unit adds a function to compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, it compares the characteristics of festivals and events in each region and identifies the similarities and differences. The analysis unit also uses the generation AI to analyze information from different regions and automatically extract the similarities and differences. For example, it compares the characteristics of local specialties and traditional crafts in each region and identifies the similarities and differences. The analysis unit also builds a system to compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, it compares the tourism resources and tourist characteristics of each region and identifies the similarities and differences. This makes it possible to identify the similarities and differences by comparing the cultures and corporate information of different regions, enabling more effective information dissemination.

[0063] The information input unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and make suggestions to optimize the input content. The information input unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering input in real time and make suggestions to optimize the input content. For example, a suggestion is made to elicit positive emotions from the information entered by the user. The information input unit also analyzes the user's emotion in real time and provides feedback to optimize the input content. For example, an optimal suggestion is made based on an emotion score for the information entered by the user. The information input unit also uses the emotion estimation function to analyze the emotion of the user when entering input and provides an interface for optimizing the input content. For example, a suggestion is made to elicit positive emotions from the information entered by the user. In this way, by analyzing the user's emotion in real time and optimizing the input content, more effective information transmission is possible.

[0064] The translation unit can use the emotion estimation function to adjust the translated content so that it evokes positive emotions when automatically translating into various languages. For example, the translation unit uses the emotion estimation function to adjust the translated content so that it evokes positive emotions when automatically translating into various languages. For example, the translation unit modifies the translated content to be more positive based on the emotion score of the translated text. The translation unit also analyzes the translated content with the emotion estimation function and makes adjustments to evoke positive emotions. For example, it replaces negative expressions with positive expressions. The translation unit also uses the emotion estimation function to build a system that adjusts the translated content so that it evokes positive emotions. For example, the translation unit modifies the translated content to evoke positive emotions based on the emotion score of the translated text. In this way, by adjusting the translated content to evoke positive emotions, it is possible to leave a better impression on the recipient of the information.

[0065] The translation department can customize the translated information to suit the culture and customs of each country. For example, the translation department adds a function that allows the generation AI to automatically customize the translated information to suit the culture and customs of each country. For example, the translated text may be modified to suit the culture and customs of each country. The translation department also analyzes the information translated by the generation AI and customizes it to suit the culture and customs of each country. For example, the translated text may be adjusted to suit the culture and customs of each country. The translation department also builds a system that adds a function that customizes the translated information to suit the culture and customs of each country. For example, the translated text may be modified to suit the culture and customs of each country. This enables more effective information dissemination by customizing information to suit the culture and customs of each country.

[0066] The translation unit can learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, the translation unit adds a function that allows the generation AI to learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. The translation unit also analyzes past translation data and learns to improve the accuracy of the translation content. For example, the translation algorithm is optimized based on past translation data. The translation unit also builds a system that allows the generation AI to learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. As a result, the accuracy of the translation content is improved by learning from past translation data and continuously improving.

[0067] The transmission unit can transmit translated information in audio or video format, thereby providing content that appeals to the visual and auditory senses. The transmission unit, for example, builds a system that enables translated information to be transmitted in audio or video format. For example, the translated text is converted into audio using speech synthesis technology. The transmission unit also uses a generation AI to automatically generate videos to transmit the translated information in video format. For example, a video is generated by combining related images and videos based on the translated text. The transmission unit also develops a system that provides content that appeals to the visual and auditory senses by transmitting translated information in audio or video format. For example, the translated text is converted into audio and transmitted in video format. This makes it possible to provide content that appeals to the visual and auditory senses by transmitting information in audio or video format.

[0068] The information dissemination department can promote the spread of information by adding a function to automatically post to social media and media platforms in each country. For example, the information dissemination department builds a system that adds a function to automatically post to social media and media platforms in each country. For example, translated information is automatically posted to Twitter and Facebook. The information dissemination department also uses the generative AI to automatically post translated information to social media and media platforms in each country. For example, translated information is automatically posted to Instagram and WeChat. The information dissemination department also develops a system that adds a function to automatically post to social media and media platforms in each country. For example, translated information is automatically posted to YouTube and TikTok. This allows information to be automatically posted to social media and media platforms in each country, promoting the spread of information.

[0069] The transmission unit uses the emotion estimation function to collect users' emotional reactions to translated information and can use the collected data to improve the translation. The transmission unit, for example, uses the emotion estimation function to collect users' emotional reactions to translated information and improves the translation based on the data. For example, the translation is corrected based on the user's emotional score. The transmission unit also builds a system that collects users' emotional reactions to translated information in real time and uses the data to improve the translation. For example, the translation algorithm is optimized based on the user's emotional reaction data. The transmission unit also uses the emotion estimation function to collect users' emotional reactions to translated information and develops a system that improves the translation based on the data. For example, the translation is corrected based on the user's emotional score. In this way, collecting users' emotional reactions and using the data to improve the translation enables more effective information transmission.

[0070] The recommendation unit can analyze the travel history and interests of overseas customers and propose individually customized travel plans. For example, the generation AI in the recommendation unit analyzes the past travel history and interests of overseas customers and proposes individually customized travel plans. For example, it creates a travel plan based on tourist spots visited in the past and events in which the customer is interested. The recommendation unit also analyzes the travel history and interests of overseas customers and builds a system that proposes individually customized travel plans. For example, it proposes the most suitable tourist spots and events based on the customer's interests. The recommendation unit also analyzes the past travel history and interests of overseas customers and proposes individually customized travel plans. For example, it proposes the most suitable tourist spots and events based on the customer's interests. In this way, customer satisfaction is improved by analyzing the past travel history and interests of overseas customers and proposing individually customized travel plans.

[0071] The recommendation unit can automatically convert information about culture, festivals, and tourist resources into videos and interactive content to enhance visual appeal. For example, the recommendation unit uses a generation AI to automatically convert information about local culture, festivals, and tourist resources into videos and interactive content. For example, a video showing a local festival can be created to enhance visual appeal. The recommendation unit also uses a generation AI to analyze information about local culture and tourist resources and automatically convert it into videos and interactive content. For example, a video introducing tourist attractions can be generated. The recommendation unit also builds a system in which the generation AI automatically converts information about local culture, festivals, and tourist resources into videos and interactive content to enhance visual appeal. For example, a video can be created to introduce the manufacturing process of a local specialty product. This provides visually appealing content, thereby improving the appeal of the information.

[0072] The recommendation unit can use the emotion estimation function to analyze the emotions of overseas customers when considering travel plans and make suggestions that elicit positive emotions. The recommendation unit, for example, uses the emotion estimation function to analyze the emotions of overseas customers when considering travel plans and make suggestions that elicit positive emotions. For example, it suggests tourist spots and events that make customers feel excited or happy. The recommendation unit also builds a system that analyzes the emotions of overseas customers in real time and proposes travel plans that elicit positive emotions. For example, it suggests optimal tourist spots and events based on the customer's emotion score. The recommendation unit also uses the emotion estimation function to analyze the emotions of overseas customers when considering travel plans and make suggestions that elicit positive emotions. For example, it suggests tourist spots and events that make customers feel excited or happy. In this way, by making suggestions that elicit positive emotions, it is possible to increase the customer's desire to travel.

[0073] The recommendation unit can provide local weather and event information and suggest the best time to travel when overseas customers plan a trip. For example, when overseas customers plan a trip, the recommendation unit's generation AI automatically provides local weather and event information and suggests the best time to travel. For example, it suggests the best time to travel based on weather forecasts and event schedules. The recommendation unit also builds a system in which the generation AI analyzes local weather and event information and suggests the best time to travel. For example, it suggests the best time to travel based on weather forecasts and event schedules. The recommendation unit also builds a system in which the generation AI automatically provides local weather and event information and suggests the best time to travel when overseas customers plan a trip. For example, it suggests the best time to travel based on weather forecasts and event schedules. The recommendation unit's generation AI automatically provides local weather and event information and suggests the best time to travel. For example, it suggests the best time to travel based on weather forecasts and event schedules. This makes it possible to suggest the best time to travel by providing local weather and event information.

[0074] The recommendation unit can analyze the reviews and ratings of other travelers and provide highly reliable information. The recommendation unit adds a function, for example, in which the generation AI analyzes the reviews and ratings of other travelers and provides highly reliable information. For example, it provides ratings of tourist destinations and accommodations based on traveler reviews. The recommendation unit also builds a system that analyzes the reviews and ratings of other travelers and provides highly reliable information. For example, it provides ratings of tourist destinations and accommodations based on traveler reviews. The recommendation unit also builds a function in which the generation AI analyzes the reviews and ratings of other travelers and provides highly reliable information. For example, it provides ratings of tourist destinations and accommodations based on traveler reviews. This makes it possible to provide highly reliable information by analyzing the reviews and ratings of other travelers.

[0075] The recommendation unit can use the emotion estimation function to monitor the user's emotional response to the travel plan in real time and continuously suggest optimal plans. The recommendation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the travel plan in real time and continuously suggest optimal plans. For example, the travel plan is adjusted based on the user's emotion score. The recommendation unit also builds a system that analyzes the user's emotional response in real time and continuously suggests optimal travel plans. For example, the travel plan is adjusted based on the user's emotion score. The recommendation unit also uses the emotion estimation function to monitor the user's emotional response to the travel plan in real time and continuously suggest optimal plans. For example, the travel plan is adjusted based on the user's emotion score. In this way, by monitoring the user's emotional response in real time and continuously suggesting optimal plans, customer satisfaction is improved.

[0076] The recommendation unit can have the generation AI automatically generate a detailed report on the technologies and products of the recommended companies and provide it to the user. For example, the recommendation unit can have the generation AI automatically generate a detailed report on the technologies and products of the recommended companies and provide it to the user. For example, the recommendation unit can generate a report that details the technical capabilities of the companies and the features of the products. The recommendation unit also builds a system in which the generation AI automatically generates a detailed report on the technologies and products of the recommended companies and provides it to the user. For example, the recommendation unit can generate a report that details the technical capabilities of the companies and the features of the products. The recommendation unit can have the generation AI automatically generate a detailed report on the technologies and products of the recommended companies and provide it to the user. For example, the recommendation unit can generate a report that details the technical capabilities of the companies and the features of the products. In this way, by automatically generating a detailed report, more specific information can be provided to the user.

[0077] The recommendation unit can add a function that analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, the recommendation unit adds a function where the generation AI analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, it recommends companies based on the latest technology trends. The recommendation unit also builds a system that analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, it recommends companies based on the latest technology trends. The recommendation unit also adds a function where the generation AI analyzes a company's technology development trends and recommends companies that will meet future technology needs. For example, it recommends companies based on the latest technology trends. This improves the efficiency of technology development by recommending companies that will meet future technology needs.

[0078] The recommendation unit can add a function to analyze companies in different industries across the board and propose technical cooperation between the two industries. For example, the recommendation unit adds a function whereby the generation AI analyzes companies in different industries across the board and proposes technical cooperation between the two industries. For example, it proposes new products and services that combine technologies from different industries. The recommendation unit also builds a system whereby it analyzes companies in different industries and proposes technical cooperation between the two industries. For example, it proposes new products and services that combine technologies from different industries. The recommendation unit also builds a function whereby the generation AI analyzes companies in different industries across the board and proposes technical cooperation between the two industries. For example, it proposes new products and services that combine technologies from different industries. This promotes the development of new products and services by proposing technical cooperation between the two industries.

[0079] The recommendation unit can add a function whereby the generation AI automatically generates contact information and proposals to promote collaboration with recommended companies. The recommendation unit, for example, adds a function whereby the generation AI automatically generates contact information and proposals to promote collaboration with recommended companies. For example, it automatically generates company contact information and proposal templates. The recommendation unit also builds a system whereby the generation AI automatically collects contact information for recommended companies and generates proposals. For example, it automatically generates company contact information and proposal templates. The recommendation unit also adds a function whereby the generation AI automatically generates contact information and proposals to promote collaboration with recommended companies. For example, it automatically generates company contact information and proposal templates. This automatically generates contact information and proposals, making collaboration between companies smoother.

[0080] The recommendation unit can use the emotion estimation function to collect users' emotional reactions to recommended companies and improve the accuracy of recommendations. The recommendation unit, for example, uses the emotion estimation function to collect users' emotional reactions to recommended companies and improve the accuracy of recommendations based on that data. For example, the recommendation unit adjusts company recommendations based on the user's emotional score. The recommendation unit also builds a system that collects users' emotional reactions to recommended companies in real time and improves the accuracy of recommendations. For example, the recommendation unit optimizes company recommendations based on user emotional reaction data. The recommendation unit also uses the emotion estimation function to collect users' emotional reactions to recommended companies and develops a system that improves the accuracy of recommendations based on that data. For example, the recommendation unit adjusts company recommendations based on the user's emotional score. In this way, by collecting users' emotional reactions, the accuracy of recommendations is improved.

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

[0082] When analyzing information about local culture and local businesses, the analysis unit uses natural language processing technology to understand the context of the information and perform more accurate analysis. For example, when analyzing information about local traditional crafts, the analysis unit understands their historical background and cultural significance. The analysis unit also uses natural language processing technology to understand the context of the information and automatically link related information. For example, when analyzing information about local business technology, the analysis unit understands how that technology is applied to other industries. The analysis unit also uses natural language processing technology to understand the context of the information and evaluate its reliability. For example, when analyzing information about local specialty products, the analysis unit evaluates whether the information is reliable. This allows the reliability of the information to be improved by understanding the context of the information and performing more accurate analysis.

[0083] The information input unit can analyze the user's emotions in real time and prioritize analysis of information that elicits positive emotions. For example, it prioritizes processing of information that elicits excitement or joy from the user. The information input unit also uses an emotion estimation function to calculate an emotion score for information input by the user and prioritize analysis of information with a high positive emotion. For example, if information about local festivals or events has a high emotion score, it prioritizes analysis of that information. The information input unit also builds a system that analyzes the user's emotions and prioritizes analysis of information that elicits positive emotions. For example, it prioritizes analysis of information that elicits positive emotions based on the emotion score of the information input by the user. This enables more effective information dissemination by prioritizing analysis of information that elicits positive emotions from the user.

[0084] The analysis unit can automatically link information to related images and videos to generate visually appealing content. For example, based on input information about local culture and local businesses, the generation AI automatically searches for and links related images and videos. For example, it automatically links related images and videos to information about local festivals. The analysis unit also allows the generation AI to analyze the input information and automatically generate related images and videos. For example, when information about local specialty products is input, it automatically generates a video showing the manufacturing process of those specialty products. The analysis unit also builds a system in which the generation AI automatically links related images and videos based on the input information to generate visually appealing content. For example, when information about local tourist attractions is input, it automatically links images and videos of those famous places. This generates visually appealing content, improving the appeal of the information.

[0085] When analyzing information, the analysis unit can compare it with past data to identify trends and changes. For example, when analyzing information on local culture or local businesses, a function can be added to compare it with past data to identify trends and changes. For example, current trends can be analyzed based on past numbers of tourists and event participants. The analysis unit also identifies trends and changes by having the generation AI analyze past data and compare it with current information. For example, current trends can be analyzed based on past festival participant numbers and sales data. The analysis unit also builds a system that adds a function to compare past data with current information to identify trends and changes. For example, current trends can be analyzed based on the past popularity and number of visitors of tourist attractions. This makes it possible to identify trends and changes by comparing with past data, enabling more effective information dissemination.

[0086] The information input unit can also accommodate voice input and handwriting input, providing a variety of input methods. For example, a system can be built that accommodates voice input and handwriting input when entering information about regional culture or local businesses. For example, speech recognition technology can be used to automatically convert what the user says into text. In addition, to accommodate handwriting input, the information input unit can introduce handwriting recognition technology and automatically analyze information entered by the user through handwriting. For example, information about regional specialty products entered by hand can be automatically converted into text. In addition, by supporting voice input and handwriting input, a system can be developed that offers a variety of input methods. For example, information entered by the user through voice can be automatically analyzed and related images and videos can be linked. This provides a variety of input methods, improving user convenience.

[0087] The analysis unit can compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, a function can be added to compare the cultures and corporate information of different regions and automatically analyze the similarities and differences. For example, the characteristics of each region's festivals and events can be compared and the similarities and differences identified. The analysis unit also uses the generation AI to analyze information from different regions and automatically extract the similarities and differences. For example, the analysis unit compares the characteristics of each region's specialty products and traditional crafts and identifies the similarities and differences. The analysis unit also builds a system that compares the cultures and corporate information of different regions and automatically analyzes the similarities and differences. For example, the analysis unit compares the tourism resources and tourist characteristics of each region and identifies the similarities and differences. This makes it possible to identify the similarities and differences by comparing the cultures and corporate information of different regions, enabling more effective information dissemination.

[0088] The information input unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and make suggestions to optimize the input content. For example, the emotion estimation function can be used to analyze the emotion of the user when entering input in real time and make suggestions to optimize the input content. For example, suggestions can be made to elicit positive emotions from the information entered by the user. The information input unit can also analyze the user's emotion in real time and provide feedback to optimize the input content. For example, optimal suggestions can be made based on an emotion score for the information entered by the user. The information input unit can also use the emotion estimation function to analyze the emotion of the user when entering input and provide an interface for optimizing the input content. For example, suggestions can be made to elicit positive emotions from the information entered by the user. In this way, by analyzing the user's emotion in real time and optimizing the input content, more effective information transmission is possible.

[0089] The translation unit can use the emotion estimation function to adjust the translated content so that it evokes positive emotions when automatically translating into various languages. For example, when automatically translating into various languages, the emotion estimation function is used to adjust the translated content so that it evokes positive emotions. For example, the translated content is corrected to a more positive expression based on the emotion score of the translated sentence. The translation unit also analyzes the translated content with the emotion estimation function and makes adjustments to evoke positive emotions. For example, it replaces negative expressions with positive expressions. The translation unit also uses the emotion estimation function to build a system that adjusts the translated content so that it evokes positive emotions. For example, the translated content is corrected to evoke positive emotions based on the emotion score of the translated sentence. In this way, the translation content can be adjusted to evoke positive emotions, making a better impression on the recipient of the information.

[0090] The translation department can customize the translated information to suit the culture and customs of each country. For example, a function can be added that allows the generation AI to automatically customize the translated information to suit the culture and customs of each country. For example, the translated text can be modified to suit the culture and customs of each country. The translation department can also analyze the information translated by the generation AI and customize it to suit the culture and customs of each country. For example, the translated text can be adjusted to suit the culture and customs of each country. The translation department can also build a system that adds a function to customize the translated information to suit the culture and customs of each country. For example, the translated text can be modified to suit the culture and customs of each country. This makes it possible to disseminate information more effectively by customizing it to suit the culture and customs of each country.

[0091] The translation unit can learn from past translation data and continuously improve in order to improve the accuracy of the translation content. For example, a function is added in which the generation AI learns from past translation data and continuously improves in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. The translation unit also analyzes past translation data and learns to improve the accuracy of the translation content. For example, the translation algorithm is optimized based on past translation data. The translation unit also builds a system in which the generation AI learns from past translation data and continuously improves in order to improve the accuracy of the translation content. For example, the translation algorithm is improved based on past translation data. In this way, the accuracy of the translation content is improved by learning from past translation data and continuously improving.

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

[0093] Step 1: The information input unit inputs information about local culture or local businesses, such as traditional crafts and specialties of the region, or the technologies and products of local businesses. Step 2: The analysis unit analyzes the information input by the information input unit. For example, the generation AI analyzes the information using text mining technology, data mining technology, or statistical analysis technology. Step 3: The dissemination unit disseminates the information analyzed by the analysis unit to the world. For example, the generation AI disseminates the information through social media, websites, newsletters, and blogs. Step 4: The translation unit automatically translates the information sent by the sending unit into each country's language. For example, the generation AI translates the information using machine translation technology, neural network translation technology, or statistical machine translation technology. Step 5: The recommendation department will recommend excellent companies in the region of Japan based on input of information about the technology desired for development. For example, the generative AI will analyze patent information, technical specifications, or research results to recommend companies.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. an information input section for inputting information about local culture or local businesses; an analysis unit that analyzes the information input by the information input unit; a transmitting unit that transmits the information analyzed by the analyzing unit to the world; a translation unit that automatically translates the information transmitted by the transmission unit into various languages; The system is characterized by having a recommendation section that recommends excellent companies in local areas of Japan when information about the technology desired for development is entered.

2. The information input unit Analyzing user emotions in real time and prioritizing information that elicits positive emotions The system of claim 1 .

3. The analysis unit Automatically linking this information with relevant images and videos to create visually appealing content The system of claim 1 .

4. The analysis unit When analyzing this information, compare it with past data to identify trends and changes. The system of claim 1 .

5. The information input unit Supports voice input and handwriting input, and provides a variety of input methods The system of claim 1 .

6. The analysis unit Comparing cultures and corporate information from different regions and automatically analyzing similarities and differences The system of claim 1 .

7. The information input unit Analyzing users' emotions in real time as they type and making suggestions to optimize their input The system of claim 1 .

8. The translation unit When translating into various languages, adjust the translation content to evoke positive emotions. The system of claim 1 .

Images