System

Abstract

An object of a system according to an embodiment is to efficiently collect, summarize, and report news based on personal hobbies and life patterns.SOLUTION: A system according to an embodiment includes a generation AI, a WEB crawler, a summary generation unit, and a reporting unit. The generation AI generates a retrieval word on the basis of a user's hobby and life pattern. The WEB crawler collects information based on the retrieval word generated by the generation AI. The summary generation unit summarizes the data collected by the WEB crawler. The report unit reports the summary text generated by the summary generation unit in a chat conversation form.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 collect, summarize, and report news based on personal hobbies and lifestyle patterns.

[0005] The system according to the embodiment aims to efficiently collect, summarize, and report news based on personal hobbies and lifestyle patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a web crawler, a summary generation unit, and a reporting unit. The generation AI generates search terms based on a user's hobbies and lifestyle patterns. The web crawler collects data based on the search terms generated by the generation AI. The summary generation unit summarizes the data collected by the web crawler. The reporting unit reports the summary text generated by the summary generation unit in a chat dialogue format. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, summarize, and report news based on personal hobbies and lifestyle patterns. [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) A news AI system according to an embodiment of the present invention reports on events that occurred yesterday and today based on personal hobbies and lifestyle patterns. This news AI system is composed of a generation AI (large-scale language model, LLM) and a web crawler for search. The generation AI generates search terms for the crawler, and the crawler collects related data. The collected data is then summarized and reported daily. Reporting is conducted in the form of a chat dialogue, and if recent topics of interest or concern come up during the conversation, they are added to the search terms. This allows the news AI system to efficiently report news tailored to the user's interests. For example, if a user is interested in sports, the system will prioritize reporting sports-related news, and if the user is interested in the latest technology, the system will report technology-related news. Furthermore, any topics that the user develops interest in during the conversation can be reflected in the next report.

[0029] A news AI system according to an embodiment includes a generation AI, a web crawler, a summary generation unit, and a reporting unit. The generation AI generates search terms based on a user's hobbies and lifestyle patterns. For example, the generation AI analyzes the user's past search history, browsing history, and interaction history to understand the user's interests. The generation AI generates search terms using a text generation AI (e.g., LLM). The generation AI can also generate search terms that reflect the user's interests using a multimodal generation AI. The web crawler collects data based on the search terms generated by the generation AI. For example, the web crawler collects the latest information from news sites, blogs, social media, etc. The web crawler can collect data from specific websites or using APIs. The summary generation unit summarizes the data collected by the web crawler. For example, the summary generation unit analyzes the collected data and generates a summary to report to the user. The summary generation unit generates the summary using a text generation AI. The summary generation unit can also extract and summarize important parts of a sentence. The reporting unit reports the summary generated by the summary generation unit in a chat dialogue format. For example, the reporting unit reports the generated summary to the user in a chat dialogue format. When the user talks about something that they have recently been interested in or concerned about during the dialogue, the reporting unit adds that to the search words and reflects them in the next report. This allows the news AI system according to the embodiment to report appropriate news based on the user's hobbies and lifestyle patterns.

[0030] The generation AI can analyze biometric data collected from a smart device and create a detailed profile of a user's lifestyle patterns. For example, the generation AI analyzes heart rate data collected from the user's smart device and creates a detailed profile of a user's lifestyle patterns. For example, the generation AI can understand the user's activity level and stress state based on heart rate fluctuations. The generation AI can also analyze sleep pattern data collected from the user's smart device and create a detailed profile of a user's lifestyle patterns. For example, the generation AI can understand the user's health condition based on sleep quality and sleep duration. The generation AI can also analyze other biometric data collected from the user's smart device (for example, number of steps taken, calorie consumption, etc.) and create a detailed profile of a user's lifestyle patterns. This allows the generation AI to create a detailed profile of a user's lifestyle patterns.

[0031] Generative AI can analyze social media activity and learn changes in interests in real time. For example, generative AI can analyze a user's social media activity and build a system that learns changes in interests in real time. For example, it can identify interests based on posts that a user has "liked" or "shared." Generative AI can also analyze a user's social media activity and learn changes in interests in real time. For example, if a user shows a lot of reactions to a particular topic, it can prioritize reporting news related to that topic. Generative AI can also analyze a user's social media activity and develop algorithms that learn changes in interests in real time. This allows it to learn changes in a user's interests in real time.

[0032] When learning about hobbies and interests, the generative AI can also take into account the hobbies of family and friends, and provide common topics to discuss. For example, the generative AI builds a system that takes into account the hobbies of family and friends when learning about a user's hobbies and interests. For example, it provides common topics based on topics that interest family and friends. Furthermore, when learning about a user's hobbies and interests, the generative AI also takes into account the hobbies of family and friends, and provides common topics to discuss. For example, it reports news based on topics that interest family and friends. Furthermore, the generative AI develops an algorithm that takes into account the hobbies of family and friends, and provides common topics to discuss. This makes it possible to provide common topics that take into account the hobbies of family and friends.

[0033] Generative AI can share data between different devices to create a more comprehensive profile. For example, generative AI can share data between different devices to build a system that learns a user's hobbies and interests. For example, it can collect data from devices such as smartphones, tablets, and smartwatches. Generative AI can also share data between different devices to create a more comprehensive profile. For example, it can synchronize data using a cloud service. Generative AI can also develop algorithms to share data between different devices to create a comprehensive profile. This allows it to share data between different devices to create a comprehensive profile.

[0034] Generative AI can analyze a user's past search terms and their results, and develop an algorithm that automatically generates the most relevant search terms. Generative AI can, for example, analyze a user's past search terms and their results, and develop an algorithm that automatically generates the most relevant search terms. For example, it can extract highly relevant keywords based on past search history. Generative AI can also analyze a user's past search terms and their results, and automatically generate the most relevant search terms. For example, it can generate search terms based on the degree of similarity of search results and the user's level of interest. Generative AI can also analyze a user's past search terms and their results, and develop an algorithm that automatically generates the most relevant search terms. This makes it possible to analyze past search terms and their results, and automatically generate highly relevant search terms.

[0035] The generation AI can generate related search terms based on keywords extracted from the dialogue history. The generation AI, for example, builds a system that generates related search terms based on keywords extracted from a user's dialogue history. For example, it extracts keywords that were frequently used during a dialogue. The generation AI also generates related search terms based on keywords extracted from the dialogue history. For example, it generates search terms based on keywords that the user showed interest in during a dialogue. The generation AI also develops an algorithm for generating related search terms based on keywords extracted from the dialogue history. This makes it possible to generate related search terms based on keywords extracted from the dialogue history.

[0036] The generation AI can generate search terms relevant to specific time periods based on the user's interests and provide timely news. The generation AI, for example, builds a system that generates search terms relevant to specific time periods based on the user's interests. For example, it generates search terms related to morning news and evening news. The generation AI also generates search terms relevant to specific time periods based on the user's interests and provides timely news. For example, it reports news relevant to specific time periods. The generation AI also develops an algorithm for generating search terms relevant to specific time periods based on the user's interests and providing timely news. This makes it possible to generate search terms relevant to specific time periods and provide timely news.

[0037] Web crawlers can develop algorithms to evaluate the reliability of the data they collect and collect only highly reliable data. For example, they can filter data based on the reliability scores of news sites and blogs. Web crawlers can also evaluate the reliability of the data they collect and collect only highly reliable data. For example, they can select data based on the reliability of the data source and the accuracy of the data. Web crawlers can also develop algorithms to evaluate the reliability of the data they collect and collect only highly reliable data. This allows them to collect only highly reliable data.

[0038] A web crawler can analyze a user's past news browsing history and prioritize collecting the most relevant data. For example, a web crawler can analyze a user's past news browsing history and build a system that prioritizes collecting the most relevant data. For example, it can evaluate relevance based on keywords in news articles viewed in the past. A web crawler can also analyze a user's past news browsing history and prioritize collecting the most relevant data. For example, it can select data based on the past browsing history. A web crawler can also analyze a user's past news browsing history and develop an algorithm for prioritize collecting the most relevant data. This makes it possible to analyze past news browsing history and prioritize collecting the most relevant data.

[0039] Web crawlers can integrate data from different data sources and provide comprehensive news reports. For example, web crawlers build systems that integrate data from different data sources and provide comprehensive news reports. For example, they combine data from news sites, blogs, and social media into a single view. Web crawlers also integrate data from different data sources and provide comprehensive news reports. For example, they collect and integrate data from different data sources. Web crawlers also develop algorithms for integrating data from different data sources and providing comprehensive news reports. This allows them to integrate data from different data sources and provide comprehensive news reports.

[0040] Web crawlers can also collect data specialized for specific regions or cultural areas, allowing them to cater to diverse user interests. For example, web crawlers collect data specialized for specific regions or cultural areas to build systems that cater to diverse user interests. For example, they collect data related to local news and cultural events. Web crawlers can also collect data specialized for specific regions or cultural areas to cater to diverse user interests. For example, they collect data from regional news sites or blogs specialized in cultural areas. Web crawlers can also collect data specialized for specific regions or cultural areas to develop algorithms that cater to diverse user interests. This allows them to collect data specialized for specific regions or cultural areas to cater to diverse interests.

[0041] The summary generation unit can automatically insert images and videos related to the summary text to generate summaries that are visually easy to understand. The summary generation unit, for example, builds a system that automatically inserts images and videos related to the summary text. For example, it adds images and videos related to a news article to the summary text. The summary generation unit also automatically inserts images and videos related to the summary text to generate summaries that are visually easy to understand. For example, it uses an image search algorithm to select related images and insert them into the summary text. The summary generation unit also develops an algorithm for automatically inserting images and videos related to the summary text to generate summaries that are visually easy to understand. This makes it possible to automatically insert images and videos related to the summary text to generate summaries that are visually easy to understand.

[0042] The summary generation unit automatically translates the summary text into different languages, allowing news reports to be made from an international perspective. The summary generation unit, for example, builds a system for automatically translating the summary text into different languages. For example, it translates into multiple languages, such as English, French, and Chinese. The summary generation unit also automatically translates the summary text into different languages ​​and makes news reports from an international perspective. For example, it reports news based on the summary text translated into different languages. The summary generation unit also develops an algorithm for automatically translating the summary text into different languages ​​and making news reports from an international perspective. This allows the summary text to be automatically translated into different languages ​​and makes news reports from an international perspective.

[0043] The summary generation unit can generate the summary text in audio format, allowing the user to receive the news audibly. The summary generation unit, for example, builds a system for generating the summary text in audio format. For example, the summary text is provided in audio format using a text-to-speech conversion technology. The summary generation unit also generates the summary text in audio format, allowing the user to receive the news audibly. For example, the summary generation unit provides the summary text in audio format using a speech synthesis algorithm. The summary generation unit also develops an algorithm for generating the summary text in audio format, allowing the user to receive the news audibly. This allows the summary text to be generated in audio format, allowing the user to receive the news audibly.

[0044] The reporting unit can analyze the user's dialogue history and develop an algorithm that automatically selects the most effective dialogue format. The reporting unit, for example, analyzes the user's dialogue history and develops an algorithm that automatically selects the most effective dialogue format. For example, an effective dialogue format is identified based on past dialogue history. The reporting unit also analyzes the user's dialogue history and automatically selects the most effective dialogue format. For example, the dialogue format is selected based on the user's response and the success rate of the dialogue. The reporting unit also analyzes the user's dialogue history and develops an algorithm for automatically selecting the most effective dialogue format. This makes it possible to analyze the user's dialogue history and automatically select the most effective dialogue format.

[0045] The reporting unit can support conversations on different devices in a chat conversation format. For example, the reporting unit builds a system that supports conversations on different devices in a chat conversation format. For example, it enables conversations on smartphones and smart speakers. The reporting unit also supports conversations on different devices in a chat conversation format. For example, it supports conversations on devices such as smartphones, tablets, and smart speakers. The reporting unit also develops an algorithm for supporting conversations on different devices in a chat conversation format. This makes it possible to support conversations on different devices.

[0046] The reporting unit can interactively report the most relevant news based on the user's past dialogue history. The reporting unit, for example, builds a system that interactively reports the most relevant news based on the user's past dialogue history. For example, the reporting unit analyzes the past dialogue history and selects highly relevant news. The reporting unit also interactively reports the most relevant news based on the user's past dialogue history. For example, the reporting unit selects news based on the past dialogue history. The reporting unit also develops an algorithm for interactively reporting the most relevant news based on the user's past dialogue history. This makes it possible to report the most relevant news based on the past dialogue history.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The news AI system can utilize the user's geographical location information to provide news specific to the area. For example, it can report weather forecasts, traffic information, and information on local events in the area where the user lives. If the user is traveling, it can provide tourist information and local news for the destination. It can also provide information on local specialties and tourist spots based on the user's location information.

[0049] The news AI system can analyze a user's learning history and provide news and information related to their studies. For example, if a user is studying a specific field, it can report the latest research results and news related to that field. It can also provide information on learning resources and online courses that interest the user. It can also suggest appropriate learning advice and resources based on the user's learning progress.

[0050] The news AI system can analyze a user's purchasing history and provide related news and product information. For example, it can report news and reviews related to products the user recently purchased. It can also provide the latest information on products and services that the user is interested in. It can also provide recommendations for related products and services based on the user's purchasing history.

[0051] The news AI system can provide information about related communities and events based on a user's hobbies and interests. For example, if a user has a particular hobby, it can report information about online communities and events related to that hobby. It can also provide information about seminars and workshops in areas that interest the user. It can also encourage participation in appropriate communities and events based on the user's hobbies and interests.

[0052] The news AI system can analyze a user's reading history and provide information on related books and articles. For example, it can report news and reviews related to books the user has recently read. It can also provide information on the latest books and articles in areas that interest the user. It can also provide recommendations for related books and articles based on the user's reading history.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The generation AI generates search terms based on the user's hobbies and lifestyle patterns. For example, the generation AI analyzes the user's past search history, browsing history, and interaction history to understand the user's interests and concerns. The generation AI generates search terms using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to generate search terms that reflect the user's interests and concerns. Step 2: The web crawler collects data based on the search terms generated by the generation AI. For example, the web crawler collects the latest information from news sites, blogs, social media, etc. The web crawler can collect data from specific websites or using APIs. Step 3: The summary generator summarizes the data collected by the web crawler. For example, the summary generator analyzes the collected data and generates a summary to report to the user. The summary generator uses text generation AI to generate the summary. The summary generator can also extract and summarize important parts of the text. Step 4: The reporting unit reports the summary text generated by the summary generation unit in a chat dialogue format. For example, the reporting unit reports the generated summary text to the user in a chat dialogue format. If the user mentions something that they have recently been interested in or concerned about during the dialogue, the reporting unit adds that to the search words and reflects it in the next report.

[0055] (Example 2) A news AI system according to an embodiment of the present invention reports on events that occurred yesterday and today based on personal hobbies and lifestyle patterns. This news AI system is composed of a generation AI (large-scale language model, LLM) and a web crawler for search. The generation AI generates search terms for the crawler, and the crawler collects related data. The collected data is then summarized and reported daily. Reporting is conducted in the form of a chat dialogue, and if recent topics of interest or concern come up during the conversation, they are added to the search terms. This allows the news AI system to efficiently report news tailored to the user's interests. For example, if a user is interested in sports, the system will prioritize reporting sports-related news, and if the user is interested in the latest technology, the system will report technology-related news. Furthermore, any topics that the user develops interest in during the conversation can be reflected in the next report.

[0056] A news AI system according to an embodiment includes a generation AI, a web crawler, a summary generation unit, and a reporting unit. The generation AI generates search terms based on a user's hobbies and lifestyle patterns. For example, the generation AI analyzes the user's past search history, browsing history, and interaction history to understand the user's interests. The generation AI generates search terms using a text generation AI (e.g., LLM). The generation AI can also generate search terms that reflect the user's interests using a multimodal generation AI. The web crawler collects data based on the search terms generated by the generation AI. For example, the web crawler collects the latest information from news sites, blogs, social media, etc. The web crawler can collect data from specific websites or using APIs. The summary generation unit summarizes the data collected by the web crawler. For example, the summary generation unit analyzes the collected data and generates a summary to report to the user. The summary generation unit generates the summary using a text generation AI. The summary generation unit can also extract and summarize important parts of a sentence. The reporting unit reports the summary generated by the summary generation unit in a chat dialogue format. For example, the reporting unit reports the generated summary to the user in a chat dialogue format. When the user talks about something that they have recently been interested in or concerned about during the dialogue, the reporting unit adds that to the search words and reflects them in the next report. This allows the news AI system according to the embodiment to report appropriate news based on the user's hobbies and lifestyle patterns.

[0057] The generation AI can use the emotion estimation function to analyze changes in emotions from the dialogue history and learn changes in hobbies and interests based on emotions. For example, the generation AI analyzes the user's past dialogue history and identifies changes in emotions using the emotion estimation function. For example, if the user expresses positive emotions toward a particular topic, it will prioritize reporting news related to that topic. The generation AI also uses the emotion estimation function to analyze changes in the user's emotions and learn changes in hobbies and interests based on emotions. For example, if the user expresses negative emotions toward a particular topic, it will not report news related to that topic. In this way, it can learn changes in hobbies and interests based on the user's emotions.

[0058] The generation AI can analyze biometric data collected from a smart device and create a detailed profile of a user's lifestyle patterns. For example, the generation AI analyzes heart rate data collected from the user's smart device and creates a detailed profile of a user's lifestyle patterns. For example, the generation AI can understand the user's activity level and stress state based on heart rate fluctuations. The generation AI can also analyze sleep pattern data collected from the user's smart device and create a detailed profile of a user's lifestyle patterns. For example, the generation AI can understand the user's health condition based on sleep quality and sleep duration. The generation AI can also analyze other biometric data collected from the user's smart device (for example, number of steps taken, calorie consumption, etc.) and create a detailed profile of a user's lifestyle patterns. This allows the generation AI to create a detailed profile of a user's lifestyle patterns.

[0059] Generative AI can analyze social media activity and learn changes in interests in real time. For example, generative AI can analyze a user's social media activity and build a system that learns changes in interests in real time. For example, it can identify interests based on posts that a user has "liked" or "shared." Generative AI can also analyze a user's social media activity and learn changes in interests in real time. For example, if a user shows a lot of reactions to a particular topic, it can prioritize reporting news related to that topic. Generative AI can also analyze a user's social media activity and develop algorithms that learn changes in interests in real time. This allows it to learn changes in a user's interests in real time.

[0060] When learning about hobbies and interests, the generative AI can also take into account the hobbies of family and friends, and provide common topics to discuss. For example, the generative AI builds a system that takes into account the hobbies of family and friends when learning about a user's hobbies and interests. For example, it provides common topics based on topics that interest family and friends. Furthermore, when learning about a user's hobbies and interests, the generative AI also takes into account the hobbies of family and friends, and provides common topics to discuss. For example, it reports news based on topics that interest family and friends. Furthermore, the generative AI develops an algorithm that takes into account the hobbies of family and friends, and provides common topics to discuss. This makes it possible to provide common topics that take into account the hobbies of family and friends.

[0061] Generative AI can share data between different devices to create a more comprehensive profile. For example, generative AI can share data between different devices to build a system that learns a user's hobbies and interests. For example, it can collect data from devices such as smartphones, tablets, and smartwatches. Generative AI can also share data between different devices to create a more comprehensive profile. For example, it can synchronize data using a cloud service. Generative AI can also develop algorithms to share data between different devices to create a comprehensive profile. This allows it to share data between different devices to create a comprehensive profile.

[0062] The generation AI can use the emotion estimation function to prioritize generating search terms that evoke positive emotions in users about specific topics. For example, the generation AI uses the emotion estimation function to build a system that prioritizes generating search terms that evoke positive emotions in users about specific topics. For example, it generates search terms based on topics that users have "liked" or "shared." The generation AI also uses the emotion estimation function to prioritize generating search terms that evoke positive emotions in users about specific topics. For example, it reports news related to topics that users have expressed positive emotions about. The generation AI also uses the emotion estimation function to develop an algorithm that prioritizes generating search terms that evoke positive emotions in users about specific topics. This allows it to prioritize generating search terms that evoke positive emotions in users.

[0063] Generative AI can analyze a user's past search terms and their results, and develop an algorithm that automatically generates the most relevant search terms. Generative AI can, for example, analyze a user's past search terms and their results, and develop an algorithm that automatically generates the most relevant search terms. For example, it can extract highly relevant keywords based on past search history. Generative AI can also analyze a user's past search terms and their results, and automatically generate the most relevant search terms. For example, it can generate search terms based on the degree of similarity of search results and the user's level of interest. Generative AI can also analyze a user's past search terms and their results, and develop an algorithm that automatically generates the most relevant search terms. This makes it possible to analyze past search terms and their results, and automatically generate highly relevant search terms.

[0064] The generation AI can generate related search terms based on keywords extracted from the dialogue history. The generation AI, for example, builds a system that generates related search terms based on keywords extracted from a user's dialogue history. For example, it extracts keywords that were frequently used during a dialogue. The generation AI also generates related search terms based on keywords extracted from the dialogue history. For example, it generates search terms based on keywords that the user showed interest in during a dialogue. The generation AI also develops an algorithm for generating related search terms based on keywords extracted from the dialogue history. This makes it possible to generate related search terms based on keywords extracted from the dialogue history.

[0065] The generation AI can generate search terms relevant to specific time periods based on the user's interests and provide timely news. The generation AI, for example, builds a system that generates search terms relevant to specific time periods based on the user's interests. For example, it generates search terms related to morning news and evening news. The generation AI also generates search terms relevant to specific time periods based on the user's interests and provides timely news. For example, it reports news relevant to specific time periods. The generation AI also develops an algorithm for generating search terms relevant to specific time periods based on the user's interests and providing timely news. This makes it possible to generate search terms relevant to specific time periods and provide timely news.

[0066] The generation AI uses the emotion estimation function to generate the search terms that users are most interested in in real time, and can reflect the results in the next news selection. For example, the generation AI uses the emotion estimation function to build a system that generates the search terms that users are most interested in in real time. For example, it generates search terms based on the user's emotional reaction. The generation AI also uses the emotion estimation function to generate the search terms that users are most interested in in real time, and reflects the results in the next news selection. For example, it selects news based on search terms that users expressed positive emotions about. The generation AI also uses the emotion estimation function to develop an algorithm that generates the search terms that users are most interested in in real time, and reflects the results in the next news selection. This allows the search terms that users are most interested in to be generated in real time, and reflected in the next news selection.

[0067] Web crawlers can develop algorithms to evaluate the reliability of the data they collect and collect only highly reliable data. For example, they can filter data based on the reliability scores of news sites and blogs. Web crawlers can also evaluate the reliability of the data they collect and collect only highly reliable data. For example, they can select data based on the reliability of the data source and the accuracy of the data. Web crawlers can also develop algorithms to evaluate the reliability of the data they collect and collect only highly reliable data. This allows them to collect only highly reliable data.

[0068] A web crawler can analyze a user's past news browsing history and prioritize collecting the most relevant data. For example, a web crawler can analyze a user's past news browsing history and build a system that prioritizes collecting the most relevant data. For example, it can evaluate relevance based on keywords in news articles viewed in the past. A web crawler can also analyze a user's past news browsing history and prioritize collecting the most relevant data. For example, it can select data based on the past browsing history. A web crawler can also analyze a user's past news browsing history and develop an algorithm for prioritize collecting the most relevant data. This makes it possible to analyze past news browsing history and prioritize collecting the most relevant data.

[0069] Web crawlers can integrate data from different data sources and provide comprehensive news reports. For example, web crawlers build systems that integrate data from different data sources and provide comprehensive news reports. For example, they combine data from news sites, blogs, and social media into a single view. Web crawlers also integrate data from different data sources and provide comprehensive news reports. For example, they collect and integrate data from different data sources. Web crawlers also develop algorithms for integrating data from different data sources and providing comprehensive news reports. This allows them to integrate data from different data sources and provide comprehensive news reports.

[0070] Web crawlers can also collect data specialized for specific regions or cultural areas, allowing them to cater to diverse user interests. For example, web crawlers collect data specialized for specific regions or cultural areas to build systems that cater to diverse user interests. For example, they collect data related to local news and cultural events. Web crawlers can also collect data specialized for specific regions or cultural areas to cater to diverse user interests. For example, they collect data from regional news sites or blogs specialized in cultural areas. Web crawlers can also collect data specialized for specific regions or cultural areas to develop algorithms that cater to diverse user interests. This allows them to collect data specialized for specific regions or cultural areas to cater to diverse interests.

[0071] A web crawler can use the emotion estimation function to analyze a user's emotional response to collected data in real time and reflect the results in the next data collection. For example, a web crawler can use the emotion estimation function to build a system that analyzes a user's emotional response to collected data in real time. For example, it can analyze facial expressions and voice when reading a news article. The web crawler can also use the emotion estimation function to analyze a user's emotional response to collected data in real time and reflect the results in the next data collection. For example, it can collect the next data based on data in which the user expressed positive emotions. The web crawler can also use the emotion estimation function to analyze a user's emotional response to collected data in real time and develop an algorithm to reflect the results in the next data collection. This allows a user's emotional response to be analyzed in real time and reflected in the next data collection.

[0072] The summary generation unit can use the emotion estimation function to incorporate elements that elicit positive emotions into the summary text. For example, the summary generation unit uses the emotion estimation function to build a system that incorporates elements that elicit positive emotions into the summary text. For example, positive expressions and encouraging words are added to the summary text. The summary generation unit also uses the emotion estimation function to incorporate elements that elicit positive emotions into the summary text. For example, positive language and encouraging messages are added to the summary text. The summary generation unit also uses the emotion estimation function to develop an algorithm for incorporating elements that elicit positive emotions into the summary text. This makes it possible to incorporate elements that elicit positive emotions into the summary text.

[0073] The summary generation unit can automatically insert images and videos related to the summary text to generate summaries that are visually easy to understand. The summary generation unit, for example, builds a system that automatically inserts images and videos related to the summary text. For example, it adds images and videos related to a news article to the summary text. The summary generation unit also automatically inserts images and videos related to the summary text to generate summaries that are visually easy to understand. For example, it uses an image search algorithm to select related images and insert them into the summary text. The summary generation unit also develops an algorithm for automatically inserting images and videos related to the summary text to generate summaries that are visually easy to understand. This makes it possible to automatically insert images and videos related to the summary text to generate summaries that are visually easy to understand.

[0074] The summary generation unit automatically translates the summary text into different languages, allowing news reports to be made from an international perspective. The summary generation unit, for example, builds a system for automatically translating the summary text into different languages. For example, it translates into multiple languages, such as English, French, and Chinese. The summary generation unit also automatically translates the summary text into different languages ​​and makes news reports from an international perspective. For example, it reports news based on the summary text translated into different languages. The summary generation unit also develops an algorithm for automatically translating the summary text into different languages ​​and making news reports from an international perspective. This allows the summary text to be automatically translated into different languages ​​and makes news reports from an international perspective.

[0075] The summary generation unit can generate the summary text in audio format, allowing the user to receive the news audibly. The summary generation unit, for example, builds a system for generating the summary text in audio format. For example, the summary text is provided in audio format using a text-to-speech conversion technology. The summary generation unit also generates the summary text in audio format, allowing the user to receive the news audibly. For example, the summary generation unit provides the summary text in audio format using a speech synthesis algorithm. The summary generation unit also develops an algorithm for generating the summary text in audio format, allowing the user to receive the news audibly. This allows the summary text to be generated in audio format, allowing the user to receive the news audibly.

[0076] The reporting unit can use the emotion estimation function to select a dialogue style according to the user's emotion and promote positive dialogue. The reporting unit, for example, uses the emotion estimation function to build a system that selects a dialogue style according to the user's emotion. For example, if the user shows positive emotion, the dialogue proceeds in a bright tone. The reporting unit also uses the emotion estimation function to select a dialogue style according to the user's emotion and promote positive dialogue. For example, if the user shows negative emotion, the dialogue proceeds using encouraging words. The reporting unit also uses the emotion estimation function to develop an algorithm for selecting a dialogue style according to the user's emotion and promoting positive dialogue. This makes it possible to select a dialogue style according to the user's emotion and promote positive dialogue.

[0077] The reporting unit can analyze the user's dialogue history and develop an algorithm that automatically selects the most effective dialogue format. The reporting unit, for example, analyzes the user's dialogue history and develops an algorithm that automatically selects the most effective dialogue format. For example, an effective dialogue format is identified based on past dialogue history. The reporting unit also analyzes the user's dialogue history and automatically selects the most effective dialogue format. For example, the dialogue format is selected based on the user's response and the success rate of the dialogue. The reporting unit also analyzes the user's dialogue history and develops an algorithm for automatically selecting the most effective dialogue format. This makes it possible to analyze the user's dialogue history and automatically select the most effective dialogue format.

[0078] The reporting unit can support conversations on different devices in a chat conversation format. For example, the reporting unit builds a system that supports conversations on different devices in a chat conversation format. For example, it enables conversations on smartphones and smart speakers. The reporting unit also supports conversations on different devices in a chat conversation format. For example, it supports conversations on devices such as smartphones, tablets, and smart speakers. The reporting unit also develops an algorithm for supporting conversations on different devices in a chat conversation format. This makes it possible to support conversations on different devices.

[0079] The reporting unit can interactively report the most relevant news based on the user's past dialogue history. The reporting unit, for example, builds a system that interactively reports the most relevant news based on the user's past dialogue history. For example, the reporting unit analyzes the past dialogue history and selects highly relevant news. The reporting unit also interactively reports the most relevant news based on the user's past dialogue history. For example, the reporting unit selects news based on the past dialogue history. The reporting unit also develops an algorithm for interactively reporting the most relevant news based on the user's past dialogue history. This makes it possible to report the most relevant news based on the past dialogue history.

[0080] The reporting unit can use the emotion estimation function to analyze the user's emotional response in a dialogue format in real time and reflect the result in the next dialogue. The reporting unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response in a dialogue format in real time. For example, it analyzes facial expressions and voice during the dialogue. The reporting unit also uses the emotion estimation function to analyze the user's emotional response in a dialogue format in real time and reflect the result in the next dialogue. For example, the next dialogue is advanced based on a dialogue format in which the user showed positive emotions. The reporting unit also uses the emotion estimation function to develop an algorithm for analyzing the user's emotional response in a dialogue format in real time and reflecting the result in the next dialogue. This allows the user's emotional response in a dialogue format to be analyzed in real time and reflected in the next dialogue.

[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] The news AI system can also provide news that takes into account the user's health status. For example, if the user is feeling stressed, it will prioritize relaxing topics and positive news. If the user is looking for health information, it can provide the latest health news and fitness information. It can also provide advice on appropriate lifestyle habits and health management based on the user's health data.

[0083] The news AI system can utilize the user's geographical location information to provide news specific to the area. For example, it can report weather forecasts, traffic information, and information on local events in the area where the user lives. If the user is traveling, it can provide tourist information and local news for the destination. It can also provide information on local specialties and tourist spots based on the user's location information.

[0084] The news AI system can analyze a user's learning history and provide news and information related to their studies. For example, if a user is studying a specific field, it can report the latest research results and news related to that field. It can also provide information on learning resources and online courses that interest the user. It can also suggest appropriate learning advice and resources based on the user's learning progress.

[0085] The news AI system can estimate the user's emotions and suggest music and entertainment based on the emotions. For example, if the user feels like relaxing, it can suggest relaxing music and movies. If the user feels like cheering up, it can suggest energetic music and action movies. Furthermore, it can also provide appropriate entertainment content according to the user's emotions.

[0086] The news AI system can analyze a user's purchasing history and provide related news and product information. For example, it can report news and reviews related to products the user recently purchased. It can also provide the latest information on products and services that the user is interested in. It can also provide recommendations for related products and services based on the user's purchasing history.

[0087] The news AI system can estimate the user's emotions and provide feedback based on those emotions. For example, if the user expresses positive emotions, it can provide feedback that reinforces those emotions. Also, if the user expresses negative emotions, it can provide feedback that alleviates those emotions. It can also provide appropriate advice and support depending on the user's emotions.

[0088] The news AI system can provide information about related communities and events based on a user's hobbies and interests. For example, if a user has a particular hobby, it can report information about online communities and events related to that hobby. It can also provide information about seminars and workshops in areas that interest the user. It can also encourage participation in appropriate communities and events based on the user's hobbies and interests.

[0089] The news AI system can estimate the user's emotions and provide reminders and notifications based on the emotions. For example, if the user is feeling stressed, it can provide a reminder to relax. Also, if the user shows positive emotions, it can provide a notification to maintain that emotion. Furthermore, it can provide appropriate reminders and notifications depending on the user's emotions.

[0090] The news AI system can analyze a user's reading history and provide information on related books and articles. For example, it can report news and reviews related to books the user has recently read. It can also provide information on the latest books and articles in areas that interest the user. It can also provide recommendations for related books and articles based on the user's reading history.

[0091] The news AI system can estimate a user's emotions and suggest exercise and fitness activities based on their emotions. For example, if a user feels like relaxing, it can suggest yoga or stretching. If a user wants to do energetic exercise, it can suggest running or high-intensity interval training (HIIT). Furthermore, it can suggest appropriate exercise and fitness activities based on the user's emotions.

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

[0093] Step 1: The generation AI generates search terms based on the user's hobbies and lifestyle patterns. For example, the generation AI analyzes the user's past search history, browsing history, and interaction history to understand the user's interests and concerns. The generation AI generates search terms using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to generate search terms that reflect the user's interests and concerns. Step 2: The web crawler collects data based on the search terms generated by the generation AI. For example, the web crawler collects the latest information from news sites, blogs, social media, etc. The web crawler can collect data from specific websites or using APIs. Step 3: The summary generator summarizes the data collected by the web crawler. For example, the summary generator analyzes the collected data and generates a summary to report to the user. The summary generator uses text generation AI to generate the summary. The summary generator can also extract and summarize important parts of the text. Step 4: The reporting unit reports the summary text generated by the summary generation unit in a chat dialogue format. For example, the reporting unit reports the generated summary text to the user in a chat dialogue format. If the user mentions something that they have recently been interested in or concerned about during the dialogue, the reporting unit adds that to the search words and reflects it in the next report.

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

[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. 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.

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

[0122] 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.

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

[0138] 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.

[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. Generative AI and Web crawlers and a summary generator; a reporting unit; The generated AI is Generate search terms based on the user's hobbies and lifestyle patterns, The web crawler Collecting data based on the search words generated by the generation AI; The summary generation unit Summarizing the data collected by the web crawler; The reporting unit The summary generated by the summary generating unit is reported in a chat dialogue format. A system characterized by:

2. The generated AI is Using emotion estimation, the system analyzes changes in emotions from dialogue history and learns changes in hobbies and interests based on emotions.

2. The system of claim 1.

3. The generated AI is When learning about these hobbies and interests, consider the hobbies of family and friends to provide common topics of conversation.

2. The system of claim 1.

4. The web crawler Using an emotion estimation function, the user's emotional response to the collected data is analyzed in real time, and the results are reflected in the next data collection.

2. The system of claim 1.

5. The summary generation unit Using emotion estimation function, elements that elicit positive emotions are incorporated into the summary text.

2. The system of claim 1.

6. The reporting unit Using an emotion estimation function, a dialogue style is selected according to the user's emotion, promoting positive dialogue.

2. The system of claim 1.

7. The generated AI is Analyzing biometric data collected from smart devices to create a detailed profile of lifestyle patterns 2. The system of claim 1.

8. The generated AI is Share data across devices to create a more comprehensive profile 2. The system of claim 1.

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