system

Abstract

The system according to this embodiment aims to provide customized information based on the user's interests and personal information. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a narration unit, and a personal information collection unit. The collection unit collects information of interest to the user. The generation unit analyzes the information collected by the collection unit and generates a radio program script. The narration unit reads aloud the script generated by the generation unit. The personal information collection unit collects the user's personal information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, customized information provision based on the user's interests and personal information has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized information based on the user's interests and personal information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a narration unit, and a personal information collection unit. The collection unit collects information of interest to the user. The generation unit analyzes the information collected by the collection unit and generates a radio program script. The narration unit reads aloud the script generated by the generation unit. The personal information collection unit collects the user's personal information. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized information based on the user's interests and personal information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An information provision system according to an embodiment of the present invention is a mechanism that utilizes a generating AI to combine a user's personal information with content from a news provision service and provide "a personalized morning information program" in radio format every morning. This information provision system collects information of interest to the user, such as the latest news, weather, today's schedule, and recent notices, and the generating AI automatically generates a radio program script based on this information, which is then read aloud by AI narration. This radio program is provided in a way that the user can listen to while lying in bed with their eyes closed in the morning, or while eating breakfast or working during their morning routine. First, the information provision system collects information of interest to the user. In this process, it collects news and weather information from the news provision service, as well as personal information such as the user's schedule and notices. For example, it collects information such as news categories of interest to the user, today's weather, and scheduled meetings and events. Next, the information provision system uses a generating AI to automatically generate a radio program script based on the collected information. The generating AI analyzes the collected information, selects information that is important to the user, and creates a script in the format of a radio program. For example, it generates a script that introduces the latest news first, followed by weather information, schedule, and notices in that order. Based on the generated script, the information delivery system uses AI narration to read radio programs. The AI ​​narration delivers information in a natural voice, conveying it in a way that users can easily listen to. For example, it might read the latest news, then provide today's weather, and then introduce schedules and announcements. This mechanism allows the information delivery system to efficiently input information during busy mornings. Users can listen to the information with their eyes closed or while working, filtering out irrelevant information and efficiently acquiring only what they need. For example, they can listen to the latest news with their eyes closed in bed in the morning, or check their schedule for the day while eating breakfast. Furthermore, the information delivery system allows users to make effective use of their time. Because users can acquire necessary information solely through audio, without being constrained by visual information, they can input information even when their hands are occupied, such as while commuting or doing household chores.For example, users can listen to radio programs using earphones while commuting, or listen to the latest news while doing housework. In this way, information provision systems can utilize generative AI to combine users' personal information with news service content, thereby realizing an efficient and convenient form of information delivery for users. This allows information provision systems to efficiently and conveniently deliver information by combining users' personal information with news service content.

[0029] The information provision system according to this embodiment comprises a collection unit, a generation unit, a narration unit, and a personal information collection unit. The collection unit collects information of interest to the user. For example, the collection unit collects news and weather information from news provision services. The collection unit can collect information from news sites using web scraping technology. The collection unit can also obtain data from news provision services using APIs. For example, the collection unit uses the API of a news provision service to obtain the latest news articles. Furthermore, the collection unit can prioritize the collection of news categories of interest to the user. For example, the collection unit collects relevant news articles based on news categories set by the user. The generation unit analyzes the information collected by the collection unit and generates a radio program script. The generation unit automatically generates a script based on the collected information using a generation AI. The generation AI, for example, uses a text generation AI (e.g., LLM) to summarize news articles and weather information and create a script. The generation unit analyzes the collected information and selects information that is important to the user. For example, the generation unit generates a script that first presents the latest news, followed by weather information, schedules, and announcements. The generation unit can create scripts in natural language using generation AI. The narration unit reads aloud the script generated by the generation unit. The narration unit provides information in a natural voice using AI narration. The narration unit reads aloud the generated script using, for example, speech synthesis technology. The narration unit can convey information in a way that users can easily listen to. For example, the narration unit reads the latest news, then provides today's weather, and then introduces schedules and announcements. The personal information collection unit collects the user's personal information. For example, the personal information collection unit collects the user's schedule and announcements. The personal information collection unit can obtain data from calendar apps and messaging apps. For example, the personal information collection unit obtains today's schedule from the user's calendar app. Furthermore, the personal information collection unit can collect the user's announcements and select important information.For example, the personal information collection unit extracts important messages from the user's messaging app. This allows the information provision system according to the embodiment to efficiently collect information of interest to the user and provide it in a radio program format.

[0030] The data collection unit collects information of interest to users. For example, it collects news and weather information from news delivery services. The data collection unit can collect information from news sites using web scraping technology. Specifically, by using web scraping technology, it analyzes the HTML structure of news sites and extracts the necessary information. For example, it can automatically obtain the title, body text, publication date and time, and author name of news articles. The data collection unit can also obtain data from news delivery services using APIs. For example, the data collection unit uses the API of a news delivery service to obtain the latest news articles. By using an API, it can efficiently obtain information according to the data format provided by the news delivery service. Furthermore, the data collection unit can prioritize collecting news categories of interest to users. For example, the data collection unit collects relevant news articles based on the news categories set by the user. If a user selects categories such as sports, politics, or entertainment, the data collection unit will prioritize collecting news articles related to these categories. This allows the data collection unit to efficiently collect and provide information that matches the user's interests. Furthermore, the data collection unit can centrally manage the collected information and collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the generation and narration units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit analyzes the information collected by the collection unit and generates a radio program script. The generation unit uses a generation AI to automatically generate a script based on the collected information. The generation AI, for example, uses a text generation AI (e.g., LLM) to summarize news articles and weather information to create a script. Specifically, the generation AI analyzes the content of collected news articles, extracts important points, and summarizes them. For example, it briefly summarizes the main content of an article based on the headline and lead paragraph. It can also summarize weather forecasts for each region and convey them concisely. The generation unit analyzes the collected information and selects information that is important to the user. For example, the generation unit generates a script that introduces the latest news first, followed by weather information, schedules, and announcements. The generation unit can create a script in natural language using the generation AI. The generation AI utilizes natural language processing technology to generate grammatically correct sentences and create an easy-to-read script. Furthermore, the generation unit can improve the content of the script based on user feedback. For example, if a user shows interest in a particular news category, information related to that category will be prioritized and incorporated into the script. Furthermore, the generation unit can analyze the content of past scripts to optimize information delivery according to user interests. This allows the generation unit to efficiently produce scripts that provide useful and interesting information to users.

[0032] The narration unit reads aloud the script generated by the generation unit. The narration unit uses AI narration to provide information in a natural voice. Specifically, the narration unit uses speech synthesis technology to read aloud the generated script. Speech synthesis technology is a technology that converts text into speech and can reproduce natural pronunciation and intonation. For example, when reading a news article, it provides easy-to-listen-to audio by emphasizing important parts and using appropriate pauses. Also, when reading weather information, it can clearly convey weather forecasts for each region. The narration unit can convey information in a way that users can easily listen to. For example, the narration unit might read the latest news, then tell the weather for the day, and then introduce schedules and announcements. This allows users to efficiently obtain important information while going about their daily activities. Furthermore, the narration unit can improve the quality of its voice based on user feedback. For example, if a user is dissatisfied with a particular pronunciation or intonation, the speech synthesis model is adjusted based on that feedback. The narration unit can also provide multiple voice styles. For example, users can select a voice style that suits their preferences, such as a formal voice style for reading the news or a casual voice style for weather forecasts. This allows the narration to provide audio information that is easy for the user to listen to and is interesting to them.

[0033] The Personal Information Collection Unit collects users' personal information. For example, it collects users' schedules and communications. Specifically, the Personal Information Collection Unit can obtain data from calendar and messaging apps. For example, it can obtain today's schedule from the user's calendar app. By using the calendar app's API, it can obtain the user's schedule information and extract important appointments. The Personal Information Collection Unit can also collect users' communications and select important information. For example, it can extract important communications from the user's messaging app. By using the messaging app's API, it can analyze the user's received messages and identify important communications. Furthermore, the Personal Information Collection Unit can improve the accuracy of the information it collects based on user feedback. For example, if a user deems a particular communications important, the system can be adjusted to prioritize the collection of that information. The Personal Information Collection Unit also properly manages the collected information to protect user privacy. For example, the collected data is encrypted and stored on a secure server. This allows the Personal Information Collection Unit to efficiently collect users' personal information and improve the overall system performance.

[0034] The generation unit can analyze news and weather information from a news service and generate a radio program script. For example, the generation unit can analyze the RSS feed of a news service to obtain the latest news articles. The generation unit can also obtain weather information using the API of a news service. For example, the generation unit can obtain weather forecasts based on data from the Japan Meteorological Agency and incorporate them into the script. The generation unit analyzes the collected news articles and weather information and selects information that is important to the user. For example, the generation unit can generate a script that introduces the latest news first, followed by weather information. This allows the generation unit to analyze information from a news service and generate a radio program script. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the RSS feed of a news service into a generation AI and have the generation AI summarize the news articles.

[0035] The narration unit can read the generated script in a natural voice. The narration unit can read the generated script using, for example, speech synthesis technology. The narration unit can also generate natural voices using voice samples. For example, the narration unit can generate natural voices based on voice samples of professional narrators. Based on the generated script, the narration unit provides information in a way that the user can easily listen to. For example, the narration unit reads the latest news, then tells the weather for the day, and then introduces the schedule and announcements. This allows for the provision of a radio program in a natural voice. Some or all of the above processing in the narration unit may be performed using a generative AI, or not. For example, the narration unit can input the generated script into a generative AI and have the generative AI perform the generation of natural voices.

[0036] The personal information collection unit can collect personal information such as the user's schedule and contact information. For example, the personal information collection unit can obtain the user's schedule from a calendar app. The personal information collection unit can also obtain the user's contact information from a messaging app. For example, the personal information collection unit can extract important contact information from the user's email account. The personal information collection unit analyzes the collected personal information and selects information that is important to the user. For example, the personal information collection unit prioritizes collecting today's schedule and important contact information. In this way, the personal information collection unit can collect the user's personal information. Some or all of the above processing in the personal information collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the personal information collection unit can input schedule data obtained from a calendar app into a generation AI and have the generation AI extract important appointments.

[0037] The data collection unit can analyze a user's past news browsing history and select the most suitable news categories. For example, the data collection unit can analyze a user's browser history to identify frequently viewed news categories. The data collection unit can also analyze news app logs to extract news categories that the user is interested in. For example, the data collection unit prioritizes collecting news categories that the user has frequently viewed in the past. The data collection unit can also suggest new news categories that the user might be interested in based on their past browsing history. The data collection unit can also predict and collect news categories that a user will view at specific times. This allows for the selection of the most suitable news categories based on the user's past browsing history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's news browsing history data into an AI and have the AI ​​select the most suitable news categories.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the user's current geographical location when gathering news and weather information. For example, the data collection unit can acquire the user's GPS data and collect weather information relevant to the current location. The data collection unit can also identify the user's geographical location based on their IP address and collect local news. For example, the data collection unit can prioritize the collection of weather information for the area where the user is currently located. The data collection unit can also collect local news relevant to the user's current location. If the user is traveling, the data collection unit can also collect news and weather information for their travel destination. This allows for the collection of highly relevant information based on the user's current geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into AI and have the AI ​​perform the collection of highly relevant information.

[0039] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting news and weather information. For example, the data collection unit can analyze the content of the user's social media posts to identify news categories of interest. The data collection unit can also collect news that the user's social media followers are interested in. For example, the data collection unit can prioritize collecting news categories that the user has shared on social media. The data collection unit can also collect news that the user's social media followers are interested in. The data collection unit can also collect weather information that the user has mentioned on social media. This allows the collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into AI and have AI perform the collection of relevant information.

[0040] The data collection unit can filter news and weather information based on the user's areas of interest. For example, the data collection unit can identify areas of interest based on the user's survey results and collect relevant news and weather information. The data collection unit can also analyze the user's past browsing history and prioritize the collection of information related to areas of interest. For example, the data collection unit can collect only news categories that the user is interested in. The data collection unit can also collect only weather information that the user is interested in. The data collection unit can also prioritize the collection of news and weather information related to the user's areas of interest. This allows information to be filtered based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user area of ​​interest data into AI and have the AI ​​perform the filtering.

[0041] The generation unit can adjust the level of detail in the script based on the importance of news and weather information during script generation. For example, the generation unit can evaluate the impact of news articles and explain important news in detail. The generation unit can also evaluate the urgency of weather information and explain important weather information in detail. For example, the generation unit can explain important news in detail and summarize less important news concisely. The generation unit can also explain important weather information in detail and summarize less important weather information concisely. The generation unit can also explain important announcements in detail and summarize less important announcements concisely. This allows the level of detail in the script to be adjusted based on the importance of news and weather information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input news article impact data into a generation AI and have the generation AI perform the adjustment of the level of detail in the script.

[0042] The generation unit can apply different generation algorithms depending on the category of news or weather information when generating a script. For example, the generation unit can apply different generation algorithms depending on the news category. The generation unit can also apply different generation algorithms depending on the weather information category. For example, the generation unit can apply different generation algorithms depending on the news category. The generation unit can also apply different generation algorithms depending on the weather information category. The generation unit can also apply different generation algorithms depending on the contact information category. This allows different generation algorithms to be applied depending on the category of news or weather information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input news category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0043] The generation unit can determine the priority of scripts based on when news and weather information is collected during script generation. For example, the generation unit can prioritize incorporating the latest news into the script. The generation unit can also prioritize incorporating the latest weather information into the script. For example, the generation unit can prioritize incorporating the latest news into the script. The generation unit can also prioritize incorporating the latest weather information into the script. The generation unit can also prioritize incorporating the latest announcements into the script. This allows the generation unit to determine the priority of scripts based on when news and weather information is collected. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input news and weather information collection timing data into a generation AI and have the generation AI perform the determination of script priorities.

[0044] The generation unit can adjust the order of scripts based on the relevance of news and weather information during script generation. For example, the generation unit can evaluate the degree of topic relevance of news articles and prioritize incorporating highly relevant news into the script. The generation unit can also evaluate the number of related news items and prioritize incorporating highly relevant weather information into the script. For example, the generation unit can prioritize incorporating highly relevant news into the script. The generation unit can also prioritize incorporating highly relevant weather information into the script. The generation unit can also prioritize incorporating highly relevant announcements into the script. This allows the order of scripts to be adjusted based on the relevance of news and weather information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data of news and weather information into a generation AI and have the generation AI perform the adjustment of the script order.

[0045] The narration unit can select the optimal narration method by referring to the user's past listening history during narration. For example, the narration unit can analyze the user's playback history to identify preferred narration styles. The narration unit can also analyze listening time to select the optimal narration speed. For example, the narration unit can prioritize selecting narration styles that the user has preferred in the past. The narration unit can also select the optimal narration speed from the user's past listening history. The narration unit can also analyze the user's past listening history to select the optimal narration tone. This allows the narration unit to select the optimal narration method based on the user's past listening history. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input the user's listening history data into AI and have the AI ​​select the optimal narration method.

[0046] The narration unit can adjust the order of narration based on the importance of news and weather information. For example, the narration unit can evaluate the impact of news articles and narrate important news first. The narration unit can also evaluate the urgency of weather information and narrate important weather information first. For example, the narration unit can narrate important news first. The narration unit can also narrate important weather information first. The narration unit can also narrate important announcements first. This allows the order of narration to be adjusted based on the importance of news and weather information. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input importance data for news and weather information into AI and have the AI ​​perform the adjustment of the narration order.

[0047] The narration unit can select the optimal narration method while considering the user's device information. For example, the narration unit can identify the type of device the user is using and select the optimal narration method. The narration unit can also analyze device usage and select the optimal narration method. For example, if the user is using a smartphone, the narration unit can provide a narration method that matches the screen size. If the user is using a tablet, the narration unit can also provide a narration method optimized for a larger screen. If the user is using a smartwatch, the narration unit can also provide a concise and highly visible narration method. This allows the narration unit to select the optimal narration method based on the user's device information. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input the user's device information into AI and have the AI ​​select the optimal narration method.

[0048] The narration unit can adjust the volume of the narration based on the user's listening environment. For example, the narration unit can evaluate the noise level around the user and set an appropriate volume. The narration unit can also identify the user's location and set an optimal volume. For example, if the user is in a quiet environment, the narration unit can narrate at a lower volume. If the user is in a noisy environment, the narration unit can narrate at a higher volume. If the user is on the move, the narration unit can narrate at an appropriate volume. This allows the narration volume to be adjusted based on the user's listening environment. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input the user's listening environment data into the AI ​​and have the AI ​​perform the volume adjustment.

[0049] The personal information collection unit can analyze a user's past schedules and communications and select the optimal collection method. For example, the personal information collection unit can analyze the history of a user's calendar app to identify frequently used schedules. The personal information collection unit can also analyze the history of a messaging app to extract important communications. For example, the personal information collection unit prioritizes collecting schedules and communications that the user has frequently used in the past. The personal information collection unit can also select the optimal collection timing from the user's past schedule. The personal information collection unit can also analyze a user's past communications and select the optimal collection method. This allows the system to select the optimal collection method based on the user's past schedules and communications. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input the user's schedule and communications history data into an AI and have the AI ​​select the optimal collection method.

[0050] The personal information collection unit can filter personal information based on the user's current lifestyle and areas of interest. For example, the personal information collection unit can analyze the user's daily activities and collect relevant schedules and communications. The personal information collection unit can also analyze the user's lifestyle patterns and collect relevant personal information. For example, the personal information collection unit can prioritize collecting relevant schedules and communications based on the user's current lifestyle. The personal information collection unit can also prioritize collecting relevant personal information based on the user's areas of interest. The personal information collection unit can also filter out unnecessary information, taking into account the user's current lifestyle and areas of interest. This allows information to be filtered based on the user's current lifestyle and areas of interest. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input user lifestyle and area of ​​interest data into AI and have the AI ​​perform the filtering.

[0051] The personal information collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting personal information. For example, the personal information collection unit can acquire the user's GPS data and collect schedules and announcements related to the current location. The personal information collection unit can also identify the user's geographical location based on their IP address and collect relevant personal information. For example, the personal information collection unit can prioritize the collection of schedules and announcements for the area where the user is currently located. The personal information collection unit can also collect personal information related to the user's current location. If the user is traveling, the personal information collection unit can also collect schedules and announcements for their travel destination. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input the user's geographical location data into AI and have the AI ​​collect highly relevant information.

[0052] The personal information collection unit can collect relevant information by analyzing the user's social media activity when collecting personal information. For example, the personal information collection unit can analyze the content of the user's social media posts to identify schedules and announcements of interest. The personal information collection unit can also collect personal information of interest to the user's social media followers. For example, the personal information collection unit can prioritize collecting schedules and announcements shared by the user on social media. The personal information collection unit can also collect personal information of interest to the user's social media followers. The personal information collection unit can also collect schedules and announcements mentioned by the user on social media. This allows the collection of relevant information based on the user's social media activity. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input the user's social media data into AI and have the AI ​​collect relevant information.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The information provision system can analyze a user's past news browsing history and select the most suitable news categories. For example, the collection unit analyzes the user's browser history to identify frequently viewed news categories. The generation unit can prioritize incorporating news categories that the user has previously shown interest in into the script. The narration unit can prioritize selecting narration styles that the user has previously enjoyed listening to and provide the most suitable narration method. This allows the system to select the most suitable news categories based on the user's past news browsing history.

[0055] The information provision system can prioritize the collection of highly relevant information by considering the user's current geographical location. For example, the collection unit acquires the user's GPS data and collects weather information and local news related to the current location. The generation unit can incorporate information related to the user's current location into a script. If the user is traveling, the narration unit can prioritize narrating news and weather information for the travel destination. This allows for the collection of highly relevant information based on the user's current geographical location.

[0056] The information provision system can collect relevant information by analyzing users' social media activity when gathering news and weather information. For example, the collection unit can analyze the content of users' social media posts and identify news categories of interest. The generation unit can prioritize incorporating news categories shared by users on social media into the script. The narration unit can prioritize narrating news that is of interest to the user's social media followers. In this way, relevant information can be collected based on the user's social media activity.

[0057] The information provision system can adjust the level of detail in the script during script generation based on the importance of news and weather information. For example, the generation unit can evaluate the impact of news articles and explain important news in detail. The generation unit can also evaluate the urgency of weather information and explain important weather information in detail. The narration unit can explain important announcements in detail and summarize less important announcements concisely. In this way, the level of detail in the script can be adjusted based on the importance of news and weather information.

[0058] The information provision system can apply different generation algorithms depending on the category of news or weather information when generating scripts. For example, the generation unit can apply different generation algorithms depending on the news category. The generation unit can apply different generation algorithms depending on the weather information category. The narration unit can apply different generation algorithms depending on the announcement category. This allows for the application of different generation algorithms depending on the category of news or weather information.

[0059] The information delivery system can adjust the volume of narration based on the user's listening environment. For example, the narration unit can evaluate the noise level around the user and set an appropriate volume. The narration unit can identify the user's location and set the optimal volume. If the user is in a quiet environment, the narration unit can narrate at a lower volume. In this way, the narration volume can be adjusted based on the user's listening environment.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects information of interest to the user. The data collection unit uses web scraping techniques and APIs to collect news and weather information from news service providers. For example, the data collection unit uses the API of a news service provider to retrieve the latest news articles and prioritizes collecting relevant news articles based on the news categories set by the user. Step 2: The generation unit analyzes the information collected by the collection unit and generates a radio program script. The generation unit automatically generates a script based on the collected information using a generation AI. For example, the generation unit uses a text generation AI (e.g., LLM) to summarize news articles and weather information, selects the information important to the user, and creates a script. The generation unit generates a script that first introduces the latest news, followed by weather information, schedules, and announcements in that order. Step 3: The narration unit reads aloud the script generated by the generation unit. The narration unit provides information in a natural voice using AI narration. For example, the narration unit reads aloud a script generated using speech synthesis technology, conveying information in a way that users can easily listen to. The narration unit reads the latest news, then gives today's weather, and then introduces the schedule and announcements. Step 4: The personal information collection unit collects the user's personal information. The personal information collection unit retrieves data from calendar and messaging apps to collect the user's schedule and messages. For example, the personal information collection unit retrieves today's schedule from the user's calendar app and extracts important messages from the messaging app.

[0062] (Example of form 2) An information provision system according to an embodiment of the present invention is a mechanism that utilizes a generating AI to combine a user's personal information with content from a news provision service and provide "a personalized morning information program" in radio format every morning. This information provision system collects information of interest to the user, such as the latest news, weather, today's schedule, and recent notices, and the generating AI automatically generates a radio program script based on this information, which is then read aloud by AI narration. This radio program is provided in a way that the user can listen to while lying in bed with their eyes closed in the morning, or while eating breakfast or working during their morning routine. First, the information provision system collects information of interest to the user. In this process, it collects news and weather information from the news provision service, as well as personal information such as the user's schedule and notices. For example, it collects information such as news categories of interest to the user, today's weather, and scheduled meetings and events. Next, the information provision system uses a generating AI to automatically generate a radio program script based on the collected information. The generating AI analyzes the collected information, selects information that is important to the user, and creates a script in the format of a radio program. For example, it generates a script that introduces the latest news first, followed by weather information, schedule, and notices in that order. Based on the generated script, the information delivery system uses AI narration to read radio programs. The AI ​​narration delivers information in a natural voice, conveying it in a way that users can easily listen to. For example, it might read the latest news, then provide today's weather, and then introduce schedules and announcements. This mechanism allows the information delivery system to efficiently input information during busy mornings. Users can listen to the information with their eyes closed or while working, filtering out irrelevant information and efficiently acquiring only what they need. For example, they can listen to the latest news with their eyes closed in bed in the morning, or check their schedule for the day while eating breakfast. Furthermore, the information delivery system allows users to make effective use of their time. Because users can acquire necessary information solely through audio, without being constrained by visual information, they can input information even when their hands are occupied, such as while commuting or doing household chores.For example, users can listen to radio programs using earphones while commuting, or listen to the latest news while doing housework. In this way, information provision systems can utilize generative AI to combine users' personal information with news service content, thereby realizing an efficient and convenient form of information delivery for users. This allows information provision systems to efficiently and conveniently deliver information by combining users' personal information with news service content.

[0063] The information provision system according to this embodiment comprises a collection unit, a generation unit, a narration unit, and a personal information collection unit. The collection unit collects information of interest to the user. For example, the collection unit collects news and weather information from news provision services. The collection unit can collect information from news sites using web scraping technology. The collection unit can also obtain data from news provision services using APIs. For example, the collection unit uses the API of a news provision service to obtain the latest news articles. Furthermore, the collection unit can prioritize the collection of news categories of interest to the user. For example, the collection unit collects relevant news articles based on news categories set by the user. The generation unit analyzes the information collected by the collection unit and generates a radio program script. The generation unit automatically generates a script based on the collected information using a generation AI. The generation AI, for example, uses a text generation AI (e.g., LLM) to summarize news articles and weather information and create a script. The generation unit analyzes the collected information and selects information that is important to the user. For example, the generation unit generates a script that first presents the latest news, followed by weather information, schedules, and announcements. The generation unit can create scripts in natural language using generation AI. The narration unit reads aloud the script generated by the generation unit. The narration unit provides information in a natural voice using AI narration. The narration unit reads aloud the generated script using, for example, speech synthesis technology. The narration unit can convey information in a way that users can easily listen to. For example, the narration unit reads the latest news, then provides today's weather, and then introduces schedules and announcements. The personal information collection unit collects the user's personal information. For example, the personal information collection unit collects the user's schedule and announcements. The personal information collection unit can obtain data from calendar apps and messaging apps. For example, the personal information collection unit obtains today's schedule from the user's calendar app. Furthermore, the personal information collection unit can collect the user's announcements and select important information.For example, the personal information collection unit extracts important messages from the user's messaging app. This allows the information provision system according to the embodiment to efficiently collect information of interest to the user and provide it in a radio program format.

[0064] The data collection unit collects information of interest to users. For example, it collects news and weather information from news delivery services. The data collection unit can collect information from news sites using web scraping technology. Specifically, by using web scraping technology, it analyzes the HTML structure of news sites and extracts the necessary information. For example, it can automatically obtain the title, body text, publication date and time, and author name of news articles. The data collection unit can also obtain data from news delivery services using APIs. For example, the data collection unit uses the API of a news delivery service to obtain the latest news articles. By using an API, it can efficiently obtain information according to the data format provided by the news delivery service. Furthermore, the data collection unit can prioritize collecting news categories of interest to users. For example, the data collection unit collects relevant news articles based on the news categories set by the user. If a user selects categories such as sports, politics, or entertainment, the data collection unit will prioritize collecting news articles related to these categories. This allows the data collection unit to efficiently collect and provide information that matches the user's interests. Furthermore, the data collection unit can centrally manage the collected information and collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the generation and narration units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The generation unit analyzes the information collected by the collection unit and generates a radio program script. The generation unit uses a generation AI to automatically generate a script based on the collected information. The generation AI, for example, uses a text generation AI (e.g., LLM) to summarize news articles and weather information to create a script. Specifically, the generation AI analyzes the content of collected news articles, extracts important points, and summarizes them. For example, it briefly summarizes the main content of an article based on the headline and lead paragraph. It can also summarize weather forecasts for each region and convey them concisely. The generation unit analyzes the collected information and selects information that is important to the user. For example, the generation unit generates a script that introduces the latest news first, followed by weather information, schedules, and announcements. The generation unit can create a script in natural language using the generation AI. The generation AI utilizes natural language processing technology to generate grammatically correct sentences and create an easy-to-read script. Furthermore, the generation unit can improve the content of the script based on user feedback. For example, if a user shows interest in a particular news category, information related to that category will be prioritized and incorporated into the script. Furthermore, the generation unit can analyze the content of past scripts to optimize information delivery according to user interests. This allows the generation unit to efficiently produce scripts that provide useful and interesting information to users.

[0066] The narration unit reads aloud the script generated by the generation unit. The narration unit uses AI narration to provide information in a natural voice. Specifically, the narration unit uses speech synthesis technology to read aloud the generated script. Speech synthesis technology is a technology that converts text into speech and can reproduce natural pronunciation and intonation. For example, when reading a news article, it provides easy-to-listen-to audio by emphasizing important parts and using appropriate pauses. Also, when reading weather information, it can clearly convey weather forecasts for each region. The narration unit can convey information in a way that users can easily listen to. For example, the narration unit might read the latest news, then tell the weather for the day, and then introduce schedules and announcements. This allows users to efficiently obtain important information while going about their daily activities. Furthermore, the narration unit can improve the quality of its voice based on user feedback. For example, if a user is dissatisfied with a particular pronunciation or intonation, the speech synthesis model is adjusted based on that feedback. The narration unit can also provide multiple voice styles. For example, users can select a voice style that suits their preferences, such as a formal voice style for reading the news or a casual voice style for weather forecasts. This allows the narration to provide audio information that is easy for the user to listen to and is interesting to them.

[0067] The Personal Information Collection Unit collects users' personal information. For example, it collects users' schedules and communications. Specifically, the Personal Information Collection Unit can obtain data from calendar and messaging apps. For example, it can obtain today's schedule from the user's calendar app. By using the calendar app's API, it can obtain the user's schedule information and extract important appointments. The Personal Information Collection Unit can also collect users' communications and select important information. For example, it can extract important communications from the user's messaging app. By using the messaging app's API, it can analyze the user's received messages and identify important communications. Furthermore, the Personal Information Collection Unit can improve the accuracy of the information it collects based on user feedback. For example, if a user deems a particular communications important, the system can be adjusted to prioritize the collection of that information. The Personal Information Collection Unit also properly manages the collected information to protect user privacy. For example, the collected data is encrypted and stored on a secure server. This allows the Personal Information Collection Unit to efficiently collect users' personal information and improve the overall system performance.

[0068] The generation unit can analyze news and weather information from a news service and generate a radio program script. For example, the generation unit can analyze the RSS feed of a news service to obtain the latest news articles. The generation unit can also obtain weather information using the API of a news service. For example, the generation unit can obtain weather forecasts based on data from the Japan Meteorological Agency and incorporate them into the script. The generation unit analyzes the collected news articles and weather information and selects information that is important to the user. For example, the generation unit can generate a script that introduces the latest news first, followed by weather information. This allows the generation unit to analyze information from a news service and generate a radio program script. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the RSS feed of a news service into a generation AI and have the generation AI summarize the news articles.

[0069] The narration unit can read the generated script in a natural voice. The narration unit can read the generated script using, for example, speech synthesis technology. The narration unit can also generate natural voices using voice samples. For example, the narration unit can generate natural voices based on voice samples of professional narrators. Based on the generated script, the narration unit provides information in a way that the user can easily listen to. For example, the narration unit reads the latest news, then tells the weather for the day, and then introduces the schedule and announcements. This allows for the provision of a radio program in a natural voice. Some or all of the above processing in the narration unit may be performed using a generative AI, or not. For example, the narration unit can input the generated script into a generative AI and have the generative AI perform the generation of natural voices.

[0070] The personal information collection unit can collect personal information such as the user's schedule and contact information. For example, the personal information collection unit can obtain the user's schedule from a calendar app. The personal information collection unit can also obtain the user's contact information from a messaging app. For example, the personal information collection unit can extract important contact information from the user's email account. The personal information collection unit analyzes the collected personal information and selects information that is important to the user. For example, the personal information collection unit prioritizes collecting today's schedule and important contact information. In this way, the personal information collection unit can collect the user's personal information. Some or all of the above processing in the personal information collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the personal information collection unit can input schedule data obtained from a calendar app into a generation AI and have the generation AI extract important appointments.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of news and weather information collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. Based on the user's emotions, the data collection unit adjusts the timing of news and weather information collection. For example, if the user is stressed, the data collection unit can collect news and weather information during times when the user is relaxed. If the user is relaxed, the data collection unit can also collect news and weather information early in the morning. If the user is busy, the data collection unit can also collect important information in a short amount of time. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can analyze a user's past news browsing history and select the most suitable news categories. For example, the data collection unit can analyze a user's browser history to identify frequently viewed news categories. The data collection unit can also analyze news app logs to extract news categories that the user is interested in. For example, the data collection unit prioritizes collecting news categories that the user has frequently viewed in the past. The data collection unit can also suggest new news categories that the user might be interested in based on their past browsing history. The data collection unit can also predict and collect news categories that a user will view at specific times. This allows for the selection of the most suitable news categories based on the user's past browsing history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's news browsing history data into an AI and have the AI ​​select the most suitable news categories.

[0073] The data collection unit can prioritize the collection of highly relevant information by considering the user's current geographical location when gathering news and weather information. For example, the data collection unit can acquire the user's GPS data and collect weather information relevant to the current location. The data collection unit can also identify the user's geographical location based on their IP address and collect local news. For example, the data collection unit can prioritize the collection of weather information for the area where the user is currently located. The data collection unit can also collect local news relevant to the user's current location. If the user is traveling, the data collection unit can also collect news and weather information for their travel destination. This allows for the collection of highly relevant information based on the user's current geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into AI and have the AI ​​perform the collection of highly relevant information.

[0074] The data collection unit can estimate the user's emotions and prioritize the news and weather information to collect based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. Based on the user's emotions, the data collection unit determines the priority of the news and weather information to collect. For example, if the user is stressed, the data collection unit will prioritize collecting positive news. If the user is relaxed, the data collection unit may also prioritize collecting detailed weather information. If the user is busy, the data collection unit may also prioritize collecting important news that can be accessed quickly. This allows for the prioritization of information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting news and weather information. For example, the data collection unit can analyze the content of the user's social media posts to identify news categories of interest. The data collection unit can also collect news that the user's social media followers are interested in. For example, the data collection unit can prioritize collecting news categories that the user has shared on social media. The data collection unit can also collect news that the user's social media followers are interested in. The data collection unit can also collect weather information that the user has mentioned on social media. This allows the collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into AI and have AI perform the collection of relevant information.

[0076] The data collection unit can filter news and weather information based on the user's areas of interest. For example, the data collection unit can identify areas of interest based on the user's survey results and collect relevant news and weather information. The data collection unit can also analyze the user's past browsing history and prioritize the collection of information related to areas of interest. For example, the data collection unit can collect only news categories that the user is interested in. The data collection unit can also collect only weather information that the user is interested in. The data collection unit can also prioritize the collection of news and weather information related to the user's areas of interest. This allows information to be filtered based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user area of ​​interest data into AI and have the AI ​​perform the filtering.

[0077] The generation unit can estimate the user's emotions and adjust the script's expression based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit adjusts the script's expression based on the user's emotions. For example, if the user is relaxed, the generation unit will generate a script with a calm expression. If the user is in a hurry, the generation unit can also generate a script with a concise and to-the-point expression. If the user is excited, the generation unit can also generate a script with an energetic expression. This allows the script's expression to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0078] The generation unit can adjust the level of detail in the script based on the importance of news and weather information during script generation. For example, the generation unit can evaluate the impact of news articles and explain important news in detail. The generation unit can also evaluate the urgency of weather information and explain important weather information in detail. For example, the generation unit can explain important news in detail and summarize less important news concisely. The generation unit can also explain important weather information in detail and summarize less important weather information concisely. The generation unit can also explain important announcements in detail and summarize less important announcements concisely. This allows the level of detail in the script to be adjusted based on the importance of news and weather information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input news article impact data into a generation AI and have the generation AI perform the adjustment of the level of detail in the script.

[0079] The generation unit can apply different generation algorithms depending on the category of news or weather information when generating a script. For example, the generation unit can apply different generation algorithms depending on the news category. The generation unit can also apply different generation algorithms depending on the weather information category. For example, the generation unit can apply different generation algorithms depending on the news category. The generation unit can also apply different generation algorithms depending on the weather information category. The generation unit can also apply different generation algorithms depending on the contact information category. This allows different generation algorithms to be applied depending on the category of news or weather information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input news category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0080] The generation unit can estimate the user's emotions and adjust the length of the script based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit adjusts the length of the script based on the user's emotions. For example, if the user is in a hurry, the generation unit will generate a short script. If the user is relaxed, the generation unit can also generate a longer script. If the user is excited, the generation unit can also generate a script with visually stimulating effects. This allows the length of the script to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The generation unit can determine the priority of scripts based on when news and weather information is collected during script generation. For example, the generation unit can prioritize incorporating the latest news into the script. The generation unit can also prioritize incorporating the latest weather information into the script. For example, the generation unit can prioritize incorporating the latest news into the script. The generation unit can also prioritize incorporating the latest weather information into the script. The generation unit can also prioritize incorporating the latest announcements into the script. This allows the generation unit to determine the priority of scripts based on when news and weather information is collected. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input news and weather information collection timing data into a generation AI and have the generation AI perform the determination of script priorities.

[0082] The generation unit can adjust the order of scripts based on the relevance of news and weather information during script generation. For example, the generation unit can evaluate the degree of topic relevance of news articles and prioritize incorporating highly relevant news into the script. The generation unit can also evaluate the number of related news items and prioritize incorporating highly relevant weather information into the script. For example, the generation unit can prioritize incorporating highly relevant news into the script. The generation unit can also prioritize incorporating highly relevant weather information into the script. The generation unit can also prioritize incorporating highly relevant announcements into the script. This allows the order of scripts to be adjusted based on the relevance of news and weather information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data of news and weather information into a generation AI and have the generation AI perform the adjustment of the script order.

[0083] The narration unit can estimate the user's emotions and adjust the tone and speed of the narration based on the estimated emotions. For example, the narration unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The narration unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the narration unit can analyze the tone and speed of the voice and calculate an emotion score. The narration unit adjusts the tone and speed of the narration based on the user's emotions. For example, if the user is relaxed, the narration unit will narrate slowly in a calm tone. If the user is in a hurry, the narration unit can narrate in a quick and concise tone. If the user is excited, the narration unit can narrate in an energetic tone. This allows the tone and speed of the narration to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the narration section may be performed using a generative AI, or they may be performed without a generative AI. For example, the narration section can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The narration unit can select the optimal narration method by referring to the user's past listening history during narration. For example, the narration unit can analyze the user's playback history to identify preferred narration styles. The narration unit can also analyze listening time to select the optimal narration speed. For example, the narration unit can prioritize selecting narration styles that the user has preferred in the past. The narration unit can also select the optimal narration speed from the user's past listening history. The narration unit can also analyze the user's past listening history to select the optimal narration tone. This allows the narration unit to select the optimal narration method based on the user's past listening history. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input the user's listening history data into AI and have the AI ​​select the optimal narration method.

[0085] The narration unit can adjust the order of narration based on the importance of news and weather information. For example, the narration unit can evaluate the impact of news articles and narrate important news first. The narration unit can also evaluate the urgency of weather information and narrate important weather information first. For example, the narration unit can narrate important news first. The narration unit can also narrate important weather information first. The narration unit can also narrate important announcements first. This allows the order of narration to be adjusted based on the importance of news and weather information. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input importance data for news and weather information into AI and have the AI ​​perform the adjustment of the narration order.

[0086] The narration unit can estimate the user's emotions and adjust the narration content based on the estimated emotions. For example, the narration unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The narration unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the narration unit can analyze the tone and speed of the voice and calculate an emotion score. The narration unit adjusts the narration content based on the user's emotions. For example, if the user is relaxed, the narration unit can provide detailed information. If the user is in a hurry, the narration unit can provide concise information. If the user is excited, the narration unit can provide visually stimulating information. This allows the narration content to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the narration section may be performed using a generative AI, or they may be performed without a generative AI. For example, the narration section can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The narration unit can select the optimal narration method while considering the user's device information. For example, the narration unit can identify the type of device the user is using and select the optimal narration method. The narration unit can also analyze device usage and select the optimal narration method. For example, if the user is using a smartphone, the narration unit can provide a narration method that matches the screen size. If the user is using a tablet, the narration unit can also provide a narration method optimized for a larger screen. If the user is using a smartwatch, the narration unit can also provide a concise and highly visible narration method. This allows the narration unit to select the optimal narration method based on the user's device information. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input the user's device information into AI and have the AI ​​select the optimal narration method.

[0088] The narration unit can adjust the volume of the narration based on the user's listening environment. For example, the narration unit can evaluate the noise level around the user and set an appropriate volume. The narration unit can also identify the user's location and set an optimal volume. For example, if the user is in a quiet environment, the narration unit can narrate at a lower volume. If the user is in a noisy environment, the narration unit can narrate at a higher volume. If the user is on the move, the narration unit can narrate at an appropriate volume. This allows the narration volume to be adjusted based on the user's listening environment. Some or all of the above processing in the narration unit may be performed using AI or not. For example, the narration unit can input the user's listening environment data into the AI ​​and have the AI ​​perform the volume adjustment.

[0089] The personal information collection unit can estimate the user's emotions and adjust the timing of personal information collection based on the estimated emotions. For example, the personal information collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The personal information collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the personal information collection unit can analyze the tone and speed of the voice and calculate an emotion score. The personal information collection unit adjusts the timing of personal information collection based on the user's emotions. For example, if the user is relaxed, the personal information collection unit can collect personal information in the early morning. If the user is busy, the personal information collection unit can also collect important personal information in a short amount of time. If the user is stressed, the personal information collection unit can also collect personal information during times when the user can relax. This allows the timing of personal information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the personal information collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the personal information collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The personal information collection unit can analyze a user's past schedules and communications and select the optimal collection method. For example, the personal information collection unit can analyze the history of a user's calendar app to identify frequently used schedules. The personal information collection unit can also analyze the history of a messaging app to extract important communications. For example, the personal information collection unit prioritizes collecting schedules and communications that the user has frequently used in the past. The personal information collection unit can also select the optimal collection timing from the user's past schedule. The personal information collection unit can also analyze a user's past communications and select the optimal collection method. This allows the system to select the optimal collection method based on the user's past schedules and communications. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input the user's schedule and communications history data into an AI and have the AI ​​select the optimal collection method.

[0091] The personal information collection unit can filter personal information based on the user's current lifestyle and areas of interest. For example, the personal information collection unit can analyze the user's daily activities and collect relevant schedules and communications. The personal information collection unit can also analyze the user's lifestyle patterns and collect relevant personal information. For example, the personal information collection unit can prioritize collecting relevant schedules and communications based on the user's current lifestyle. The personal information collection unit can also prioritize collecting relevant personal information based on the user's areas of interest. The personal information collection unit can also filter out unnecessary information, taking into account the user's current lifestyle and areas of interest. This allows information to be filtered based on the user's current lifestyle and areas of interest. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input user lifestyle and area of ​​interest data into AI and have the AI ​​perform the filtering.

[0092] The personal information collection unit can estimate the user's emotions and determine the priority of personal information to collect based on the estimated emotions. For example, the personal information collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The personal information collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the personal information collection unit can analyze the tone and speed of the voice and calculate an emotion score. Based on the user's emotions, the personal information collection unit determines the priority of personal information to collect. For example, if the user is relaxed, the personal information collection unit will prioritize collecting detailed schedules and notices. If the user is in a hurry, the personal information collection unit can also prioritize collecting important schedules and notices. If the user is stressed, the personal information collection unit can also prioritize collecting information that helps them relax. This allows the priority of personal information to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the personal information collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the personal information collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The personal information collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting personal information. For example, the personal information collection unit can acquire the user's GPS data and collect schedules and announcements related to the current location. The personal information collection unit can also identify the user's geographical location based on their IP address and collect relevant personal information. For example, the personal information collection unit can prioritize the collection of schedules and announcements for the area where the user is currently located. The personal information collection unit can also collect personal information related to the user's current location. If the user is traveling, the personal information collection unit can also collect schedules and announcements for their travel destination. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input the user's geographical location data into AI and have the AI ​​collect highly relevant information.

[0094] The personal information collection unit can collect relevant information by analyzing the user's social media activity when collecting personal information. For example, the personal information collection unit can analyze the content of the user's social media posts to identify schedules and announcements of interest. The personal information collection unit can also collect personal information of interest to the user's social media followers. For example, the personal information collection unit can prioritize collecting schedules and announcements shared by the user on social media. The personal information collection unit can also collect personal information of interest to the user's social media followers. The personal information collection unit can also collect schedules and announcements mentioned by the user on social media. This allows the collection of relevant information based on the user's social media activity. Some or all of the above processing in the personal information collection unit may be performed using AI or not. For example, the personal information collection unit can input the user's social media data into AI and have the AI ​​collect relevant information.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The information delivery system can estimate the user's emotions and adjust the tone of the news based on those emotions. For example, if the user is stressed, the collection unit will prioritize gathering calming news, and the generation unit will incorporate that news into the script in a calm tone. The narration unit can explain the news in more detail if the user is relaxed, or provide a concise explanation that gets to the point if the user is in a hurry. In this way, the tone of the news can be adjusted according to the user's emotions.

[0097] The information provision system can analyze a user's past news browsing history and select the most suitable news categories. For example, the collection unit analyzes the user's browser history to identify frequently viewed news categories. The generation unit can prioritize incorporating news categories that the user has previously shown interest in into the script. The narration unit can prioritize selecting narration styles that the user has previously enjoyed listening to and provide the most suitable narration method. This allows the system to select the most suitable news categories based on the user's past news browsing history.

[0098] The information provision system can prioritize the collection of highly relevant information by considering the user's current geographical location. For example, the collection unit acquires the user's GPS data and collects weather information and local news related to the current location. The generation unit can incorporate information related to the user's current location into a script. If the user is traveling, the narration unit can prioritize narrating news and weather information for the travel destination. This allows for the collection of highly relevant information based on the user's current geographical location.

[0099] The information provision system can estimate the user's emotions and prioritize the news and weather information to collect based on those estimated emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can prioritize incorporating positive news into the script if the user is feeling stressed. The narration unit can prioritize narrating detailed weather information if the user is relaxed. In this way, the system can prioritize information according to the user's emotions.

[0100] The information provision system can collect relevant information by analyzing users' social media activity when gathering news and weather information. For example, the collection unit can analyze the content of users' social media posts and identify news categories of interest. The generation unit can prioritize incorporating news categories shared by users on social media into the script. The narration unit can prioritize narrating news that is of interest to the user's social media followers. In this way, relevant information can be collected based on the user's social media activity.

[0101] The information provision system can estimate the user's emotions and adjust the script's expression based on those emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. If the user is relaxed, the generation unit can generate a script with a calm expression. If the user is in a hurry, the narration unit can provide narration in a concise and to-the-point style. This allows the script's expression to be adjusted according to the user's emotions.

[0102] The information provision system can adjust the level of detail in the script during script generation based on the importance of news and weather information. For example, the generation unit can evaluate the impact of news articles and explain important news in detail. The generation unit can also evaluate the urgency of weather information and explain important weather information in detail. The narration unit can explain important announcements in detail and summarize less important announcements concisely. In this way, the level of detail in the script can be adjusted based on the importance of news and weather information.

[0103] The information provision system can apply different generation algorithms depending on the category of news or weather information when generating scripts. For example, the generation unit can apply different generation algorithms depending on the news category. The generation unit can apply different generation algorithms depending on the weather information category. The narration unit can apply different generation algorithms depending on the announcement category. This allows for the application of different generation algorithms depending on the category of news or weather information.

[0104] The information provision system can estimate the user's emotions and adjust the length of the script based on those emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. If the user is in a hurry, the generation unit can generate a short script. If the user is relaxed, the narration unit can generate a longer script. This allows the length of the script to be adjusted according to the user's emotions.

[0105] The information delivery system can adjust the volume of narration based on the user's listening environment. For example, the narration unit can evaluate the noise level around the user and set an appropriate volume. The narration unit can identify the user's location and set the optimal volume. If the user is in a quiet environment, the narration unit can narrate at a lower volume. In this way, the narration volume can be adjusted based on the user's listening environment.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects information of interest to the user. The data collection unit uses web scraping techniques and APIs to collect news and weather information from news service providers. For example, the data collection unit uses the API of a news service provider to retrieve the latest news articles and prioritizes collecting relevant news articles based on the news categories set by the user. Step 2: The generation unit analyzes the information collected by the collection unit and generates a radio program script. The generation unit automatically generates a script based on the collected information using a generation AI. For example, the generation unit uses a text generation AI (e.g., LLM) to summarize news articles and weather information, selects the information important to the user, and creates a script. The generation unit generates a script that first introduces the latest news, followed by weather information, schedules, and announcements in that order. Step 3: The narration unit reads aloud the script generated by the generation unit. The narration unit provides information in a natural voice using AI narration. For example, the narration unit reads aloud a script generated using speech synthesis technology, conveying information in a way that users can easily listen to. The narration unit reads the latest news, then gives today's weather, and then introduces the schedule and announcements. Step 4: The personal information collection unit collects the user's personal information. The personal information collection unit retrieves data from calendar and messaging apps to collect the user's schedule and messages. For example, the personal information collection unit retrieves today's schedule from the user's calendar app and extracts important messages from the messaging app.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the collection unit, generation unit, narration unit, and personal information collection unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. The narration unit is implemented by the control unit 46A of the smart device 14. The personal information collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, generation unit, narration unit, and personal information collection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The narration unit is implemented, for example, by the control unit 46A of the smart glasses 214. The personal information collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, generation unit, narration unit, and personal information collection unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. The narration unit is implemented by the control unit 46A of the headset terminal 314. The personal information collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, generation unit, narration unit, and personal information collection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The narration unit is implemented, for example, by the control unit 46A of the robot 414. The personal information collection unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A collection unit that collects information of interest to the user, A generation unit analyzes the information collected by the collection unit and generates a radio program script, A narration unit reads aloud the script generated by the generation unit, It comprises a personal information collection unit that collects the user's personal information. A system characterized by the following features. (Note 2) The generating unit is Analyze news and weather information from news delivery services to generate radio program scripts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned narration section is, The generated script is read aloud in a natural voice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned personal information collection unit is: Collects personal information such as the user's schedule and contact details. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of news and weather information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past news browsing history to select the most suitable news category. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting news and weather information, the system prioritizes collecting highly relevant information by considering the user's current geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's sentiment and determines the priority of news and weather information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting news and weather information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting news and weather information, filtering is performed based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates the user's emotions and adjusts the script's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating the script, adjust the level of detail based on the importance of news and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating scripts, different generation algorithms are applied depending on the category of news or weather information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the script length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating scripts, prioritize the scripts based on when news and weather information is collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating the script, adjust the order of the script based on the relevance of news and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned narration section is, It estimates the user's emotions and adjusts the tone and speed of the narration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned narration section is, During narration, the system selects the optimal narration method by referring to the user's past listening history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned narration section is, During narration, the order of narration is adjusted based on the importance of news and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned narration section is, The system estimates the user's emotions and adjusts the narration content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned narration section is, During narration, the system selects the optimal narration method while considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned narration section is, During narration, the volume of the narration is adjusted based on the user's listening environment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned personal information collection unit is: We estimate the user's emotions and adjust the timing of personal information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned personal information collection unit is: Analyze the user's past schedules and communications to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned personal information collection unit is: When collecting personal information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned personal information collection unit is: It estimates the user's emotions and determines the priority of personal information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned personal information collection unit is: When collecting personal information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned personal information collection unit is: When collecting personal information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information of interest to the user, A generation unit analyzes the information collected by the collection unit and generates a radio program script, A narration unit reads aloud the script generated by the generation unit, It comprises a personal information collection unit that collects the user's personal information. A system characterized by the following features.

2. The generating unit is Analyze news and weather information from news delivery services to generate radio program scripts. The system according to feature 1.

3. The aforementioned narration section is, The generated script is read aloud in a natural voice. The system according to feature 1.

4. The aforementioned personal information collection unit is: Collects personal information such as the user's schedule and contact details. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of news and weather information collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past news browsing history to select the most suitable news category. The system according to feature 1.

7. The aforementioned collection unit is When collecting news and weather information, the system prioritizes collecting highly relevant information by considering the user's current geographical location. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's sentiment and determines the priority of news and weather information to collect based on the estimated user sentiment. The system according to feature 1.

Images