system

Abstract

An object of a system according to an embodiment is to efficiently generate content that matches the needs of a user and optimize the content for an algorithm of a search engine.SOLUTION: A system includes a user need analysis part, a content generation part, and an SEO optimization part. The user need analysis unit analyzes a search history and action data of the user. The content generation unit generates content based on a result of the analysis by the user need analysis unit. The SEO optimization unit optimizes the content generated by the content generation unit for an algorithm of a search engine.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 technologies have had the problem of not being able to efficiently generate content that meets user needs and to be sufficiently optimized for search engine algorithms.

[0005] The system according to the embodiment aims to efficiently generate content that meets user needs and optimize it for search engine algorithms. [Means for solving the problem]

[0006] The system according to the embodiment includes a user needs analysis unit, a content generation unit, and an SEO optimization unit. The user needs analysis unit analyzes user search history and behavioral data. The content generation unit generates content based on the results of the analysis by the user needs analysis unit. The SEO optimization unit optimizes the content generated by the content generation unit for search engine algorithms. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate content that meets the needs of users and optimize it for search engine algorithms. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 SEO countermeasure system according to the embodiment of the present invention is a system that utilizes generation AI to efficiently create content that meets user needs and achieves high rankings in search results. As a result, the SEO countermeasure system can efficiently generate high-quality content that meets user needs and achieve high rankings in search results.

[0029] The SEO system according to the embodiment includes a user needs analysis unit, a content generation unit, and an SEO optimization unit. The user needs analysis unit analyzes a user's search history and behavioral data. For example, if a user searches for "healthy eating," the user needs analysis unit analyzes the user's specific search needs based on the user's search history. The user needs analysis unit can also use data mining technology to analyze the user's behavioral data and identify the user's interests. The content generation unit generates content based on the results of the analysis by the user needs analysis unit. For example, the content generation unit uses a generation AI to generate detailed articles and recipes related to "healthy eating." The content generation unit can also use the generation AI to generate video and audio content tailored to the user's needs. The SEO optimization unit optimizes the content generated by the content generation unit for search engine algorithms. For example, the SEO optimization unit selects appropriate keywords, sets metadata, and builds internal links. The SEO optimization unit can also use the generation AI to analyze the SEO strategies of competing websites and propose optimal SEO strategies based on the analysis. As a result, the SEO countermeasure system according to the embodiment can efficiently generate high-quality content that meets user needs and achieve high rankings in search results. For example, content generated by the generation AI can be displayed at the top of search results, thereby increasing user satisfaction. Furthermore, on corporate websites, content generated by the generation AI can be displayed at the top of search results, thereby increasing the effectiveness of attracting customers.

[0030] The user needs analysis unit can identify more detailed needs by analyzing social media posts and comments in addition to search history. For example, the generation AI analyzes the user's search history as well as posts and comments on social media such as Twitter and Facebook. For example, if a user searches for "healthy eating," it collects related social media posts and identifies what specific information the user is looking for. This allows for more accurate content generation by identifying the user's detailed needs.

[0031] The user needs analysis unit can analyze the user's past purchasing history and identify needs for products and services that the user is likely to purchase. For example, the generation AI analyzes the user's past purchasing history and identifies needs for products and services that the user is likely to purchase. For example, if the user has purchased "health foods" in the past, the generation AI will use that purchasing history to identify the specific health foods and related information the user is looking for. This makes it possible to generate targeted content by identifying needs for products and services that the user is likely to purchase.

[0032] The user needs analysis unit can analyze voice search data and identify needs obtained from the voice. For example, the generation AI analyzes the user's voice search data and identifies needs obtained from the voice. For example, if a user performs a voice search for "healthy eating," the generation AI analyzes the voice data and identifies the specific information the user is seeking. In this way, by analyzing the voice search data, it is possible to identify needs obtained from the voice and generate more diverse content.

[0033] The user needs analysis unit can analyze search histories in different languages ​​and perform multilingual needs analysis. For example, the generation AI analyzes search histories in different languages ​​and performs multilingual needs analysis. For example, if a user searches for "healthy eating" in English and Japanese, the generation AI analyzes the search history and identifies the specific information the user is seeking. This enables multilingual needs analysis to generate content that caters to global users.

[0034] The content generation unit generates video or audio content based on the user's needs, and can provide information in formats other than text. For example, the content generation unit generates video content using a generation AI based on the user's needs. For example, a detailed recipe video related to "healthy eating" is generated and provided to the user. The content generation unit also generates audio content using a generation AI based on the user's needs. For example, a podcast related to "healthy eating" is generated and provided to the user. In this way, by generating video and audio content, information can be provided to the user in a variety of formats.

[0035] The content generation unit can generate customized interactive content according to the user's needs. In the content generation unit, for example, the generation AI generates a customized quiz according to the user's needs. For example, the generation AI generates a quiz about "healthy eating" and provides it to the user. In addition, the content generation unit generates a customized questionnaire according to the user's needs. For example, the generation AI generates a questionnaire about "healthy eating" and provides it to the user. In this way, by generating customized interactive content, it is possible to increase user engagement.

[0036] The content generation unit can generate content optimized for different platforms based on user needs. For example, the generation AI in the content generation unit generates content optimized for blogs based on user needs. For example, a detailed article on "healthy eating" is generated and posted on a blog. The content generation unit also generates content optimized for social media based on user needs. For example, a short post on "healthy eating" is generated and posted on social media. In this way, by generating content optimized for different platforms, it becomes possible to provide information effectively on each platform.

[0037] The content generation unit can generate series content that is updated regularly based on the user's needs. For example, the content generation unit generates series content related to "healthy eating" that is updated regularly based on the user's needs using a generation AI. For example, new recipes are provided every week. The content generation unit also generates series content related to "exercise" that is updated regularly based on the user's needs using a generation AI. For example, a new exercise plan is provided every month. In this way, by generating series content that is updated regularly, it is possible to continuously attract the user's interest.

[0038] The SEO optimization unit has a deep understanding of the user's search intent and can select keywords and set metadata based on that. For example, the generation AI in the SEO optimization unit has a deep understanding of the user's search intent and selects keywords based on that. For example, if a user is searching for "healthy eating," it will select specific related keywords. The SEO optimization unit also has a deep understanding of the user's search intent and sets metadata based on that. For example, if a user is searching for information about "exercise," it will set related metadata. This allows for a deep understanding of the user's search intent, enabling more effective keyword selection and metadata setting.

[0039] The SEO optimization unit can implement SEO measures optimized for different search engines. For example, the generation AI in the SEO optimization unit implements SEO measures optimized for Google. For example, it selects keywords and sets metadata that are compatible with Google's algorithm. The generation AI in the SEO optimization unit also implements SEO measures optimized for Bing. For example, it selects keywords and sets metadata that are compatible with Bing's algorithm. The generation AI in the SEO optimization unit also implements SEO measures optimized for Yahoo. For example, it selects keywords and sets metadata that are compatible with Yahoo's algorithm. As a result, by implementing SEO measures optimized for different search engines, it is possible to expect the site to appear at the top of search results on each search engine.

[0040] The SEO optimization unit can analyze search trends for each region and implement region-specific SEO measures. For example, the generation AI in the SEO optimization unit analyzes search trends for each region and implements region-specific SEO measures. For example, it selects keywords that are popular in a specific region and generates content specialized for that region. The generation AI in the SEO optimization unit also analyzes seasonal trends for each region and implements SEO measures based on that. For example, it selects keywords that are popular in a specific season and generates content specialized for that season. By implementing region-specific SEO measures, it is possible to expect the site to appear at the top of search results in a specific region.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The SEO system can also generate content optimized for specific time periods based on user needs. For example, if a user searches for "healthy breakfast" in the morning, the AI ​​will provide the best breakfast recipes for that time period. Similarly, if a user searches for "ways to relax" in the evening, the AI ​​will suggest the best ways to relax for that time period. This allows the system to generate content optimized for specific time periods, thereby better meeting user needs.

[0043] The SEO system can also generate content related to specific events or seasons based on user needs. For example, if a user searches for "Christmas dinner" during Christmas, the AI ​​generator will provide the perfect Christmas dinner recipe for that time of year. Similarly, if a user searches for "summer exercise" during summer, the AI ​​generator will suggest the perfect exercise plan for that time of year. This allows the system to generate content related to specific events or seasons, thereby better meeting user needs.

[0044] The SEO system can also generate content optimized for specific devices based on user needs. For example, if a user searches for "healthy eating" on a smartphone, the AI ​​generator can provide recipes optimized for smartphones. Similarly, if a user searches for "exercise" on a tablet, the AI ​​generator can suggest exercise plans optimized for tablets. This allows the system to generate content optimized for specific devices, better meeting user needs.

[0045] The SEO system can also generate content optimized for specific user groups based on their needs. For example, if a senior citizen searches for "healthy eating," the AI ​​can provide recipes that are ideal for seniors. If a student searches for "exercise," the AI ​​can suggest exercise plans that are ideal for students. This allows the system to generate content optimized for specific user groups, better meeting user needs.

[0046] The SEO system can also generate content optimized for specific regions based on user needs. For example, if a user in Japan searches for "healthy eating," the AI ​​generator can provide recipes using Japanese ingredients. Similarly, if a user in the United States searches for "exercise," the AI ​​generator can suggest exercise plans tailored to American trends. This allows the system to generate content optimized for specific regions, better meeting user needs.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The user needs analysis unit analyzes the user's search history and behavioral data. For example, if a user searches for "healthy eating," the unit analyzes the specific information the user is looking for based on the user's search history. Data mining technology can also be used to analyze the user's behavioral data and identify the user's interests. Step 2: The content generation unit generates content based on the results of the analysis by the user needs analysis unit. For example, the generation AI can be used to generate detailed articles and recipes on "healthy eating." It can also generate video and audio content that meets the user's needs. Step 3: The SEO optimization department optimizes the content generated by the content generation department for search engine algorithms, for example by selecting appropriate keywords, setting metadata, and building internal links. The generation AI can also analyze the SEO strategies of competitor sites and propose optimal SEO measures based on that analysis.

[0049] (Example 2) The SEO countermeasure system according to the embodiment of the present invention is a system that utilizes generation AI to efficiently create content that meets user needs and achieves high rankings in search results. As a result, the SEO countermeasure system can efficiently generate high-quality content that meets user needs and achieve high rankings in search results.

[0050] The SEO system according to the embodiment includes a user needs analysis unit, a content generation unit, and an SEO optimization unit. The user needs analysis unit analyzes a user's search history and behavioral data. For example, if a user searches for "healthy eating," the user needs analysis unit analyzes the user's specific search needs based on the user's search history. The user needs analysis unit can also use data mining technology to analyze the user's behavioral data and identify the user's interests. The content generation unit generates content based on the results of the analysis by the user needs analysis unit. For example, the content generation unit uses a generation AI to generate detailed articles and recipes related to "healthy eating." The content generation unit can also use the generation AI to generate video and audio content tailored to the user's needs. The SEO optimization unit optimizes the content generated by the content generation unit for search engine algorithms. For example, the SEO optimization unit selects appropriate keywords, sets metadata, and builds internal links. The SEO optimization unit can also use the generation AI to analyze the SEO strategies of competing websites and propose optimal SEO strategies based on the analysis. As a result, the SEO countermeasure system according to the embodiment can efficiently generate high-quality content that meets user needs and achieve high rankings in search results. For example, content generated by the generation AI can be displayed at the top of search results, thereby increasing user satisfaction. Furthermore, on corporate websites, content generated by the generation AI can be displayed at the top of search results, thereby increasing the effectiveness of attracting customers.

[0051] The user needs analysis unit can identify more detailed needs by analyzing social media posts and comments in addition to search history. For example, the generation AI analyzes the user's search history as well as posts and comments on social media such as Twitter and Facebook. For example, if a user searches for "healthy eating," it collects related social media posts and identifies what specific information the user is looking for. This allows for more accurate content generation by identifying the user's detailed needs.

[0052] The user needs analysis unit can analyze the user's past purchasing history and identify needs for products and services that the user is likely to purchase. For example, the generation AI analyzes the user's past purchasing history and identifies needs for products and services that the user is likely to purchase. For example, if the user has purchased "health foods" in the past, the generation AI will use that purchasing history to identify the specific health foods and related information the user is looking for. This makes it possible to generate targeted content by identifying needs for products and services that the user is likely to purchase.

[0053] The user needs analysis unit can use the emotion estimation function to estimate emotions from search history and behavioral data and perform needs analysis based on emotions. For example, the generation AI analyzes the user's search history and behavioral data and estimates the user's emotions using the emotion estimation function. For example, if a user searches for "ways to relieve stress," the generation AI estimates the user's stress level based on the search history and suggests specific ways to relieve stress. This enables more personalized content to be generated by analyzing needs based on the user's emotions.

[0054] The user needs analysis unit can analyze voice search data and identify needs obtained from the voice. For example, the generation AI analyzes the user's voice search data and identifies needs obtained from the voice. For example, if a user performs a voice search for "healthy eating," the generation AI analyzes the voice data and identifies the specific information the user is seeking. In this way, by analyzing the voice search data, it is possible to identify needs obtained from the voice and generate more diverse content.

[0055] The user needs analysis unit can analyze search histories in different languages ​​and perform multilingual needs analysis. For example, the generation AI analyzes search histories in different languages ​​and performs multilingual needs analysis. For example, if a user searches for "healthy eating" in English and Japanese, the generation AI analyzes the search history and identifies the specific information the user is seeking. This enables multilingual needs analysis to generate content that caters to global users.

[0056] The user needs analysis unit can use the emotion estimation function to analyze the emotions felt by the user while searching in real time and identify needs based on those emotions. For example, the user needs analysis unit analyzes the emotions felt by the generation AI while the user is searching in real time and identifies needs based on those emotions. For example, if a user is searching for "ways to relieve stress," the generation AI analyzes the emotions felt during the search and identifies the specific relief method the user is seeking. This makes it possible to generate more appropriate content by analyzing the emotions felt by the user while searching in real time.

[0057] The content generation unit generates video or audio content based on the user's needs, and can provide information in formats other than text. For example, the content generation unit generates video content using a generation AI based on the user's needs. For example, a detailed recipe video related to "healthy eating" is generated and provided to the user. The content generation unit also generates audio content using a generation AI based on the user's needs. For example, a podcast related to "healthy eating" is generated and provided to the user. In this way, by generating video and audio content, information can be provided to the user in a variety of formats.

[0058] The content generation unit can generate customized interactive content according to the user's needs. In the content generation unit, for example, the generation AI generates a customized quiz according to the user's needs. For example, the generation AI generates a quiz about "healthy eating" and provides it to the user. In addition, the content generation unit generates a customized questionnaire according to the user's needs. For example, the generation AI generates a questionnaire about "healthy eating" and provides it to the user. In this way, by generating customized interactive content, it is possible to increase user engagement.

[0059] The content generation unit can use the emotion estimation function to generate content in a tone and style that matches the user's emotions. For example, the generation AI in the content generation unit uses the emotion estimation function to generate content in a tone that matches the user's emotions. For example, if the user wants to relax, the generation AI generates an article about "ways to relax" in a calm tone. Also, the content generation unit uses the emotion estimation function to generate content in a style that matches the user's emotions. For example, if the user is excited, the generation AI generates an article about "exercise" in an energetic style. This makes it possible to provide more personalized information by generating content in a tone and style that matches the user's emotions.

[0060] The content generation unit can generate content optimized for different platforms based on user needs. For example, the generation AI in the content generation unit generates content optimized for blogs based on user needs. For example, a detailed article on "healthy eating" is generated and posted on a blog. The content generation unit also generates content optimized for social media based on user needs. For example, a short post on "healthy eating" is generated and posted on social media. In this way, by generating content optimized for different platforms, it becomes possible to provide information effectively on each platform.

[0061] The content generation unit can generate series content that is updated regularly based on the user's needs. For example, the content generation unit generates series content related to "healthy eating" that is updated regularly based on the user's needs using a generation AI. For example, new recipes are provided every week. The content generation unit also generates series content related to "exercise" that is updated regularly based on the user's needs using a generation AI. For example, a new exercise plan is provided every month. In this way, by generating series content that is updated regularly, it is possible to continuously attract the user's interest.

[0062] The content generation unit can use the emotion estimation function to generate content that will evoke the most positive emotions in the user. For example, the content generation unit uses the emotion estimation function to generate content that will evoke the most positive emotions in the user. For example, if a user searches for "healthy eating," the content generation unit provides information on recipes and ingredients that will evoke positive emotions. Furthermore, the content generation unit uses the emotion estimation function to provide an exercise plan that will evoke the most positive emotions in the user. For example, if a user searches for information on "exercise," the content generation unit provides an exercise plan that will evoke positive emotions. This allows the generation of content that will evoke the most positive emotions in the user, thereby increasing user satisfaction.

[0063] The SEO optimization unit has a deep understanding of the user's search intent and can select keywords and set metadata based on that. For example, the generation AI in the SEO optimization unit has a deep understanding of the user's search intent and selects keywords based on that. For example, if a user is searching for "healthy eating," it will select specific related keywords. The SEO optimization unit also has a deep understanding of the user's search intent and sets metadata based on that. For example, if a user is searching for information about "exercise," it will set related metadata. This allows for a deep understanding of the user's search intent, enabling more effective keyword selection and metadata setting.

[0064] The SEO optimization unit uses the emotion estimation function to predict the emotions a user will have when viewing search results and implements SEO measures that elicit positive emotions. For example, the SEO optimization unit uses the emotion estimation function to predict the emotions a user will have when viewing search results and implements SEO measures that elicit positive emotions. For example, when a user searches for "healthy eating," the SEO optimization unit sets a title and meta description that elicits positive emotions. In addition, when a user searches for information on "exercise," the SEO optimization unit uses the emotion estimation function to implement SEO measures that elicit positive emotions. This elicits positive emotions when users view search results, which is expected to improve click-through rates and dwell times.

[0065] The SEO optimization unit can implement SEO measures optimized for different search engines. For example, the generation AI in the SEO optimization unit implements SEO measures optimized for Google. For example, it selects keywords and sets metadata that are compatible with Google's algorithm. The generation AI in the SEO optimization unit also implements SEO measures optimized for Bing. For example, it selects keywords and sets metadata that are compatible with Bing's algorithm. The generation AI in the SEO optimization unit also implements SEO measures optimized for Yahoo. For example, it selects keywords and sets metadata that are compatible with Yahoo's algorithm. As a result, by implementing SEO measures optimized for different search engines, it is possible to expect the site to appear at the top of search results on each search engine.

[0066] The SEO optimization unit can analyze search trends for each region and implement region-specific SEO measures. For example, the generation AI in the SEO optimization unit analyzes search trends for each region and implements region-specific SEO measures. For example, it selects keywords that are popular in a specific region and generates content specialized for that region. The generation AI in the SEO optimization unit also analyzes seasonal trends for each region and implements SEO measures based on that. For example, it selects keywords that are popular in a specific season and generates content specialized for that season. By implementing region-specific SEO measures, it is possible to expect the site to appear at the top of search results in a specific region.

[0067] The SEO optimization unit uses the emotion estimation function to analyze the emotion a user feels when they click on a search result in real time, and can continuously implement optimal SEO measures. For example, the generation AI uses the emotion estimation function to analyze the emotion a user feels when they click on a search result in real time, and can continuously implement optimal SEO measures. For example, when a user searches for "healthy eating," the SEO optimization unit implements SEO measures that elicit positive emotions. Furthermore, when a user searches for information on "exercise," the generation AI uses the emotion estimation function to implement SEO measures that elicit positive emotions. In this way, by analyzing the emotion a user feels when they click on a search result in real time, it is possible to continuously implement optimal SEO measures.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The SEO system can also generate content optimized for specific time periods based on user needs. For example, if a user searches for "healthy breakfast" in the morning, the AI ​​will provide the best breakfast recipes for that time period. Similarly, if a user searches for "ways to relax" in the evening, the AI ​​will suggest the best ways to relax for that time period. This allows the system to generate content optimized for specific time periods, thereby better meeting user needs.

[0070] The SEO system can also generate content related to specific events or seasons based on user needs. For example, if a user searches for "Christmas dinner" during Christmas, the AI ​​generator will provide the perfect Christmas dinner recipe for that time of year. Similarly, if a user searches for "summer exercise" during summer, the AI ​​generator will suggest the perfect exercise plan for that time of year. This allows the system to generate content related to specific events or seasons, thereby better meeting user needs.

[0071] The SEO system can also generate content optimized for specific devices based on user needs. For example, if a user searches for "healthy eating" on a smartphone, the AI ​​generator can provide recipes optimized for smartphones. Similarly, if a user searches for "exercise" on a tablet, the AI ​​generator can suggest exercise plans optimized for tablets. This allows the system to generate content optimized for specific devices, better meeting user needs.

[0072] The SEO system can also generate content optimized for specific user groups based on their needs. For example, if a senior citizen searches for "healthy eating," the AI ​​can provide recipes that are ideal for seniors. If a student searches for "exercise," the AI ​​can suggest exercise plans that are ideal for students. This allows the system to generate content optimized for specific user groups, better meeting user needs.

[0073] The SEO system can also generate content optimized for specific regions based on user needs. For example, if a user in Japan searches for "healthy eating," the AI ​​generator can provide recipes using Japanese ingredients. Similarly, if a user in the United States searches for "exercise," the AI ​​generator can suggest exercise plans tailored to American trends. This allows the system to generate content optimized for specific regions, better meeting user needs.

[0074] The SEO system can further estimate the user's emotions and, based on the estimated emotions, generate content in a calm tone if the user wants to relax. For example, if a user searches for "ways to relax," the generation AI will generate an article about ways to relax in a calm tone. Also, if a user searches for "ways to relieve stress," the generation AI will generate an article about ways to relieve stress in a tone that elicits a relaxed feeling. This makes it possible to provide more personalized information by generating content in a tone that matches the user's emotions.

[0075] The SEO system can also estimate the user's emotions and, based on the estimated emotions, generate content in an energetic tone if the user is excited. For example, if a user searches for "exercise," the generation AI will generate an article about exercise in an energetic tone. Similarly, if a user searches for "adventure travel," the generation AI will generate an article about adventure travel in a tone that evokes an excited mood. This allows for more personalized information provision by generating content in a tone that matches the user's emotions.

[0076] The SEO system can further estimate the user's emotions and, based on the estimated emotions, generate content in a comforting tone if the user is sad. For example, if a user searches for "how to get over a breakup," the generation AI will generate an article about how to get over it in a comforting tone. Similarly, if a user searches for "losing a pet," the generation AI will generate an article about losing a pet in a comforting tone. This allows for more personalized information provision by generating content in a tone that matches the user's emotions.

[0077] The SEO system can also estimate the user's emotions and, based on the estimated emotions, generate content in a tone that gives a sense of security if the user is feeling anxious. For example, if a user searches for "health checkup results," the generation AI will generate an article about the results in a tone that gives a sense of security. Similarly, if a user searches for "interview preparation," the generation AI will generate an article about interview preparation in a tone that gives a sense of security. This allows for more personalized information provision by generating content in a tone that matches the user's emotions.

[0078] The SEO system can also estimate the user's emotions and, based on the estimated emotions, generate content in a tone that amplifies the user's joy if the user is happy. For example, if a user searches for "successful experiences," the generation AI will generate articles about successful experiences in a tone that amplifies that joy. Similarly, if a user searches for "congratulatory messages," the generation AI will generate articles about congratulatory messages in a tone that amplifies that joy. This allows for more personalized information provision by generating content in a tone that matches the user's emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The user needs analysis unit analyzes the user's search history and behavioral data. For example, if a user searches for "healthy eating," the unit analyzes the specific information the user is looking for based on the user's search history. Data mining technology can also be used to analyze the user's behavioral data and identify the user's interests. Step 2: The content generation unit generates content based on the results of the analysis by the user needs analysis unit. For example, the generation AI can be used to generate detailed articles and recipes on "healthy eating." It can also generate video and audio content that meets the user's needs. Step 3: The SEO optimization department optimizes the content generated by the content generation department for search engine algorithms, for example by selecting appropriate keywords, setting metadata, and building internal links. The generation AI can also analyze the SEO strategies of competitor sites and propose optimal SEO measures based on that analysis.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user needs analysis unit that analyzes user search history and behavioral data; a content generation unit that generates content based on the results of the analysis by the user needs analysis unit; an SEO optimization unit that optimizes the content generated by the content generation unit for a search engine algorithm; A system characterized by:

2. The user needs analysis unit In addition to the search history, social media posts and comments are analyzed to identify more specific needs.

2. The system of claim 1.

3. The user needs analysis unit Analyzing the user's voice search data and identifying needs obtained from the voice 2. The system of claim 1.

4. The content generation unit Generate video or audio content based on the user's needs and provide information in formats other than text 2. The system of claim 1.

5. The SEO optimization unit: Analyze the SEO strategies of competitor sites and propose optimal SEO measures based on the results 2. The system of claim 1.

6. The user needs analysis unit Emotions are estimated from the search history and behavioral data, and needs analysis is performed based on emotions.

2. The system of claim 1.

7. The content generation unit Generate content in a tone and style that matches the user's emotions 2. The system of claim 1.

8. The SEO optimization unit: Predict the emotions users will feel when viewing search results and implement SEO measures that will elicit positive emotions.

2. The system of claim 1.

Images