system

Abstract

The system according to this embodiment aims to provide personalized audio content based on the user's interests and preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on the user's viewing history and selected personalities. The analysis unit analyzes the information collected by the collection unit and selects content based on the user's interests and preferences. The generation unit transcribes and summarizes the content selected by the analysis unit using a speech generation AI and generates an action program in which the personality reads aloud. The provision unit provides the audio content generated by the generation unit to the user.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, personalized voice content based on the interests and concerns of users has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized voice content based on the interests and concerns of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on the user's viewing history and selected personalities. The analysis unit analyzes the information collected by the collection unit and selects content based on the user's interests. The generation unit transcribes and summarizes the content selected by the analysis unit using a speech generation AI and generates an action program in which the personality reads aloud. The provision unit provides the audio content generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized audio content based on the user's interests and preferences. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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) The radio service according to an embodiment of the present invention is a radio service that allows users to listen to content owned by the SB Group (mainly content from e-commerce sites) 24 hours a day. This radio service allows users to choose a personality created by a voice-generating AI that they have selected or trained themselves. Personalities can be selected from a variety of models, including real people. The content that can be listened to is mainly B2C content from e-commerce sites, and personalized content is delivered on demand 24 hours a day. Personalization is performed based on past listening history, etc. For example, users can select or train their favorite personalities. For example, they can choose from models such as announcers, singers, presenters / MCs, voice actors, DJs, comedians, and idols. This allows users to enjoy a special radio experience just for them. Next, the content to be listened to is selected from a variety of content owned by e-commerce sites. For example, this includes news, entertainment, sports, shopping, weather forecasts, etc. This content is transcribed and summarized by the voice-generating AI and read aloud by the personality. For example, it can summarize questions and answers from knowledge-sharing sites and read them aloud in a DJ-style format. Furthermore, content is personalized based on the user's past viewing history. This allows users to watch content tailored to their interests and preferences on demand, 24 hours a day. For example, if a user has watched a lot of news in the past, news content will be prioritized. Personalities can also be linked with e-commerce sites, allowing for advertising and creation / sale by creators, similar to stamps. This enables monetization of personalities. For example, users can "get" personalities by adding them as friends. This system allows users to listen to any content from e-commerce sites, read by voice-generating AI, 24 hours a day. This allows users to enjoy content without looking, enriching their time while doing other things. For example, it can be used in various situations such as commuting, driving, bathing, and exercising.This allows radio services to provide personalized audio content based on users' listening history and information about selected personalities.

[0029] The radio service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on the user's listening history and selected personalities. For example, the collection unit collects listening history such as the type of content the user has listened to, the listening time, and the frequency of listening. The collection unit can also collect information such as the profile and past activity history of personalities selected by the user. For example, the collection unit stores data of the content the user has listened to on a server and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and selects content based on the user's interests. For example, the analysis unit analyzes the listening history data to identify trends in content that the user is interested in. The analysis unit can also analyze personality information to understand the characteristics of personalities that the user prefers. For example, the analysis unit inputs the listening history data into a machine learning algorithm to predict the user's interests. The generation unit transcribes and summarizes the content selected by the analysis unit using a speech generation AI and generates an action program in which a personality reads it aloud. The generation unit, for example, summarizes news articles and generates scripts for a personality to read aloud. The generation unit can also summarize questions and answers from knowledge-sharing sites and generate a program to read them aloud in a DJ-like style. For example, the generation unit inputs the text of a news article into a speech generation AI and generates summarized audio data. The delivery unit provides the audio content generated by the generation unit to the user. The delivery unit, for example, streams the audio content, allowing users to listen in real time. The delivery unit can also make the audio content downloadable, allowing users to listen offline. For example, the delivery unit provides the audio content to the user through a web application or mobile application. This allows the radio service according to the embodiment to provide personalized audio content based on the user's listening history and information about selected personalities.

[0030] The data collection unit collects information on users' viewing history and selected personalities. Specifically, it collects detailed viewing history, including the type of content viewed, viewing time, and viewing frequency. For example, it can understand what types of content users prefer to watch, such as music, news, and podcasts, and at what times of day their viewing time is concentrated. Furthermore, it records how often specific content is viewed. This makes it possible to analyze users' viewing patterns in detail. The data collection unit also collects information on the profiles and past activity history of personalities selected by users. For example, it collects information on a personality's past appearances, their content, and fan reactions to understand what kind of personalities users prefer. This information is stored on a server and provided to the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and generation units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit and selects content based on user interests. Specifically, it analyzes viewing history data to identify trends in content that users are interested in. For example, it inputs viewing history data into a machine learning algorithm to predict user interests. The machine learning algorithm learns what kind of content users like based on past viewing history and predicts what kind of content should be provided in the future. The analysis unit can also analyze personality information to understand the characteristics of personalities that users prefer. For example, it analyzes a personality's past activity history and fan reactions to identify what kind of personality users prefer. This allows the analysis unit to select the most suitable content based on user interests. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict fluctuations in the popularity of specific content based on past viewing data and formulate future content provision plans. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit uses a speech generation AI to transcribe and summarize content selected by the analysis unit, and generates an action program for a personality to read aloud. Specifically, it summarizes news articles and generates a script for the personality to read aloud. For example, the text of a news article is input to the speech generation AI, and summarized audio data is generated. This speech generation AI uses natural language processing technology to analyze the input text, extract important information, and summarize it. The generation unit can also summarize questions and answers from knowledge-sharing sites and generate an action program for reading them aloud in a DJ style. For example, questions and answers from a knowledge-sharing site are input to the speech generation AI, and summarized audio data is generated. This audio data is used as a script for the personality to read aloud in a DJ style. Furthermore, the generation unit can also generate customized content based on the user's interests. For example, if a user likes a particular genre of music, the generation unit summarizes news and information related to that genre and generates a script for the personality to read aloud. This allows the generation unit to generate personalized content tailored to the user's interests and provide it to the delivery unit.

[0033] The delivery unit provides users with audio content generated by the generation unit. Specifically, it streams audio content, allowing users to listen to it in real time. For example, it provides audio content to users through web applications and mobile applications. Users can use these applications to listen to audio content in real time. The delivery unit can also make audio content downloadable, allowing users to listen to it offline. For example, it can download audio content so that it can be listened to even in environments without an internet connection. Furthermore, the delivery unit can collect user feedback and continuously improve the quality and delivery method of the content. For example, it can collect ratings and comments on content viewed by users and use this to improve the quality of the content. In addition, the delivery unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the delivery unit to provide audio content to users quickly and reliably, improving user satisfaction.

[0034] The generation unit can generate an action program that summarizes questions and answers from a knowledge-sharing site and reads them aloud in a DJ style. For example, the generation unit can collect and summarize questions and answers from a knowledge-sharing site. For example, the generation unit can analyze the text data of questions and answers and extract important information. The generation unit can also generate a DJ-style reading script based on the extracted information. For example, the generation unit can input the text of questions and answers into a speech generation AI and generate DJ-style audio data. This allows the user to experience a new way of reading information from a knowledge-sharing site in a DJ style. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the text data of questions and answers from a knowledge-sharing site into a generation AI and have the generation AI perform the summarization and generation of the reading script.

[0035] The data collection unit can analyze a user's past viewing history and select the optimal data collection method. For example, the data collection unit can analyze trends in content the user has previously viewed and prioritize collecting similar content. Furthermore, if a user tends to watch content during specific time periods, the data collection unit can adjust its collection accordingly. For instance, the data collection unit can re-collect content that the user interrupted, allowing them to resume viewing from where they left off. This allows the optimal data collection method to be selected by analyzing past viewing history. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past viewing history data into an AI and have the AI ​​select the optimal data collection method.

[0036] The data collection unit can filter viewing history based on the user's current lifestyle and areas of interest. For example, if the user is at work, the data collection unit will prioritize collecting business-related content. It can also collect entertainment and hobby-related content if the user is on holiday. For instance, if the user is attending a specific event, the data collection unit will collect content related to that event. This allows for the collection of highly relevant information by filtering viewing history based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user lifestyle data into an AI and have the AI ​​perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant viewing history by considering the user's geographical location when collecting viewing history. For example, if the user is in a specific region, the data collection unit will prioritize collecting news and event information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting tourist information and local news from their travel destination. For example, if the user is at home, the data collection unit will prioritize collecting local news and regional information. This allows for the priority collection of highly relevant information by considering 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 an AI and have the AI ​​perform the collection of highly relevant viewing history.

[0038] The collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the collection unit prioritizes collecting viewing history related to content shared by the user on social media. The collection unit can also collect viewing history based on the content posted by accounts that the user follows. For example, the collection unit collects viewing history related to topics in online communities that the user participates in. This allows for the collection of relevant viewing history by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant history.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the viewing history during the analysis. For example, the analysis unit can perform a detailed analysis on important viewing history to provide deeper insights. It can also perform a basic analysis on general viewing history to provide concise information. For example, the analysis unit can perform a customized analysis on viewing history that the user is particularly interested in. This allows for the provision of appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the viewing history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input viewing history importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the viewing history during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to news-related viewing history. Similarly, the analysis unit can apply an entertainment-specific analysis algorithm to entertainment-related viewing history. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related viewing history. This allows for the application of an appropriate analysis algorithm according to the category of the viewing history, thereby providing highly accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input viewing history category data into AI and have the AI ​​perform the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the submission date of viewing history. For example, the analysis unit will prioritize the analysis of recent viewing history. The analysis unit can also analyze past viewing history as needed. For example, the analysis unit will prioritize the analysis of viewing history during a specific event or campaign period. By determining the priority of analysis based on the submission date of viewing history, the analysis results can be provided at the appropriate time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input viewing history submission date data into AI and have the AI ​​perform the determination of analysis priorities.

[0042] The analysis unit can adjust the order of analysis based on the relevance of viewing history during the analysis process. For example, the analysis unit may prioritize analyzing viewing history related to the user's current interests. It can also prioritize analyzing viewing history that is highly relevant to the user's past viewing history. For example, the analysis unit may prioritize analyzing particularly important content in the user's viewing history. By adjusting the order of analysis based on the relevance of viewing history, more relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input viewing history relevance data into AI and have the AI ​​perform the adjustment of the analysis order.

[0043] The generation unit can adjust the level of detail of the generated content based on its importance. For example, the generation unit can generate a detailed action program for important content. It can also generate a basic action program for general content. For example, the generation unit can generate a customized action program for content of particular interest to the user. This allows for the provision of appropriate action programs by adjusting the level of detail of the generated content based on its importance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the generated content.

[0044] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a news-specific generation algorithm to news-related content. Similarly, it can apply an entertainment-specific generation algorithm to entertainment-related content. For example, it can apply a sports-specific generation algorithm to sports-related content. This allows for the provision of highly accurate operating programs by applying the appropriate generation algorithm according to the content category. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input content category data into the AI ​​and have the AI ​​perform the application of the generation algorithm.

[0045] The generation unit can determine the generation priority based on the content submission date during generation. For example, the generation unit will prioritize generating action programs for recent content. The generation unit can also generate action programs for past content as needed. For example, the generation unit will prioritize generating action programs for content from a specific event or campaign period. This allows for the provision of action programs at the appropriate time by determining the generation priority based on the content submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input content submission date data into AI and have the AI ​​determine the generation priority.

[0046] The generation unit can adjust the generation order based on the relevance of the content during generation. For example, the generation unit can prioritize generating content related to the user's current interests. It can also prioritize generating content that is highly relevant to the user's past viewing history. For example, the generation unit can prioritize generating content that is particularly important in the user's viewing history. By adjusting the generation order based on the relevance of the content, it is possible to provide a more relevant program. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input content relevance data into AI and have AI perform the adjustment of the generation order.

[0047] The delivery unit can adjust the level of detail provided based on the importance of the audio content at the time of delivery. For example, the delivery unit will provide a detailed explanation for important audio content. The delivery unit can also provide basic information for general audio content. For example, the delivery unit will provide a customized delivery for audio content that the user is particularly interested in. This allows for the provision of appropriate information by adjusting the level of detail based on the importance of the audio content. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input audio content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the delivery.

[0048] The delivery unit can apply different delivery algorithms depending on the category of the audio content at the time of delivery. For example, the delivery unit can apply a news-specific delivery algorithm to news-related audio content. Similarly, the delivery unit can apply an entertainment-specific delivery algorithm to entertainment-related audio content. For example, the delivery unit can apply a sports-specific delivery algorithm to sports-related audio content. This allows for the provision of highly accurate information by applying the appropriate delivery algorithm according to the category of the audio content. Some or all of the above processing in the delivery unit may be performed using AI, or without AI. For example, the delivery unit can input audio content category data into AI and have the AI ​​perform the application of the delivery algorithm.

[0049] The distribution department can determine the priority of audio content distribution based on the submission date of the content. For example, the distribution department will prioritize the distribution of recent audio content. The distribution department can also provide past audio content as needed. For example, the distribution department will prioritize the distribution of audio content from specific events or campaign periods. This allows information to be provided at the appropriate time by determining the priority of distribution based on the submission date of the audio content. Some or all of the above processing in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input audio content submission date data into AI and have the AI ​​perform the determination of distribution priority.

[0050] The delivery unit can adjust the order of delivery based on the relevance of the audio content. For example, the delivery unit may prioritize audio content related to the user's current interests. The delivery unit may also prioritize audio content that is highly relevant to the user's past viewing history. For example, the delivery unit may prioritize particularly important content from the user's viewing history. By adjusting the order of delivery based on the relevance of the audio content, more relevant information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit may input audio content relevance data into AI and have the AI ​​perform the adjustment of the delivery order.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can prioritize the collection of highly relevant history by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of news and event information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of tourist information and local news from their travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of local news and regional information. This allows for the priority collection of highly relevant information by considering 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 an AI and have the AI ​​perform the collection of highly relevant history.

[0053] The collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the collection unit prioritizes collecting viewing history related to content shared by the user on social media. The collection unit can also collect viewing history based on the content posted by accounts that the user follows. For example, the collection unit collects viewing history related to topics in online communities that the user participates in. This allows for the collection of relevant viewing history by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant history.

[0054] The distribution department can determine the priority of audio content distribution based on the submission date of the content. For example, the distribution department will prioritize the distribution of recent audio content. The distribution department can also provide past audio content as needed. For example, the distribution department will prioritize the distribution of audio content from specific events or campaign periods. This allows information to be provided at the appropriate time by determining the priority of distribution based on the submission date of the audio content. Some or all of the above processing in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input audio content submission date data into AI and have the AI ​​perform the determination of distribution priority.

[0055] The analysis unit can apply different analysis algorithms depending on the category of the viewing history during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to news-related viewing history. Similarly, the analysis unit can apply an entertainment-specific analysis algorithm to entertainment-related viewing history. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related viewing history. This allows for the application of an appropriate analysis algorithm according to the category of the viewing history, thereby providing highly accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input viewing history category data into AI and have the AI ​​perform the application of the analysis algorithm.

[0056] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a news-specific generation algorithm to news-related content. Similarly, it can apply an entertainment-specific generation algorithm to entertainment-related content. For example, it can apply a sports-specific generation algorithm to sports-related content. This allows for the provision of highly accurate operating programs by applying the appropriate generation algorithm according to the content category. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input content category data into the AI ​​and have the AI ​​perform the application of the generation algorithm.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects information on the user's viewing history and selected personalities. For example, it collects viewing history such as the type of content the user watched, viewing time, and viewing frequency, as well as information such as the profile and past activity history of the personalities selected by the user. The collected data is stored on a server and provided to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit and selects content based on the user's interests. For example, it analyzes viewing history data to identify trends in content that the user is interested in. It can also analyze personality information to understand the personality traits that the user prefers. Viewing history data is input into a machine learning algorithm to predict the user's interests. Step 3: The generation unit uses a speech generation AI to transcribe and summarize the content selected by the analysis unit, and generates an action program for the personality to read aloud. For example, it can summarize a news article and generate a script for the personality to read aloud. It can also summarize questions and answers from a knowledge-sharing site and generate an action program for reading them aloud in a DJ style. The text of the news article is input into the speech generation AI, and the summarized audio data is generated. Step 4: The delivery unit provides the audio content generated by the generation unit to the user. For example, the audio content can be streamed so that the user can listen to it in real time. Alternatively, the audio content can be made downloadable so that the user can listen to it offline. The audio content can be provided to the user through a web application or a mobile application.

[0059] (Example of form 2) The radio service according to an embodiment of the present invention is a radio service that allows users to listen to content owned by the SB Group (mainly content from e-commerce sites) 24 hours a day. This radio service allows users to choose a personality created by a voice-generating AI that they have selected or trained themselves. Personalities can be selected from a variety of models, including real people. The content that can be listened to is mainly B2C content from e-commerce sites, and personalized content is delivered on demand 24 hours a day. Personalization is performed based on past listening history, etc. For example, users can select or train their favorite personalities. For example, they can choose from models such as announcers, singers, presenters / MCs, voice actors, DJs, comedians, and idols. This allows users to enjoy a special radio experience just for them. Next, the content to be listened to is selected from a variety of content owned by e-commerce sites. For example, this includes news, entertainment, sports, shopping, weather forecasts, etc. This content is transcribed and summarized by the voice-generating AI and read aloud by the personality. For example, it can summarize questions and answers from knowledge-sharing sites and read them aloud in a DJ-style format. Furthermore, content is personalized based on the user's past viewing history. This allows users to watch content tailored to their interests and preferences on demand, 24 hours a day. For example, if a user has watched a lot of news in the past, news content will be prioritized. Personalities can also be linked with e-commerce sites, allowing for advertising and creation / sale by creators, similar to stamps. This enables monetization of personalities. For example, users can "get" personalities by adding them as friends. This system allows users to listen to any content from e-commerce sites, read by voice-generating AI, 24 hours a day. This allows users to enjoy content without looking, enriching their time while doing other things. For example, it can be used in various situations such as commuting, driving, bathing, and exercising.This allows radio services to provide personalized audio content based on users' listening history and information about selected personalities.

[0060] The radio service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on the user's listening history and selected personalities. For example, the collection unit collects listening history such as the type of content the user has listened to, the listening time, and the frequency of listening. The collection unit can also collect information such as the profile and past activity history of personalities selected by the user. For example, the collection unit stores data of the content the user has listened to on a server and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and selects content based on the user's interests. For example, the analysis unit analyzes the listening history data to identify trends in content that the user is interested in. The analysis unit can also analyze personality information to understand the characteristics of personalities that the user prefers. For example, the analysis unit inputs the listening history data into a machine learning algorithm to predict the user's interests. The generation unit transcribes and summarizes the content selected by the analysis unit using a speech generation AI and generates an action program in which a personality reads it aloud. The generation unit, for example, summarizes news articles and generates scripts for a personality to read aloud. The generation unit can also summarize questions and answers from knowledge-sharing sites and generate a program to read them aloud in a DJ-like style. For example, the generation unit inputs the text of a news article into a speech generation AI and generates summarized audio data. The delivery unit provides the audio content generated by the generation unit to the user. The delivery unit, for example, streams the audio content, allowing users to listen in real time. The delivery unit can also make the audio content downloadable, allowing users to listen offline. For example, the delivery unit provides the audio content to the user through a web application or mobile application. This allows the radio service according to the embodiment to provide personalized audio content based on the user's listening history and information about selected personalities.

[0061] The data collection unit collects information on users' viewing history and selected personalities. Specifically, it collects detailed viewing history, including the type of content viewed, viewing time, and viewing frequency. For example, it can understand what types of content users prefer to watch, such as music, news, and podcasts, and at what times of day their viewing time is concentrated. Furthermore, it records how often specific content is viewed. This makes it possible to analyze users' viewing patterns in detail. The data collection unit also collects information on the profiles and past activity history of personalities selected by users. For example, it collects information on a personality's past appearances, their content, and fan reactions to understand what kind of personalities users prefer. This information is stored on a server and provided to the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and generation units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis unit analyzes the information collected by the data collection unit and selects content based on user interests. Specifically, it analyzes viewing history data to identify trends in content that users are interested in. For example, it inputs viewing history data into a machine learning algorithm to predict user interests. The machine learning algorithm learns what kind of content users like based on past viewing history and predicts what kind of content should be provided in the future. The analysis unit can also analyze personality information to understand the characteristics of personalities that users prefer. For example, it analyzes a personality's past activity history and fan reactions to identify what kind of personality users prefer. This allows the analysis unit to select the most suitable content based on user interests. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict fluctuations in the popularity of specific content based on past viewing data and formulate future content provision plans. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0063] The generation unit uses a speech generation AI to transcribe and summarize content selected by the analysis unit, and generates an action program for a personality to read aloud. Specifically, it summarizes news articles and generates a script for the personality to read aloud. For example, the text of a news article is input to the speech generation AI, and summarized audio data is generated. This speech generation AI uses natural language processing technology to analyze the input text, extract important information, and summarize it. The generation unit can also summarize questions and answers from knowledge-sharing sites and generate an action program for reading them aloud in a DJ style. For example, questions and answers from a knowledge-sharing site are input to the speech generation AI, and summarized audio data is generated. This audio data is used as a script for the personality to read aloud in a DJ style. Furthermore, the generation unit can also generate customized content based on the user's interests. For example, if a user likes a particular genre of music, the generation unit summarizes news and information related to that genre and generates a script for the personality to read aloud. This allows the generation unit to generate personalized content tailored to the user's interests and provide it to the delivery unit.

[0064] The delivery unit provides users with audio content generated by the generation unit. Specifically, it streams audio content, allowing users to listen to it in real time. For example, it provides audio content to users through web applications and mobile applications. Users can use these applications to listen to audio content in real time. The delivery unit can also make audio content downloadable, allowing users to listen to it offline. For example, it can download audio content so that it can be listened to even in environments without an internet connection. Furthermore, the delivery unit can collect user feedback and continuously improve the quality and delivery method of the content. For example, it can collect ratings and comments on content viewed by users and use this to improve the quality of the content. In addition, the delivery unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the delivery unit to provide audio content to users quickly and reliably, improving user satisfaction.

[0065] The generation unit can generate an action program that summarizes questions and answers from a knowledge-sharing site and reads them aloud in a DJ style. For example, the generation unit can collect and summarize questions and answers from a knowledge-sharing site. For example, the generation unit can analyze the text data of questions and answers and extract important information. The generation unit can also generate a DJ-style reading script based on the extracted information. For example, the generation unit can input the text of questions and answers into a speech generation AI and generate DJ-style audio data. This allows the user to experience a new way of reading information from a knowledge-sharing site in a DJ style. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the text data of questions and answers from a knowledge-sharing site into a generation AI and have the generation AI perform the summarization and generation of the reading script.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of viewing history 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. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. 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. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This reduces the burden on the user by adjusting the timing of viewing history collection 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 AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0067] The data collection unit can analyze a user's past viewing history and select the optimal data collection method. For example, the data collection unit can analyze trends in content the user has previously viewed and prioritize collecting similar content. Furthermore, if a user tends to watch content during specific time periods, the data collection unit can adjust its collection accordingly. For instance, the data collection unit can re-collect content that the user interrupted, allowing them to resume viewing from where they left off. This allows the optimal data collection method to be selected by analyzing past viewing history. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past viewing history data into an AI and have the AI ​​select the optimal data collection method.

[0068] The data collection unit can filter viewing history based on the user's current lifestyle and areas of interest. For example, if the user is at work, the data collection unit will prioritize collecting business-related content. It can also collect entertainment and hobby-related content if the user is on holiday. For instance, if the user is attending a specific event, the data collection unit will collect content related to that event. This allows for the collection of highly relevant information by filtering viewing history based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user lifestyle data into an AI and have the AI ​​perform the filtering.

[0069] The data collection unit can estimate the user's emotions and determine the priority of viewing history to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting entertainment-related viewing history. Similarly, if the user is stressed, the data collection unit may prioritize collecting viewing history related to relaxation or wellness. For example, if the user is excited, the data collection unit may prioritize collecting action or sports-related viewing history. This allows for the collection of more relevant information by prioritizing viewing history based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of viewing history.

[0070] The data collection unit can prioritize the collection of highly relevant viewing history by considering the user's geographical location when collecting viewing history. For example, if the user is in a specific region, the data collection unit will prioritize collecting news and event information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting tourist information and local news from their travel destination. For example, if the user is at home, the data collection unit will prioritize collecting local news and regional information. This allows for the priority collection of highly relevant information by considering 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 an AI and have the AI ​​perform the collection of highly relevant viewing history.

[0071] The collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the collection unit prioritizes collecting viewing history related to content shared by the user on social media. The collection unit can also collect viewing history based on the content posted by accounts that the user follows. For example, the collection unit collects viewing history related to topics in online communities that the user participates in. This allows for the collection of relevant viewing history by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant history.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will display the analysis results in a calm tone. Conversely, if the user is stressed, the analysis unit can display the analysis results concisely and clearly. For example, if the user is agitated, the analysis unit will display the analysis results in a visually stimulating format. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the viewing history during the analysis. For example, the analysis unit can perform a detailed analysis on important viewing history to provide deeper insights. It can also perform a basic analysis on general viewing history to provide concise information. For example, the analysis unit can perform a customized analysis on viewing history that the user is particularly interested in. This allows for the provision of appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the viewing history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input viewing history importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the viewing history during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to news-related viewing history. Similarly, the analysis unit can apply an entertainment-specific analysis algorithm to entertainment-related viewing history. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related viewing history. This allows for the application of an appropriate analysis algorithm according to the category of the viewing history, thereby providing highly accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input viewing history category data into AI and have the AI ​​perform the application of the analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize entertainment-related analysis. Similarly, if the user is stressed, the analysis unit may prioritize relaxation and healing-related analysis. For example, if the user is excited, the analysis unit may prioritize action and sports-related analysis. This allows for more appropriate analysis results by prioritizing analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​determine the priority of analysis.

[0076] The analysis unit can determine the priority of analysis based on the submission date of viewing history. For example, the analysis unit will prioritize the analysis of recent viewing history. The analysis unit can also analyze past viewing history as needed. For example, the analysis unit will prioritize the analysis of viewing history during a specific event or campaign period. By determining the priority of analysis based on the submission date of viewing history, the analysis results can be provided at the appropriate time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input viewing history submission date data into AI and have the AI ​​perform the determination of analysis priorities.

[0077] The analysis unit can adjust the order of analysis based on the relevance of viewing history during the analysis process. For example, the analysis unit may prioritize analyzing viewing history related to the user's current interests. It can also prioritize analyzing viewing history that is highly relevant to the user's past viewing history. For example, the analysis unit may prioritize analyzing particularly important content in the user's viewing history. By adjusting the order of analysis based on the relevance of viewing history, more relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input viewing history relevance data into AI and have the AI ​​perform the adjustment of the analysis order.

[0078] The generation unit can estimate the user's emotions and adjust the way it expresses the generated action program based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an action program that proceeds in a calm tone. It can also generate a quick and concise action program if the user is in a hurry. For example, if the user is excited, the generation unit can generate an action program with visually stimulating effects. This allows for the provision of more appropriate action programs by adjusting the expression of the action program based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the expression of the action program.

[0079] The generation unit can adjust the level of detail of the generated content based on its importance. For example, the generation unit can generate a detailed action program for important content. It can also generate a basic action program for general content. For example, the generation unit can generate a customized action program for content of particular interest to the user. This allows for the provision of appropriate action programs by adjusting the level of detail of the generated content based on its importance. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the generated content.

[0080] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a news-specific generation algorithm to news-related content. Similarly, it can apply an entertainment-specific generation algorithm to entertainment-related content. For example, it can apply a sports-specific generation algorithm to sports-related content. This allows for the provision of highly accurate operating programs by applying the appropriate generation algorithm according to the content category. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input content category data into the AI ​​and have the AI ​​perform the application of the generation algorithm.

[0081] The generation unit can estimate the user's emotions and determine the priority of the action programs to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize generating entertainment-related action programs. Similarly, if the user is stressed, the generation unit may prioritize generating relaxation or healing-related action programs. For example, if the user is excited, the generation unit may prioritize generating action or sports-related action programs. This allows for the provision of more appropriate action programs by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​determine the priority of the action programs.

[0082] The generation unit can determine the generation priority based on the content submission date during generation. For example, the generation unit will prioritize generating action programs for recent content. The generation unit can also generate action programs for past content as needed. For example, the generation unit will prioritize generating action programs for content from a specific event or campaign period. This allows for the provision of action programs at the appropriate time by determining the generation priority based on the content submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input content submission date data into AI and have the AI ​​determine the generation priority.

[0083] The generation unit can adjust the generation order based on the relevance of the content during generation. For example, the generation unit can prioritize generating content related to the user's current interests. It can also prioritize generating content that is highly relevant to the user's past viewing history. For example, the generation unit can prioritize generating content that is particularly important in the user's viewing history. By adjusting the generation order based on the relevance of the content, it is possible to provide a more relevant program. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input content relevance data into AI and have AI perform the adjustment of the generation order.

[0084] The service provider can estimate the user's emotions and adjust the presentation of the audio content based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver audio content in a calm tone. If the user is stressed, the service provider can also deliver concise and clear audio content. For example, if the user is excited, the service provider can deliver audio content with visually stimulating effects. This allows for the delivery of more appropriate audio content by adjusting its presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 service provider may be performed using AI, or not. For example, the service provider can input user emotion data into an AI and have the AI ​​adjust the presentation of the audio content.

[0085] The delivery unit can adjust the level of detail provided based on the importance of the audio content at the time of delivery. For example, the delivery unit will provide a detailed explanation for important audio content. The delivery unit can also provide basic information for general audio content. For example, the delivery unit will provide a customized delivery for audio content that the user is particularly interested in. This allows for the provision of appropriate information by adjusting the level of detail based on the importance of the audio content. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input audio content importance data into AI and have the AI ​​perform the adjustment of the level of detail of the delivery.

[0086] The delivery unit can apply different delivery algorithms depending on the category of the audio content at the time of delivery. For example, the delivery unit can apply a news-specific delivery algorithm to news-related audio content. Similarly, the delivery unit can apply an entertainment-specific delivery algorithm to entertainment-related audio content. For example, the delivery unit can apply a sports-specific delivery algorithm to sports-related audio content. This allows for the provision of highly accurate information by applying the appropriate delivery algorithm according to the category of the audio content. Some or all of the above processing in the delivery unit may be performed using AI, or without AI. For example, the delivery unit can input audio content category data into AI and have the AI ​​perform the application of the delivery algorithm.

[0087] The service provider can estimate the user's emotions and determine the priority of audio content to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize entertainment-related audio content. Similarly, if the user is stressed, the service provider may prioritize relaxation or healing-related audio content. For example, if the user is excited, the service provider may prioritize action or sports-related audio content. This allows for the provision of more appropriate information by prioritizing audio content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into an AI and have the AI ​​determine the priority of audio content.

[0088] The distribution department can determine the priority of audio content distribution based on the submission date of the content. For example, the distribution department will prioritize the distribution of recent audio content. The distribution department can also provide past audio content as needed. For example, the distribution department will prioritize the distribution of audio content from specific events or campaign periods. This allows information to be provided at the appropriate time by determining the priority of distribution based on the submission date of the audio content. Some or all of the above processing in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input audio content submission date data into AI and have the AI ​​perform the determination of distribution priority.

[0089] The delivery unit can adjust the order of delivery based on the relevance of the audio content. For example, the delivery unit may prioritize audio content related to the user's current interests. The delivery unit may also prioritize audio content that is highly relevant to the user's past viewing history. For example, the delivery unit may prioritize particularly important content from the user's viewing history. By adjusting the order of delivery based on the relevance of the audio content, more relevant information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit may input audio content relevance data into AI and have the AI ​​perform the adjustment of the delivery order.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will display the analysis results in a calm tone. Conversely, if the user is stressed, the analysis unit can display the analysis results concisely and clearly. For example, if the user is agitated, the analysis unit will display the analysis results in a visually stimulating format. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the presentation of the analysis.

[0092] The data collection unit can prioritize the collection of highly relevant history by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of news and event information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of tourist information and local news from their travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of local news and regional information. This allows for the priority collection of highly relevant information by considering 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 an AI and have the AI ​​perform the collection of highly relevant history.

[0093] The service provider can estimate the user's emotions and adjust the presentation of the audio content based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver audio content in a calm tone. If the user is stressed, the service provider can also deliver concise and clear audio content. For example, if the user is excited, the service provider can deliver audio content with visually stimulating effects. This allows for the delivery of more appropriate audio content by adjusting its presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 service provider may be performed using AI, or not. For example, the service provider can input user emotion data into an AI and have the AI ​​adjust the presentation of the audio content.

[0094] The collection unit can analyze a user's social media activity and collect relevant history when collecting viewing history. For example, the collection unit prioritizes collecting viewing history related to content shared by the user on social media. The collection unit can also collect viewing history based on the content posted by accounts that the user follows. For example, the collection unit collects viewing history related to topics in online communities that the user participates in. This allows for the collection of relevant viewing history by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant history.

[0095] The generation unit can estimate the user's emotions and adjust the way it expresses the generated action program based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an action program that proceeds in a calm tone. It can also generate a quick and concise action program if the user is in a hurry. For example, if the user is excited, the generation unit can generate an action program with visually stimulating effects. This allows for the provision of more appropriate action programs by adjusting the expression of the action program based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the expression of the action program.

[0096] The distribution department can determine the priority of audio content distribution based on the submission date of the content. For example, the distribution department will prioritize the distribution of recent audio content. The distribution department can also provide past audio content as needed. For example, the distribution department will prioritize the distribution of audio content from specific events or campaign periods. This allows information to be provided at the appropriate time by determining the priority of distribution based on the submission date of the audio content. Some or all of the above processing in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input audio content submission date data into AI and have the AI ​​perform the determination of distribution priority.

[0097] The analysis unit can apply different analysis algorithms depending on the category of the viewing history during analysis. For example, the analysis unit can apply a news-specific analysis algorithm to news-related viewing history. Similarly, the analysis unit can apply an entertainment-specific analysis algorithm to entertainment-related viewing history. For example, the analysis unit can apply a sports-specific analysis algorithm to sports-related viewing history. This allows for the application of an appropriate analysis algorithm according to the category of the viewing history, thereby providing highly accurate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input viewing history category data into AI and have the AI ​​perform the application of the analysis algorithm.

[0098] The data collection unit can estimate the user's emotions and determine the priority of viewing history to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting entertainment-related viewing history. Similarly, if the user is stressed, the data collection unit may prioritize collecting viewing history related to relaxation or wellness. For example, if the user is excited, the data collection unit may prioritize collecting action or sports-related viewing history. This allows for the collection of more relevant information by prioritizing viewing history based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of viewing history.

[0099] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a news-specific generation algorithm to news-related content. Similarly, it can apply an entertainment-specific generation algorithm to entertainment-related content. For example, it can apply a sports-specific generation algorithm to sports-related content. This allows for the provision of highly accurate operating programs by applying the appropriate generation algorithm according to the content category. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input content category data into the AI ​​and have the AI ​​perform the application of the generation algorithm.

[0100] The service provider can estimate the user's emotions and determine the priority of audio content to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize entertainment-related audio content. Similarly, if the user is stressed, the service provider may prioritize relaxation or healing-related audio content. For example, if the user is excited, the service provider may prioritize action or sports-related audio content. This allows for the provision of more appropriate information by prioritizing audio content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into an AI and have the AI ​​determine the priority of audio content.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects information on the user's viewing history and selected personalities. For example, it collects viewing history such as the type of content the user watched, viewing time, and viewing frequency, as well as information such as the profile and past activity history of the personalities selected by the user. The collected data is stored on a server and provided to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit and selects content based on the user's interests. For example, it analyzes viewing history data to identify trends in content that the user is interested in. It can also analyze personality information to understand the personality traits that the user prefers. Viewing history data is input into a machine learning algorithm to predict the user's interests. Step 3: The generation unit uses a speech generation AI to transcribe and summarize the content selected by the analysis unit, and generates an action program for the personality to read aloud. For example, it can summarize a news article and generate a script for the personality to read aloud. It can also summarize questions and answers from a knowledge-sharing site and generate an action program for reading them aloud in a DJ style. The text of the news article is input into the speech generation AI, and the summarized audio data is generated. Step 4: The delivery unit provides the audio content generated by the generation unit to the user. For example, the audio content can be streamed so that the user can listen to it in real time. Alternatively, the audio content can be made downloadable so that the user can listen to it offline. The audio content can be provided to the user through a web application or a mobile application.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect information on the user's viewing history and selected personalities using the control unit 46A of the smart device 14. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and selects content based on the user's interests. The generation unit, for example, transcribes and summarizes the content selected by the identification processing unit 290 of the data processing unit 12 using a speech generation AI and generates an action program in which the personality reads aloud. The provision unit provides the user with the audio content generated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect information on the user's viewing history and selected personalities by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information by the identification processing unit 290 of the data processing unit 12 and selects content based on the user's interests. The generation unit, for example, transcribes and summarizes the content selected by the identification processing unit 290 of the data processing unit 12 into text using a speech generation AI and generates an operation program in which the personality reads aloud. The provision unit, for example, provides the user with the audio content generated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect information on the user's viewing history and selected personalities using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12 and selects content based on the user's interests. The generation unit, for example, transcribes and summarizes the content selected by the identification processing unit 290 of the data processing unit 12 using a speech generation AI and generates an action program in which the personality reads aloud. The provision unit provides the user with the audio content generated by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect information on the user's viewing history and selected personalities by the control unit 46A of the robot 414. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing unit 12 and selects content based on the user's interests. The generation unit, for example, transcribes and summarizes the content selected by the specific processing unit 290 of the data processing unit 12 into text using a speech generation AI and generates an action program for the personality to read aloud. The provision unit provides the user with the audio content generated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A collection unit that collects information on the user's viewing history and selected personalities, An analysis unit analyzes the information collected by the aforementioned collection unit and selects content based on the user's interests and preferences. The generation unit generates an action program in which a personality reads aloud the content selected by the analysis unit, which is then converted into text and summarized by a speech generation AI. The system includes a providing unit that provides the audio content generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is Generate a program that summarizes questions and answers from knowledge-sharing websites and reads them aloud in a DJ-like style. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of viewing history collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past viewing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When collecting viewing history, 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 6) The aforementioned collection unit is It estimates the user's emotions and determines the priority of viewing history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting viewing history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting viewing history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the viewing history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts how the generated behavioral program is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, different generation algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and determines the priority of the action programs to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the generation priority is determined based on the content submission date. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the generation order is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way audio content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the audio content, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing content, different delivery algorithms are applied depending on the category of the audio content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the audio content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, the priority of provision will be determined based on the submission timing of the audio content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When delivering content, the delivery order will be adjusted based on the relevance of the audio content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 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 on the user's viewing history and selected personalities, An analysis unit analyzes the information collected by the aforementioned collection unit and selects content based on the user's interests and preferences. The generation unit generates an action program in which the personality reads aloud the content selected by the analysis unit, which is then converted into text and summarized by a speech generation AI. The system includes a providing unit that provides the audio content generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Generate a program that summarizes questions and answers from knowledge-sharing websites and reads them aloud in a DJ-like style. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of viewing history collection based on the estimated user emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past viewing history and select the optimal data collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting viewing history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and determines the priority of viewing history to collect based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting viewing history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is When collecting viewing history, the system analyzes the user's social media activity and collects relevant history. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of viewing history. The system according to feature 1.

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