system

Abstract

The system according to the embodiment aims to efficiently provide news that matches the user's hobbies and preferences. [Solution] A system according to an embodiment includes a collection unit, a learning unit, an editing unit, and a providing unit. The collection unit collects user information. The learning unit learns the user's hobbies and interests based on the information collected by the collection unit. The editing unit edits news based on the information obtained by the learning unit. The providing unit provides the news edited by the editing unit.

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 making it difficult for users to efficiently collect and view news that matches their interests and tastes.

[0005] The system according to the embodiment aims to efficiently provide news that matches the user's hobbies and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, an editing unit, and a providing unit. The collection unit collects user information. The learning unit learns the user's hobbies and interests based on the information collected by the collection unit. The editing unit edits news based on the information obtained by the learning unit. The providing unit provides the news edited by the editing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide news that matches the user's interests and tastes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information collection system according to an embodiment of the present invention uses a compilation tool equipped with a generation AI to collect only the information a user wants and automatically edit it for the desired viewing time. The information collection system allows a user to specify the information they want, and the generation AI learns the user's interests and preferences to create a news program tailored to that user. This news program is automatically edited within the time frame specified by the user. For example, the information collection system allows a user to input topics and keywords of interest. Topics can include sports, technology, and entertainment. This information is then input into the generation AI. The information collection system then uses the generation AI to analyze the user's past viewing history and search history to identify the user's interests. For example, the generation AI identifies the user's interests based on the topics and keywords the user frequently viewed in the past. The generation AI then creates a personalized news program based on the user's interests and preferences. For example, if a user is interested in sports and technology, the system collects news related to these topics to create a program. This news program is automatically edited within the time frame specified by the user. For example, if a user requests a 10-minute news program, the generation AI edits the news to fit within 10 minutes. This allows users to obtain information efficiently. This allows the information gathering system to efficiently collect information tailored to the user's interests and view it within the desired viewing time. For example, a busy businessman can efficiently obtain the latest information by watching a 10-minute news program during his commute. Furthermore, viewing news programs based on user hobbies and preferences increases user satisfaction.

[0029] An information collection system according to an embodiment includes a collection unit, a learning unit, an editing unit, and a providing unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and interests. The collection unit includes, for example, a voice input and text input interface. The voice input converts the user's voice into text using, for example, voice recognition technology. The text input collects the user's text using, for example, keyboard input or handwriting recognition technology. The learning unit uses a generation AI to learn the user's hobbies and interests based on the information collected by the collection unit. The learning is performed using, for example, a machine learning algorithm. The learning unit analyzes viewing history and search history to identify the user's hobbies and interests. For example, the user's hobbies and interests are identified based on topics and keywords that the user has frequently viewed in the past. The editing unit uses the generation AI to edit news based on the information obtained by the learning unit. The editing is performed based on, for example, news selection criteria and editing algorithms. The editing unit includes an algorithm for determining news priorities. For example, the priority is determined based on the importance and relevance of the news. The providing unit provides the news edited by the editing unit. The provision is performed based on, for example, a distribution means and a provision timing. The providing unit has a method for providing the edited news to the user. For example, the news is provided using email distribution or app notifications. This enables the information collection system according to the embodiment to efficiently collect, learn, edit, and provide information for the user.

[0030] The collection unit includes an interface for voice input or text input. Voice input involves converting a user's voice into text using, for example, voice recognition technology. For example, the collection unit collects a user's voice using a microphone and converts it into text data using voice recognition technology. Text input involves collecting a user's text using, for example, keyboard input or handwriting recognition technology. For example, the collection unit collects text entered by a user using a keyboard. The collection unit can also convert text entered by handwriting by a user into digital data using handwriting recognition technology. This allows a user to enter information by voice or text.

[0031] The learning unit can analyze the viewing history or search history to identify the user's hobbies and interests. The viewing history includes information such as the titles of videos viewed by the user and the viewing time. For example, the learning unit analyzes the titles and viewing time of videos viewed by the user in the past to identify the user's interests. The search history includes information such as the keywords searched by the user and the search date and time. For example, the learning unit analyzes the keywords searched by the user in the past and the search date and time to identify the user's interests. This allows the learning unit to accurately grasp the user's hobbies and interests. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the viewing history or search history into the generation AI and have the generation AI identify the user's hobbies and interests.

[0032] The editorial department may be equipped with an algorithm for determining the priority of news. The algorithm for determining the priority determines the priority based on, for example, the importance and relevance of the news. For example, the editorial department scores the importance of news and prioritizes editing news with high scores. The editorial department can also evaluate the relevance of news and prioritize editing news with high relevance. Furthermore, the editorial department can determine the priority based on the urgency of news. For example, news with high urgency is prioritized. This allows the editorial department to appropriately determine the priority of news. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input the importance and relevance of news into the generation AI and have the generation AI determine the priority.

[0033] The providing unit may include a method for providing the edited news to the user. The providing method may provide the news by email or app notification, for example. For example, the providing unit may send the edited news to the user by email. The providing unit may also provide the news to the user by app notification. The providing unit may also provide the news through a website or social media. For example, the providing unit may post the edited news on a website so that the user can access it. The providing unit may also share the news through social media. This allows the providing unit to appropriately provide the edited news to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the edited news to a generation AI and cause the generation AI to select a delivery method.

[0034] The collection unit can analyze the user's past information collection history and select a collection method. For example, the collection unit prioritizes collection of information sources that the user has frequently used in the past. The collection unit can also collect related information based on topics that the user has frequently viewed in the past. Furthermore, the collection unit can analyze the user's past information collection patterns and collect information at the optimal timing. This allows the collection unit to select the optimal collection method based on the user's past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history into the generation AI and have the generation AI select the collection method.

[0035] When collecting information, the collection unit can filter the information based on the user's current areas of interest and lifestyle. For example, the collection unit preferentially collects information related to topics in which the user is currently interested. The collection unit can also filter appropriate information according to the user's lifestyle (e.g., at work, on vacation, etc.). Furthermore, the collection unit can collect relevant information based on the user's current activity (e.g., exercising, reading, etc.). This allows the collection unit to filter appropriate information based on the user's current areas of interest and lifestyle. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's current areas of interest and lifestyle to the generation AI and have the generation AI perform the filtering.

[0036] When collecting information, the collection unit can select a collection means according to the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. This allows the collection unit to select the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input method to the generation AI and have the generation AI select the collection means.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting news and event information for the area where the user is currently located. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information for the travel destination and local news. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. This allows the collection unit to prioritize collecting highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0038] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect related information based on the content posted by accounts the user follows on social media. The collection unit can also analyze the content posted by the user on social media and collect information that may be of interest to the user. Furthermore, the collection unit can also refer to the activities of the user's friends on social media. This allows the collection unit to collect related information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related information.

[0039] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information sources that the user has previously rated highly. The collection unit can also collect information by excluding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. This allows the collection unit to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] During learning, the learning unit can optimize the learning algorithm by referring to past viewing history and search history. The learning unit optimizes the learning algorithm, for example, based on topics that the user has frequently viewed in the past. The learning unit can also analyze the user's past search history and reflect related topics in the learning algorithm. Furthermore, the learning unit can integrate the user's viewing history and search history to construct an optimal learning algorithm. This allows the learning unit to optimize the learning algorithm based on the user's past viewing history and search history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past viewing history and search history into the generation AI and cause the generation AI to optimize the learning algorithm.

[0041] The learning unit can update the learning data to reflect user feedback during learning. For example, the learning unit can add content that the user has given a high rating to the learning data. The learning unit can also exclude content that the user has given a low rating from the learning data. Furthermore, the learning unit can periodically update the learning data based on user feedback. This allows the learning unit to update the learning data based on user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback to a generation AI and cause the generation AI to update the learning data.

[0042] The learning unit can analyze changes in the user's interests during learning and adjust the update frequency of the learning data. For example, if the user's interests change suddenly, the learning unit can quickly update the learning data. The learning unit can also reduce the update frequency of the learning data if the user's interests are stable. Furthermore, the learning unit can periodically analyze changes in the user's interests and set an optimal update frequency. This allows the learning unit to adjust the update frequency of the learning data based on changes in the user's interests. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input data on changes in the user's interests to the generation AI and have the generation AI adjust the update frequency.

[0043] During learning, the learning unit can integrate information from different data sources to enrich the learning data. The learning unit collects information from different data sources, such as news sites, blogs, and social media, to enrich the learning data. The learning unit can also integrate information from different data sources to construct comprehensive learning data. Furthermore, the learning unit can analyze information from different data sources and select optimal learning data. This allows the learning unit to enrich the learning data by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into a generation AI and have the generation AI integrate the information.

[0044] The learning unit can adjust the learning algorithm based on the user's lifestyle during learning. For example, if the user consumes a lot of information in the morning, the learning unit can strengthen the learning algorithm in the morning. Also, if the user consumes a lot of information in the evening, the learning unit can strengthen the learning algorithm in the evening. Furthermore, the learning unit can dynamically adjust the parameters of the learning algorithm according to the user's lifestyle. This allows the learning unit to adjust the learning algorithm based on the user's lifestyle. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the user's lifestyle data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0045] During learning, the learning unit can customize the learning content according to the user's level of expertise. For example, the learning unit prioritizes learning information related to fields in which the user has expertise. The learning unit can also learn basic information related to fields in which the user is a beginner. Furthermore, the learning unit can dynamically customize the learning content according to the user's level of expertise. This allows the learning unit to customize the learning content according to the user's level of expertise. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's level of expertise into the generation AI and cause the generation AI to customize the learning content.

[0046] During editing, the editorial department can adjust the level of detail of the editing based on the importance of the news. For example, the editorial department edits important news to include detailed information. The editorial department can also edit general news in a concise manner that focuses on the main points. Furthermore, the editorial department can dynamically adjust the level of detail of the editing depending on the importance of the news. This allows the editorial department to adjust the level of detail of the editing based on the importance of the news. Some or all of the above-mentioned processing in the editorial department may be performed using AI, for example, or may be performed without using AI. For example, the editorial department can input news importance data into the generation AI and have the generation AI adjust the level of detail of the editing.

[0047] During editing, the editorial department can apply different editing algorithms depending on the news category. For example, the editorial department can apply an editing algorithm that emphasizes game results and highlights to sports news. The editorial department can also apply an editing algorithm that includes new product introductions and technical explanations to technology news. Furthermore, the editorial department can apply an editing algorithm that includes interviews and behind-the-scenes information to entertainment news. This allows the editorial department to apply an appropriate editing algorithm depending on the news category. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input news category data into the generation AI and have the generation AI apply the editing algorithm.

[0048] When editing, the editorial department can improve the accuracy of editing by referring to the user's past editing results. For example, the editorial department refers to an editing style that the user has previously rated highly and edits the news in a similar style. The editorial department can also edit the news in a different style, avoiding editing styles that the user has previously rated poorly. Furthermore, the editorial department can analyze the user's past editing results and select the optimal editing method. In this way, the editorial department can improve the accuracy of editing by referring to the user's past editing results. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input the user's past editing result data into the generation AI and have the generation AI improve the accuracy of editing.

[0049] During editing, the editorial department can determine the priority of editing based on the time when the news was submitted. For example, the editorial department can prioritize editing the latest news and provide it quickly. The editorial department can also perform concise editing to focus on the main points for older submitted news. Furthermore, the editorial department can dynamically adjust the priority of editing depending on the time when the news was submitted. This allows the editorial department to determine the priority of editing based on the time when the news was submitted. Some or all of the above-mentioned processing in the editorial department may be performed using AI, for example, or may be performed without using AI. For example, the editorial department can input data on the time when the news was submitted into the generation AI and have the generation AI determine the priority of editing.

[0050] The editorial department can adjust the editing order based on the relevance of the news when editing. For example, the editorial department can prioritize editing highly relevant news and provide it to viewers. The editorial department can also perform concise editing that focuses on the main points for less relevant news. Furthermore, the editorial department can dynamically adjust the editing order according to the relevance of the news. This allows the editorial department to adjust the editing order based on the relevance of the news. Some or all of the above-mentioned processing in the editorial department may be performed using AI, for example, or may be performed without using AI. For example, the editorial department can input news relevance data into a generation AI and have the generation AI adjust the editing order.

[0051] During editing, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. For example, if the user has technical expertise, the editing department may perform editing that uses a lot of technical terminology. Furthermore, if the user is a beginner, the editing department may perform editing that avoids technical terminology and makes it easier to understand. Furthermore, the editing department can dynamically adjust the use of technical terminology in the editing depending on the user's level of expertise. This allows the editing department to adjust the use of technical terminology in the editing depending on the user's level of expertise. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department may input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0052] The providing unit can select the optimal delivery method by referring to the user's past viewing history when providing news. For example, the providing unit provides related news based on topics that the user has viewed frequently in the past. The providing unit can also analyze the user's past viewing history and select the optimal delivery method. Furthermore, the providing unit can integrate the user's viewing history and search history to construct the optimal delivery method. This allows the providing unit to select the optimal delivery method based on the user's past viewing history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past viewing history into the generation AI and have the generation AI select the delivery method.

[0053] The providing unit can customize the provided content according to the user's current task at the time of providing. For example, when the user is at work, the providing unit can provide work-related news. Furthermore, when the user is on vacation, the providing unit can provide relaxing content. Furthermore, the providing unit can dynamically customize the provided content according to the user's current task. This allows the providing unit to customize the provided content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generating AI and cause the generating AI to customize the provided content.

[0054] The providing unit can improve the providing method by reflecting user feedback when providing the content. For example, the providing unit preferentially uses a providing method that the user has given a high rating. The providing unit can also try a different method, avoiding a providing method that the user has given a low rating. Furthermore, the providing unit can periodically improve the providing method based on user feedback. This allows the providing unit to improve the providing method based on user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into a generation AI and cause the generation AI to improve the providing method.

[0055] The providing unit can select the optimal providing method by taking into consideration the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the providing unit to select the optimal providing method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the providing method.

[0056] The providing unit can make the provided content multilingual based on the user's language setting when providing the content. The providing unit, for example, automatically sets the language of the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the provided content in that language. This allows the providing unit to make the provided content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to execute multilingual settings.

[0057] The providing unit can provide highly relevant news by taking into account the user's geographical location information when providing the news. For example, the providing unit can prioritize providing news and event information for the area where the user is currently located. Furthermore, if the user is traveling, the providing unit can also prioritize providing tourist information for the travel destination and local news. Furthermore, if the user is in a specific location, the providing unit can also provide news related to that location. This allows the providing unit to provide highly relevant news based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to provide highly relevant news.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can also analyze the user's social media activities to collect related information. For example, it can collect related news based on the content posted by accounts the user follows on social media. It can also analyze the content posted by the user on social media to collect information that may be of interest to the user. It can also collect related information based on the activities of the user's friends on social media. This allows the collection unit to collect related information based on the user's social media activities.

[0060] The editorial department can also apply different editing algorithms depending on the news category. For example, for sports news, an editing algorithm that emphasizes game results and highlights can be applied. For technology news, an editing algorithm that includes new product introductions and technical explanations can be applied. For entertainment news, an editing algorithm that includes interviews and behind-the-scenes information can be applied. This allows the editorial department to apply the appropriate editing algorithm depending on the news category.

[0061] The collection unit can also prioritize collecting highly relevant information based on the user's geographical location information. For example, it can prioritize collecting news and event information for the area where the user is currently located. Also, if the user is traveling, it can prioritize collecting tourist information for the travel destination and local news. Furthermore, if the user is in a specific location, it can prioritize collecting information related to that location. This allows the collection unit to prioritize collecting highly relevant information based on the user's geographical location information.

[0062] The learning unit can also customize the learning content according to the user's level of expertise. For example, the learning unit can prioritize learning information in fields in which the user has expertise. The learning unit can also learn basic information in fields in which the user is a beginner. Furthermore, the learning content can be dynamically customized according to the user's level of expertise. This allows the learning unit to customize the learning content according to the user's level of expertise.

[0063] The providing unit can also select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This allows the providing unit to select the optimal providing method based on the user's device information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects user information, including personal information, behavioral history, and interests. The collection unit is equipped with interfaces for voice and text input, and collects user information using voice recognition technology, keyboard input, and handwriting recognition technology. Step 2: The learning unit uses the generation AI to learn the user's hobbies and preferences based on the information collected by the collection unit. Learning is performed using a machine learning algorithm, analyzing viewing history and search history to identify the user's hobbies and preferences. Step 3: The editorial department uses the generative AI to edit the news based on the information obtained by the learning department. Editing is based on news selection criteria and editing algorithms, and prioritizes news based on its importance and relevance. Step 4: The news provider provides the news edited by the editorial department. The news is provided based on the delivery method and timing, and is provided via email or app notifications.

[0066] (Example 2) An information collection system according to an embodiment of the present invention uses a compilation tool equipped with a generation AI to collect only the information a user wants and automatically edit it for the desired viewing time. The information collection system allows a user to specify the information they want, and the generation AI learns the user's interests and preferences to create a news program tailored to that user. This news program is automatically edited within the time frame specified by the user. For example, the information collection system allows a user to input topics and keywords of interest. Topics can include sports, technology, and entertainment. This information is then input into the generation AI. The information collection system then uses the generation AI to analyze the user's past viewing history and search history to identify the user's interests. For example, the generation AI identifies the user's interests based on the topics and keywords the user frequently viewed in the past. The generation AI then creates a personalized news program based on the user's interests and preferences. For example, if a user is interested in sports and technology, the system collects news related to these topics to create a program. This news program is automatically edited within the time frame specified by the user. For example, if a user requests a 10-minute news program, the generation AI edits the news to fit within 10 minutes. This allows users to obtain information efficiently. This allows the information gathering system to efficiently collect information tailored to the user's interests and view it within the desired viewing time. For example, a busy businessman can efficiently obtain the latest information by watching a 10-minute news program during his commute. Furthermore, viewing news programs based on user hobbies and preferences increases user satisfaction.

[0067] An information collection system according to an embodiment includes a collection unit, a learning unit, an editing unit, and a providing unit. The collection unit collects user information. The user information includes, but is not limited to, personal information, behavioral history, and interests. The collection unit includes, for example, a voice input and text input interface. The voice input converts the user's voice into text using, for example, voice recognition technology. The text input collects the user's text using, for example, keyboard input or handwriting recognition technology. The learning unit uses a generation AI to learn the user's hobbies and interests based on the information collected by the collection unit. The learning is performed using, for example, a machine learning algorithm. The learning unit analyzes viewing history and search history to identify the user's hobbies and interests. For example, the user's hobbies and interests are identified based on topics and keywords that the user has frequently viewed in the past. The editing unit uses the generation AI to edit news based on the information obtained by the learning unit. The editing is performed based on, for example, news selection criteria and editing algorithms. The editing unit includes an algorithm for determining news priorities. For example, the priority is determined based on the importance and relevance of the news. The providing unit provides the news edited by the editing unit. The provision is performed based on, for example, a distribution means and a provision timing. The providing unit has a method for providing the edited news to the user. For example, the news is provided using email distribution or app notifications. This enables the information collection system according to the embodiment to efficiently collect, learn, edit, and provide information for the user.

[0068] The collection unit includes an interface for voice input or text input. Voice input involves converting a user's voice into text using, for example, voice recognition technology. For example, the collection unit collects a user's voice using a microphone and converts it into text data using voice recognition technology. Text input involves collecting a user's text using, for example, keyboard input or handwriting recognition technology. For example, the collection unit collects text entered by a user using a keyboard. The collection unit can also convert text entered by handwriting by a user into digital data using handwriting recognition technology. This allows a user to enter information by voice or text.

[0069] The learning unit can analyze the viewing history or search history to identify the user's hobbies and interests. The viewing history includes information such as the titles of videos viewed by the user and the viewing time. For example, the learning unit analyzes the titles and viewing time of videos viewed by the user in the past to identify the user's interests. The search history includes information such as the keywords searched by the user and the search date and time. For example, the learning unit analyzes the keywords searched by the user in the past and the search date and time to identify the user's interests. This allows the learning unit to accurately grasp the user's hobbies and interests. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the viewing history or search history into the generation AI and have the generation AI identify the user's hobbies and interests.

[0070] The editorial department may be equipped with an algorithm for determining the priority of news. The algorithm for determining the priority determines the priority based on, for example, the importance and relevance of the news. For example, the editorial department scores the importance of news and prioritizes editing news with high scores. The editorial department can also evaluate the relevance of news and prioritize editing news with high relevance. Furthermore, the editorial department can determine the priority based on the urgency of news. For example, news with high urgency is prioritized. This allows the editorial department to appropriately determine the priority of news. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input the importance and relevance of news into the generation AI and have the generation AI determine the priority.

[0071] The providing unit may include a method for providing the edited news to the user. The providing method may provide the news by email or app notification, for example. For example, the providing unit may send the edited news to the user by email. The providing unit may also provide the news to the user by app notification. The providing unit may also provide the news through a website or social media. For example, the providing unit may post the edited news on a website so that the user can access it. The providing unit may also share the news through social media. This allows the providing unit to appropriately provide the edited news to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the edited news to a generation AI and cause the generation AI to select a delivery method.

[0072] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit can instantly collect information and provide the latest information in real time. Furthermore, if the user is feeling stressed, the collection unit can temporarily delay information collection and provide the information when the user is calm. Furthermore, if the user is busy, the collection unit can collect information in the background so that the user can check the information at their own convenience. This allows the collection unit to adjust the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0073] The collection unit can analyze the user's past information collection history and select a collection method. For example, the collection unit prioritizes collection of information sources that the user has frequently used in the past. The collection unit can also collect related information based on topics that the user has frequently viewed in the past. Furthermore, the collection unit can analyze the user's past information collection patterns and collect information at the optimal timing. This allows the collection unit to select the optimal collection method based on the user's past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history into the generation AI and have the generation AI select the collection method.

[0074] When collecting information, the collection unit can filter the information based on the user's current areas of interest and lifestyle. For example, the collection unit preferentially collects information related to topics in which the user is currently interested. The collection unit can also filter appropriate information according to the user's lifestyle (e.g., at work, on vacation, etc.). Furthermore, the collection unit can collect relevant information based on the user's current activity (e.g., exercising, reading, etc.). This allows the collection unit to filter appropriate information based on the user's current areas of interest and lifestyle. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's current areas of interest and lifestyle to the generation AI and have the generation AI perform the filtering.

[0075] When collecting information, the collection unit can select a collection means according to the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. This allows the collection unit to select the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input method to the generation AI and have the generation AI select the collection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit can prioritize collecting the latest news and trending information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting interesting articles and entertainment information. Furthermore, when the user is stressed, the collection unit can prioritize collecting relaxing content and information that helps to change moods. This allows the collection unit to prioritize information based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0077] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting news and event information for the area where the user is currently located. Furthermore, when the user is traveling, the collection unit can prioritize collecting tourist information for the travel destination and local news. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. This allows the collection unit to prioritize collecting highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0078] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect related information based on the content posted by accounts the user follows on social media. The collection unit can also analyze the content posted by the user on social media and collect information that may be of interest to the user. Furthermore, the collection unit can also refer to the activities of the user's friends on social media. This allows the collection unit to collect related information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related information.

[0079] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information sources that the user has previously rated highly. The collection unit can also collect information by excluding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. This allows the collection unit to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0080] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select relaxing content as training data. Furthermore, if the user is excited, the learning unit can select stimulating content as training data. Furthermore, if the user is stressed, the learning unit can select content that helps relieve stress as training data. This allows the learning unit to select training data based on the user's emotions, enabling more appropriate learning. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0081] During learning, the learning unit can optimize the learning algorithm by referring to past viewing history and search history. The learning unit optimizes the learning algorithm, for example, based on topics that the user has frequently viewed in the past. The learning unit can also analyze the user's past search history and reflect related topics in the learning algorithm. Furthermore, the learning unit can integrate the user's viewing history and search history to construct an optimal learning algorithm. This allows the learning unit to optimize the learning algorithm based on the user's past viewing history and search history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past viewing history and search history into the generation AI and cause the generation AI to optimize the learning algorithm.

[0082] The learning unit can update the learning data to reflect user feedback during learning. For example, the learning unit can add content that the user has given a high rating to the learning data. The learning unit can also exclude content that the user has given a low rating from the learning data. Furthermore, the learning unit can periodically update the learning data based on user feedback. This allows the learning unit to update the learning data based on user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback to a generation AI and cause the generation AI to update the learning data.

[0083] The learning unit can analyze changes in the user's interests during learning and adjust the update frequency of the learning data. For example, if the user's interests change suddenly, the learning unit can quickly update the learning data. The learning unit can also reduce the update frequency of the learning data if the user's interests are stable. Furthermore, the learning unit can periodically analyze changes in the user's interests and set an optimal update frequency. This allows the learning unit to adjust the update frequency of the learning data based on changes in the user's interests. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input data on changes in the user's interests to the generation AI and have the generation AI adjust the update frequency.

[0084] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, when the user is relaxed, the learning unit increases the frequency of learning to actively absorb new information. Furthermore, when the user is stressed, the learning unit can reduce the frequency of learning to avoid information overload. Furthermore, the learning unit can dynamically adjust the frequency of learning according to the user's emotional state. This allows the learning unit to adjust the frequency of learning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of learning.

[0085] During learning, the learning unit can integrate information from different data sources to enrich the learning data. The learning unit collects information from different data sources, such as news sites, blogs, and social media, to enrich the learning data. The learning unit can also integrate information from different data sources to construct comprehensive learning data. Furthermore, the learning unit can analyze information from different data sources and select optimal learning data. This allows the learning unit to enrich the learning data by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into a generation AI and have the generation AI integrate the information.

[0086] The learning unit can adjust the learning algorithm based on the user's lifestyle during learning. For example, if the user consumes a lot of information in the morning, the learning unit can strengthen the learning algorithm in the morning. Also, if the user consumes a lot of information in the evening, the learning unit can strengthen the learning algorithm in the evening. Furthermore, the learning unit can dynamically adjust the parameters of the learning algorithm according to the user's lifestyle. This allows the learning unit to adjust the learning algorithm based on the user's lifestyle. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the user's lifestyle data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0087] During learning, the learning unit can customize the learning content according to the user's level of expertise. For example, the learning unit prioritizes learning information related to fields in which the user has expertise. The learning unit can also learn basic information related to fields in which the user is a beginner. Furthermore, the learning unit can dynamically customize the learning content according to the user's level of expertise. This allows the learning unit to customize the learning content according to the user's level of expertise. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's level of expertise into the generation AI and cause the generation AI to customize the learning content.

[0088] The editorial department can estimate the user's emotions and adjust the news editing method based on the estimated user emotions. For example, if the user is relaxed, the editorial department edits news with detailed information. Furthermore, if the user is in a hurry, the editorial department can edit short news that focuses on the main points. Furthermore, if the user is excited, the editorial department can edit news with visually stimulating effects. This allows the editorial department to adjust the news editing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editorial department may be performed using, for example, an AI, or may be performed without using an AI. For example, the editorial department can input the user's emotion data into the generation AI and have the generation AI adjust the editing method.

[0089] During editing, the editorial department can adjust the level of detail of the editing based on the importance of the news. For example, the editorial department edits important news to include detailed information. The editorial department can also edit general news in a concise manner that focuses on the main points. Furthermore, the editorial department can dynamically adjust the level of detail of the editing depending on the importance of the news. This allows the editorial department to adjust the level of detail of the editing based on the importance of the news. Some or all of the above-mentioned processing in the editorial department may be performed using AI, for example, or may be performed without using AI. For example, the editorial department can input news importance data into the generation AI and have the generation AI adjust the level of detail of the editing.

[0090] During editing, the editorial department can apply different editing algorithms depending on the news category. For example, the editorial department can apply an editing algorithm that emphasizes game results and highlights to sports news. The editorial department can also apply an editing algorithm that includes new product introductions and technical explanations to technology news. Furthermore, the editorial department can apply an editing algorithm that includes interviews and behind-the-scenes information to entertainment news. This allows the editorial department to apply an appropriate editing algorithm depending on the news category. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input news category data into the generation AI and have the generation AI apply the editing algorithm.

[0091] When editing, the editorial department can improve the accuracy of editing by referring to the user's past editing results. For example, the editorial department refers to an editing style that the user has previously rated highly and edits the news in a similar style. The editorial department can also edit the news in a different style, avoiding editing styles that the user has previously rated poorly. Furthermore, the editorial department can analyze the user's past editing results and select the optimal editing method. In this way, the editorial department can improve the accuracy of editing by referring to the user's past editing results. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input the user's past editing result data into the generation AI and have the generation AI improve the accuracy of editing.

[0092] The editorial department can estimate the user's emotions and adjust the length of the news based on the estimated user emotions. For example, if the user is relaxed, the editorial department edits longer news with more detailed information. If the user is in a hurry, the editorial department can also edit shorter news that focuses on the main points. Furthermore, if the user is excited, the editorial department can edit news with visually stimulating effects. This allows the editorial department to adjust the length of the news based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editorial department can be performed using AI, for example, or without AI. For example, the editorial department can input the user's emotion data into the generation AI and have the generation AI adjust the length of the news.

[0093] During editing, the editorial department can determine the priority of editing based on the time when the news was submitted. For example, the editorial department can prioritize editing the latest news and provide it quickly. The editorial department can also perform concise editing to focus on the main points for older submitted news. Furthermore, the editorial department can dynamically adjust the priority of editing depending on the time when the news was submitted. This allows the editorial department to determine the priority of editing based on the time when the news was submitted. Some or all of the above-mentioned processing in the editorial department may be performed using AI, for example, or may be performed without using AI. For example, the editorial department can input data on the time when the news was submitted into the generation AI and have the generation AI determine the priority of editing.

[0094] The editorial department can adjust the editing order based on the relevance of the news when editing. For example, the editorial department can prioritize editing highly relevant news and provide it to viewers. The editorial department can also perform concise editing that focuses on the main points for less relevant news. Furthermore, the editorial department can dynamically adjust the editing order according to the relevance of the news. This allows the editorial department to adjust the editing order based on the relevance of the news. Some or all of the above-mentioned processing in the editorial department may be performed using AI, for example, or may be performed without using AI. For example, the editorial department can input news relevance data into a generation AI and have the generation AI adjust the editing order.

[0095] During editing, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. For example, if the user has technical expertise, the editing department may perform editing that uses a lot of technical terminology. Furthermore, if the user is a beginner, the editing department may perform editing that avoids technical terminology and makes it easier to understand. Furthermore, the editing department can dynamically adjust the use of technical terminology in the editing depending on the user's level of expertise. This allows the editing department to adjust the use of technical terminology in the editing depending on the user's level of expertise. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department may input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0096] The providing unit can estimate the user's emotions and adjust the news delivery method based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide news with detailed information. Furthermore, if the user is in a hurry, the providing unit can provide short news that covers the main points. Furthermore, if the user is excited, the providing unit can provide news with visually stimulating effects. This allows the providing unit to adjust the news delivery method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the delivery method.

[0097] The providing unit can select the optimal delivery method by referring to the user's past viewing history when providing news. For example, the providing unit provides related news based on topics that the user has viewed frequently in the past. The providing unit can also analyze the user's past viewing history and select the optimal delivery method. Furthermore, the providing unit can integrate the user's viewing history and search history to construct the optimal delivery method. This allows the providing unit to select the optimal delivery method based on the user's past viewing history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past viewing history into the generation AI and have the generation AI select the delivery method.

[0098] The providing unit can customize the provided content according to the user's current task at the time of providing. For example, when the user is at work, the providing unit can provide work-related news. Furthermore, when the user is on vacation, the providing unit can provide relaxing content. Furthermore, the providing unit can dynamically customize the provided content according to the user's current task. This allows the providing unit to customize the provided content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generating AI and cause the generating AI to customize the provided content.

[0099] The providing unit can improve the providing method by reflecting user feedback when providing the content. For example, the providing unit preferentially uses a providing method that the user has given a high rating. The providing unit can also try a different method, avoiding a providing method that the user has given a low rating. Furthermore, the providing unit can periodically improve the providing method based on user feedback. This allows the providing unit to improve the providing method based on user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into a generation AI and cause the generation AI to improve the providing method.

[0100] The providing unit can estimate the user's emotions and adjust the order in which news is provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing news containing detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing short news that covers the main points. Furthermore, if the user is excited, the providing unit can prioritize providing news with visually stimulating effects. This allows the providing unit to adjust the order in which news is provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the news provision order.

[0101] The providing unit can select the optimal providing method by taking into consideration the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the providing unit to select the optimal providing method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the providing method.

[0102] The providing unit can make the provided content multilingual based on the user's language setting when providing the content. The providing unit, for example, automatically sets the language of the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the provided content in that language. This allows the providing unit to make the provided content multilingual based on the user's language setting. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to execute multilingual settings.

[0103] The providing unit can provide highly relevant news by taking into account the user's geographical location information when providing the news. For example, the providing unit can prioritize providing news and event information for the area where the user is currently located. Furthermore, if the user is traveling, the providing unit can also prioritize providing tourist information for the travel destination and local news. Furthermore, if the user is in a specific location, the providing unit can also provide news related to that location. This allows the providing unit to provide highly relevant news based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to provide highly relevant news. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, editing unit, and providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a user's voice and text using the microphone 38B or keyboard of the smart device 14, and converts the collected voice and text into text data using voice recognition technology by the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's hobbies and preferences based on the collected information. The editing unit is realized by the specific processing unit 290 of the data processing device 12 and edits news based on the information obtained by the learning unit. The providing unit is realized by the control unit 46A of the smart device 14 and provides the edited news to the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, learning unit, editing unit, and providing unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's voice and text using the microphone 238 and keyboard of the smart glasses 214, and converts the collected voice and text into text data using voice recognition technology by the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's hobbies and preferences based on the collected information. The editing unit is realized by the specific processing unit 290 of the data processing device 12 and edits news based on the information obtained by the learning unit. The providing unit is realized by the control unit 46A of the smart glasses 214 and provides the edited news to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, editing unit, and providing unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's voice and text using the microphone 238 and keyboard of the headset-type terminal 314, and converts them into text data using voice recognition technology by the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's hobbies and preferences based on the collected information. The editing unit is realized by the specific processing unit 290 of the data processing device 12 and edits news based on the information obtained by the learning unit. The providing unit is realized by the control unit 46A of the headset-type terminal 314 and provides the edited news to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, editing unit, and providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's voice and text using the microphone 238 and keyboard of the robot 414, and converts the collected voice and text into text data using voice recognition technology by the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's hobbies and preferences based on the collected information. The editing unit is realized by the specific processing unit 290 of the data processing device 12 and edits news based on the information obtained by the learning unit. The providing unit is realized by the control unit 46A of the robot 414 and provides the edited news to the user.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The information collection system can also collect the user's health data and customize the news content based on the user's health condition. For example, if the user is exercising, exercise-related news and health information can be provided as a priority. If the user is resting, content that helps the user relax and information that helps reduce stress can be provided. Furthermore, if the user is sick or injured, medical information tailored to the user's condition and advice to help with recovery can be provided. This allows the information collection system to provide the most appropriate information according to the user's health condition.

[0106] The collection unit can also analyze the user's social media activities to collect related information. For example, it can collect related news based on the content posted by accounts the user follows on social media. It can also analyze the content posted by the user on social media to collect information that may be of interest to the user. It can also collect related information based on the activities of the user's friends on social media. This allows the collection unit to collect related information based on the user's social media activities.

[0107] The learning unit can also estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, relaxing content can be selected as learning data. If the user is excited, stimulating content can be selected as learning data. Furthermore, if the user is stressed, content that helps to reduce stress can be selected as learning data. In this way, the learning unit can select learning data based on the user's emotions, enabling more appropriate learning.

[0108] The editorial department can also apply different editing algorithms depending on the news category. For example, for sports news, an editing algorithm that emphasizes game results and highlights can be applied. For technology news, an editing algorithm that includes new product introductions and technical explanations can be applied. For entertainment news, an editing algorithm that includes interviews and behind-the-scenes information can be applied. This allows the editorial department to apply the appropriate editing algorithm depending on the news category.

[0109] The providing unit can further estimate the user's emotions and adjust the way in which news is provided based on the estimated user's emotions. For example, if the user is relaxed, news containing detailed information can be provided. If the user is in a hurry, short news that covers the main points can be provided. Furthermore, if the user is excited, news with visually stimulating effects can be provided. This allows the providing unit to adjust the way in which news is provided based on the user's emotions.

[0110] The collection unit can also prioritize collecting highly relevant information based on the user's geographical location information. For example, it can prioritize collecting news and event information for the area where the user is currently located. Also, if the user is traveling, it can prioritize collecting tourist information for the travel destination and local news. Furthermore, if the user is in a specific location, it can prioritize collecting information related to that location. This allows the collection unit to prioritize collecting highly relevant information based on the user's geographical location information.

[0111] The learning unit can also customize the learning content according to the user's level of expertise. For example, the learning unit can prioritize learning information in fields in which the user has expertise. The learning unit can also learn basic information in fields in which the user is a beginner. Furthermore, the learning content can be dynamically customized according to the user's level of expertise. This allows the learning unit to customize the learning content according to the user's level of expertise.

[0112] The editorial department can further estimate the user's emotions and adjust the way the news is edited based on the estimated user's emotions. For example, if the user is relaxed, the editorial department can edit news that includes detailed information. If the user is in a hurry, the editorial department can edit short news that covers the main points. Furthermore, if the user is excited, the editorial department can edit news that adds visually stimulating effects. This allows the editorial department to adjust the way the news is edited based on the user's emotions.

[0113] The providing unit can also select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. This allows the providing unit to select the optimal providing method based on the user's device information.

[0114] The providing unit can further estimate the user's emotions and adjust the order in which news is provided based on the estimated user's emotions. For example, if the user is relaxed, news containing detailed information can be provided preferentially. If the user is in a hurry, short news that covers the main points can be provided preferentially. Furthermore, if the user is excited, news with visually stimulating effects can be provided preferentially. In this way, the providing unit can adjust the order in which news is provided based on the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects user information, including personal information, behavioral history, and interests. The collection unit is equipped with interfaces for voice and text input, and collects user information using voice recognition technology, keyboard input, and handwriting recognition technology. Step 2: The learning unit uses the generation AI to learn the user's hobbies and preferences based on the information collected by the collection unit. Learning is performed using a machine learning algorithm, analyzing viewing history and search history to identify the user's hobbies and preferences. Step 3: The editorial department uses the generative AI to edit the news based on the information obtained by the learning department. Editing is based on news selection criteria and editing algorithms, and prioritizes news based on its importance and relevance. Step 4: The news provider provides the news edited by the editorial department. The news is provided based on the delivery method and timing, and is provided via email or app notifications.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; a learning unit that learns the user's hobbies and preferences based on the information collected by the collecting unit; an editing unit that edits news based on the information obtained by the learning unit; a providing unit that provides the news edited by the editing unit. A system characterized by:

2. The collecting unit Provides a voice or text input interface 2. The system of claim 1.

3. The learning unit Analyzing viewing or search history to identify user interests and preferences 2. The system of claim 1.

4. The editorial department It has an algorithm that prioritizes news 2. The system of claim 1.

5. The providing unit Provide a way to provide edited news to users 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past information collection history and select the collection method 2. The system of claim 1.

8. The collecting unit When collecting information, filter it based on the user's current interests and life situation.

2. The system of claim 1.

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