System

Abstract

An object of the system according to the embodiment is to integrally convert and distribute various media formats and to recommend highly relevant content.SOLUTION: A system includes a media conversion part, a broadcast cooperation part, a content recommendation part, and an abnormality detection part. The media conversion unit mutually converts media formats of voice, text, and image. The broadcast cooperation unit distributes the media converted by the media conversion unit in cooperation with the broadcast system. The content recommendation unit recommends a highly relevant content based on a viewing history and a search history of the user. The abnormality detection unit analyzes the content and issues a warning when there is an abnormality.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately integratedly converted and distributed various media formats and recommended highly relevant content, so there is room for improvement.

[0005] The system according to the embodiment aims to convert and distribute various media formats in an integrated manner and recommend highly relevant content. [Means for solving the problem]

[0006] The system according to the embodiment includes a media conversion unit, a broadcast cooperation unit, a content recommendation unit, and an anomaly detection unit. The media conversion unit converts audio, text, and image media formats into each other. The broadcast cooperation unit distributes the media converted by the media conversion unit in cooperation with a broadcast system. The content recommendation unit recommends highly relevant content based on the user's viewing history and search history. The anomaly detection unit analyzes the content and issues a warning if an anomaly is detected. [Effects of the Invention]

[0007] The system according to the embodiment can convert and distribute various media formats in an integrated manner and recommend highly relevant content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A Universal Media Creator (UMC) system according to an embodiment of the present invention is a system that converts media formats such as audio, text, and images into each other, distributes information widely in cooperation with a broadcasting system, recommends highly relevant content, and detects anomalous content. As a result, the UMC system can convert various media formats into each other, distribute information widely, recommend highly relevant content, and detect anomalous content.

[0029] The UMC system according to the embodiment includes a media conversion unit, a broadcast cooperation unit, a content recommendation unit, and an anomaly detection unit. The media conversion unit converts media formats such as audio, text, and images into each other. For example, the media conversion unit converts audio into text using speech recognition technology. The media conversion unit can also translate text and convert it into audio in another language using speech synthesis technology. The media conversion unit can also convert images into text using image recognition technology. The broadcast cooperation unit distributes the media converted by the media conversion unit in cooperation with a broadcast system. For example, the broadcast cooperation unit converts audio from a news broadcast into text in real time, converts the text into audio in another language, and delivers it to viewers overseas. The content recommendation unit recommends highly relevant content based on a user's viewing history and search history. For example, if a user reads a specific news article, the content recommendation unit recommends other news articles and videos related to that article. The anomaly detection unit analyzes the content and issues an alert if an anomaly is detected. For example, the anomaly detection unit can detect and alert users if a news article or video contains inappropriate content. This allows the UMC system to convert between various media formats, distribute information widely, recommend relevant content, and detect anomalous content.

[0030] The media conversion unit can automatically remove background sounds or noise from the audio data and convert clear audio into text. The media conversion unit, for example, introduces technology for automatically removing background sounds or noise from audio data and converting clear audio into text. For example, the media conversion unit analyzes audio data recorded in a noisy environment, removes noise, and converts the audio into text. The media conversion unit can also analyze audio data from a meeting, remove background sounds, and convert the speech into text. The media conversion unit can also analyze audio data recorded outdoors, remove wind sounds and traffic sounds, and convert the speech into text. This allows accurate text to be generated by removing background sounds and noise and converting clear audio into text.

[0031] The media conversion unit can analyze the characteristics of the speaker of the voice data and perform text conversion based on the results. The media conversion unit, for example, analyzes the characteristics of the speaker of the voice data and performs text conversion accordingly. For example, the voice data of a young speaker is converted into text using casual expressions. The media conversion unit also converts, for example, the voice data of a female speaker into text using feminine expressions. The media conversion unit also converts, for example, the voice data of a speaker with a particular accent into text that reflects that accent. In this way, more natural text can be generated by performing text conversion according to the speaker's characteristics.

[0032] The media conversion unit can provide information for the hearing impaired by converting audio data into sign language video at the same time as converting audio data into text. For example, the media conversion unit can provide content for the hearing impaired by converting audio data into sign language video at the same time as converting audio data into text. For example, the media conversion unit converts the audio of a news broadcast into text and converts it into sign language video. For example, the media conversion unit converts the audio of a lecture into text and converts it into sign language video. For example, the media conversion unit converts the audio of a movie into text and converts it into sign language video. In this way, content for the hearing impaired can be provided by converting audio data into sign language video.

[0033] The media conversion unit can analyze text data and automatically generate related images or video clips to provide visual complementary information. For example, the media conversion unit analyzes text data and automatically generates related images or video clips to provide visual complementary information. For example, the media conversion unit analyzes the text of a news article and generates related images or video clips. Furthermore, the media conversion unit analyzes the text of a lecture and generates related charts and video clips. Furthermore, the media conversion unit analyzes a movie script and generates images and video clips of related scenes. In this way, visual complementary information related to the text data is provided to help understand the information.

[0034] The broadcast collaboration unit can analyze audio data from the broadcast system in real time and automatically highlight specific keywords or phrases. The broadcast collaboration unit adds, for example, a function to analyze audio data from the broadcast system in real time and automatically highlight specific keywords or phrases. For example, the broadcast collaboration unit highlights important keywords during a news broadcast. The broadcast collaboration unit also highlights important phrases during a lecture. The broadcast collaboration unit also highlights important remarks during an interview. In this way, the audio data from the broadcast system is analyzed in real time and important information is visually emphasized, thereby helping viewers understand the content.

[0035] The broadcast collaboration unit can analyze audio data from the broadcasting system, summarize important information, and convert it into text. The broadcast collaboration unit, for example, introduces technology that analyzes audio data from the broadcasting system and summarizes important information and converts it into text. For example, the broadcast collaboration unit automatically summarizes the main points of a news broadcast and converts it into text. The broadcast collaboration unit also automatically summarizes the main points of a lecture and converts it into text. The broadcast collaboration unit also automatically summarizes the main points of an interview and converts it into text. In this way, by summarizing and converting audio data from the broadcasting system into text, important information can be efficiently provided to viewers.

[0036] The broadcast collaboration unit can analyze audio data from the broadcasting system, automatically collect related social media posts, and display viewer reactions in real time. The broadcast collaboration unit, for example, analyzes audio data from the broadcasting system, automatically collects related social media posts, and displays viewer reactions in real time. For example, it displays related tweets during a news broadcast. The broadcast collaboration unit also displays related Facebook posts during a lecture, for example. The broadcast collaboration unit also displays related Instagram posts during an interview, for example. In this way, by analyzing audio data from the broadcasting system and displaying related social media posts in real time, viewer reactions can be immediately grasped.

[0037] The broadcast collaboration unit can analyze audio data from the broadcast system and automatically generate related news articles or blog posts to provide to viewers. The broadcast collaboration unit, for example, analyzes audio data from the broadcast system and automatically generates related news articles or blog posts to provide to viewers. For example, the broadcast collaboration unit generates related articles based on the content of a news broadcast. The broadcast collaboration unit also generates related blog posts based on the content of a lecture, for example. The broadcast collaboration unit also generates related articles based on the content of an interview, for example. In this way, by analyzing audio data from the broadcast system and automatically generating related news articles or blog posts, additional information can be provided to viewers.

[0038] The content recommendation unit can analyze a user's viewing history and search history and suggest content based on viewing time or viewing frequency. The content recommendation unit introduces, for example, a technology that analyzes a user's viewing history and search history and recommends content based on viewing time and viewing frequency. For example, the content recommendation unit recommends series content to users who watch for long periods of time. Furthermore, the content recommendation unit recommends short videos to users who watch for short periods of time. Furthermore, the content recommendation unit recommends the latest content to users who watch frequently. In this way, the user's viewing experience is optimized by recommending content based on viewing time and viewing frequency.

[0039] The content recommendation unit can analyze a user's viewing history and search history and suggest content based on the viewer's interests. The content recommendation unit adds a function to analyze a user's viewing history and search history and recommend content based on the viewer's interests. For example, to a user who is interested in a particular genre, the content recommendation unit recommends new releases in that genre. For example, to a user who is interested in a particular theme, the content recommendation unit recommends documentaries related to that theme. For example, to a user who is interested in works starring a particular actor, the content recommendation unit recommends other works by that actor. In this way, content is recommended based on the viewer's interests, improving viewer satisfaction.

[0040] The content recommendation unit can analyze a user's viewing history and search history and suggest related offline events or seminars. The content recommendation unit, for example, analyzes a user's viewing history and search history and recommends related offline events and seminars. For example, to a user who is interested in a particular technology, the content recommendation unit recommends a seminar related to that technology. Furthermore, to a user who is interested in a particular movie, the content recommendation unit recommends an event showing that movie. Furthermore, to a user who is interested in a particular author, the content recommendation unit recommends a lecture by that author. In this way, by recommending related offline events and seminars based on the viewing history and search history, it is easier to attract the user's interest.

[0041] The content recommendation unit can analyze a user's viewing history and search history and suggest related books or papers. The content recommendation unit adds a function of, for example, analyzing a user's viewing history and search history and recommending related books and papers. For example, to a user who is interested in a particular theme, the content recommendation unit recommends books related to that theme. Furthermore, for example, to a user who is interested in a particular technology, the content recommendation unit recommends papers related to that technology. Furthermore, for example, to a user who is interested in a particular historical event, the content recommendation unit recommends books related to that event. In this way, the user's knowledge is deepened by recommending related books and papers based on the viewing history and search history.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The UMC system may further include a visual customization unit that customizes content based on the user's visual preferences. For example, the visual customization unit may analyze the user's preferred color tone and design style and change the interface color and layout accordingly. If the user prefers a particular visual effect, the visual customization unit may preferentially display content that includes that effect. Furthermore, the visual customization unit may automatically adjust the screen brightness and contrast to reduce the user's visual fatigue. This allows customization according to the user's visual preferences, providing a more comfortable viewing experience.

[0044] The UMC system may further include a health monitoring unit that monitors the user's health status and provides appropriate content. For example, the health monitoring unit may analyze the user's heart rate and stress level and recommend relaxing content based on the results. The health monitoring unit may also analyze the user's sleep patterns and provide content to improve sleep quality. Furthermore, the health monitoring unit may analyze the user's exercise volume and provide content to promote exercise. This allows the user's health to be supported by providing appropriate content according to the user's health status.

[0045] The UMC system may further include an education customization unit that customizes educational content based on the user's learning style. For example, if the user is a visual learner, the education customization unit may provide content that makes extensive use of diagrams and videos. Alternatively, if the user is an auditory learner, the education customization unit may provide content that emphasizes audio commentary. Furthermore, if the user is a hands-on learner, the education customization unit may provide content that includes interactive exercises and simulations. This maximizes learning effectiveness by providing educational content that matches the user's learning style.

[0046] The UMC system may further include a community recommendation unit that recommends related communities based on the user's hobbies and interests. For example, if the user is interested in a particular sport, the community recommendation unit may recommend online communities related to that sport. Alternatively, if the user has a particular hobby, the community recommendation unit may recommend forums and groups related to that hobby. Furthermore, if the user is interested in a particular topic, the community recommendation unit may recommend discussion groups related to that topic. This allows the user's social connections to be strengthened by recommending communities based on the user's hobbies and interests.

[0047] The UMC system may further include a podcast recommendation unit that recommends related podcasts based on the user's viewing history. For example, if the user has watched a movie of a particular genre, the podcast recommendation unit may recommend podcasts related to that genre. If the user is interested in a particular theme, the podcast recommendation unit may also recommend podcasts related to that theme. Furthermore, if the user is interested in a particular celebrity, the podcast recommendation unit may also recommend podcasts featuring that celebrity. This makes it easier to attract the user's interest by recommending related podcasts based on the user's viewing history.

[0048] The UMC system may further include an online course recommendation unit that recommends related online courses based on the user's viewing history. For example, if the user is interested in a particular technology, the online course recommendation unit may recommend online courses related to that technology. If the user wants to learn a particular language, the online course recommendation unit may also recommend online courses related to that language. Furthermore, if the user has a particular hobby, the online course recommendation unit may also recommend online courses related to that hobby. By recommending related online courses based on the user's viewing history, the user's motivation to learn can be increased.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The media conversion unit converts media formats such as voice, text, and images into each other. For example, voice can be converted into text using voice recognition technology, and the text can be translated and converted into voice in another language using voice synthesis technology. Images can also be converted into text using image recognition technology. Step 2: The broadcasting linkage unit distributes the media converted by the media conversion unit in cooperation with the broadcasting system. For example, it converts the audio of a news broadcast into text in real time, and then converts that text into audio in another language for delivery to overseas viewers. Step 3: The content recommendation unit recommends relevant content based on the user's viewing and search history. For example, if a user reads a particular news article, it will recommend other news articles and videos related to that article. Step 4: The anomaly detection unit analyzes the content and issues a warning if an anomaly is detected. For example, if a news article or video contains inappropriate content, the unit will detect the content and issue a warning.

[0051] (Example 2) A Universal Media Creator (UMC) system according to an embodiment of the present invention is a system that converts media formats such as audio, text, and images into each other, distributes information widely in cooperation with a broadcasting system, recommends highly relevant content, and detects anomalous content. As a result, the UMC system can convert various media formats into each other, distribute information widely, recommend highly relevant content, and detect anomalous content.

[0052] The UMC system according to the embodiment includes a media conversion unit, a broadcast cooperation unit, a content recommendation unit, and an anomaly detection unit. The media conversion unit converts media formats such as audio, text, and images into each other. For example, the media conversion unit converts audio into text using speech recognition technology. The media conversion unit can also translate text and convert it into audio in another language using speech synthesis technology. The media conversion unit can also convert images into text using image recognition technology. The broadcast cooperation unit distributes the media converted by the media conversion unit in cooperation with a broadcast system. For example, the broadcast cooperation unit converts audio from a news broadcast into text in real time, converts the text into audio in another language, and delivers it to viewers overseas. The content recommendation unit recommends highly relevant content based on a user's viewing history and search history. For example, if a user reads a specific news article, the content recommendation unit recommends other news articles and videos related to that article. The anomaly detection unit analyzes the content and issues an alert if an anomaly is detected. For example, the anomaly detection unit can detect and alert users if a news article or video contains inappropriate content. This allows the UMC system to convert between various media formats, distribute information widely, recommend relevant content, and detect anomalous content.

[0053] The media conversion unit can analyze the emotional tone of the voice data and perform text conversion based on the emotion. The media conversion unit, for example, analyzes the emotional tone of the voice data and performs text conversion according to the emotion. For example, voice data with a strong emotion of joy is converted into text using positive expressions. The media conversion unit also converts voice data with a strong emotion of sadness into text using expressions that reflect the emotion. The media conversion unit also converts voice data with a strong emotion of surprise into text using expressions that emphasize the emotion. In this way, by performing text conversion based on the emotional tone of the voice data, it is possible to generate text that reflects the emotion.

[0054] The media conversion unit can automatically remove background sounds or noise from the audio data and convert clear audio into text. The media conversion unit, for example, introduces technology for automatically removing background sounds or noise from audio data and converting clear audio into text. For example, the media conversion unit analyzes audio data recorded in a noisy environment, removes noise, and converts the audio into text. The media conversion unit can also analyze audio data from a meeting, remove background sounds, and convert the speech into text. The media conversion unit can also analyze audio data recorded outdoors, remove wind sounds and traffic sounds, and convert the speech into text. This allows accurate text to be generated by removing background sounds and noise and converting clear audio into text.

[0055] The media conversion unit can analyze the characteristics of the speaker of the voice data and perform text conversion based on the results. The media conversion unit, for example, analyzes the characteristics of the speaker of the voice data and performs text conversion accordingly. For example, the voice data of a young speaker is converted into text using casual expressions. The media conversion unit also converts, for example, the voice data of a female speaker into text using feminine expressions. The media conversion unit also converts, for example, the voice data of a speaker with a particular accent into text that reflects that accent. In this way, more natural text can be generated by performing text conversion according to the speaker's characteristics.

[0056] The media conversion unit can provide information for the hearing impaired by converting audio data into sign language video at the same time as converting audio data into text. For example, the media conversion unit can provide content for the hearing impaired by converting audio data into sign language video at the same time as converting audio data into text. For example, the media conversion unit converts the audio of a news broadcast into text and converts it into sign language video. For example, the media conversion unit converts the audio of a lecture into text and converts it into sign language video. For example, the media conversion unit converts the audio of a movie into text and converts it into sign language video. In this way, content for the hearing impaired can be provided by converting audio data into sign language video.

[0057] The media conversion unit can analyze text data and automatically generate related images or video clips to provide visual complementary information. For example, the media conversion unit analyzes text data and automatically generates related images or video clips to provide visual complementary information. For example, the media conversion unit analyzes the text of a news article and generates related images or video clips. Furthermore, the media conversion unit analyzes the text of a lecture and generates related charts and video clips. Furthermore, the media conversion unit analyzes a movie script and generates images and video clips of related scenes. In this way, visual complementary information related to the text data is provided to help understand the information.

[0058] The media conversion unit can use the emotion estimation function to automatically add music or sound effects that are optimal for the user based on the emotional tone of the audio data. The media conversion unit, for example, uses the emotion estimation function to automatically add music or sound effects that are optimal for the user based on the emotional tone of the audio data. For example, the media conversion unit adds cheerful music to audio data that contains a strong emotion of joy. The media conversion unit also adds quiet music to audio data that contains a strong emotion of sadness. The media conversion unit also adds sound effects to audio data that contains a strong emotion of surprise. In this way, the user experience is improved by adding optimal music or sound effects based on the emotional tone of the audio data.

[0059] The broadcast collaboration unit can analyze audio data from the broadcast system in real time and automatically highlight specific keywords or phrases. The broadcast collaboration unit adds, for example, a function to analyze audio data from the broadcast system in real time and automatically highlight specific keywords or phrases. For example, the broadcast collaboration unit highlights important keywords during a news broadcast. The broadcast collaboration unit also highlights important phrases during a lecture. The broadcast collaboration unit also highlights important remarks during an interview. In this way, the audio data from the broadcast system is analyzed in real time and important information is visually emphasized, thereby helping viewers understand the content.

[0060] The broadcast collaboration unit can analyze audio data from the broadcasting system, summarize important information, and convert it into text. The broadcast collaboration unit, for example, introduces technology that analyzes audio data from the broadcasting system and summarizes important information and converts it into text. For example, the broadcast collaboration unit automatically summarizes the main points of a news broadcast and converts it into text. The broadcast collaboration unit also automatically summarizes the main points of a lecture and converts it into text. The broadcast collaboration unit also automatically summarizes the main points of an interview and converts it into text. In this way, by summarizing and converting audio data from the broadcasting system into text, important information can be efficiently provided to viewers.

[0061] The broadcast collaboration unit can analyze audio data from the broadcasting system and estimate viewer reactions based on the emotional tone. The broadcast collaboration unit adds a function of analyzing audio data from the broadcasting system and predicting viewer reactions based on the emotional tone, for example. For example, the broadcast collaboration unit analyzes the emotional tone of a news broadcast and predicts viewer reactions. The broadcast collaboration unit also analyzes the emotional tone of a lecture and predicts viewer reactions. The broadcast collaboration unit also analyzes the emotional tone of an interview and predicts viewer reactions. This improves the effectiveness of the broadcast content by predicting viewer reactions based on the emotional tone of the audio data from the broadcasting system.

[0062] The broadcast collaboration unit can analyze audio data from the broadcasting system, automatically collect related social media posts, and display viewer reactions in real time. The broadcast collaboration unit, for example, analyzes audio data from the broadcasting system, automatically collects related social media posts, and displays viewer reactions in real time. For example, it displays related tweets during a news broadcast. The broadcast collaboration unit also displays related Facebook posts during a lecture, for example. The broadcast collaboration unit also displays related Instagram posts during an interview, for example. In this way, by analyzing audio data from the broadcasting system and displaying related social media posts in real time, viewer reactions can be immediately grasped.

[0063] The broadcast collaboration unit can analyze audio data from the broadcast system and automatically generate related news articles or blog posts to provide to viewers. The broadcast collaboration unit, for example, analyzes audio data from the broadcast system and automatically generates related news articles or blog posts to provide to viewers. For example, the broadcast collaboration unit generates related articles based on the content of a news broadcast. The broadcast collaboration unit also generates related blog posts based on the content of a lecture, for example. The broadcast collaboration unit also generates related articles based on the content of an interview, for example. In this way, by analyzing audio data from the broadcast system and automatically generating related news articles or blog posts, additional information can be provided to viewers.

[0064] The broadcast collaboration unit can use the emotion estimation function to automatically add advertisements that are optimal for viewers based on the emotional tone of the audio data from the broadcasting system. The broadcast collaboration unit, for example, uses the emotion estimation function to automatically insert advertisements that are optimal for viewers based on the emotional tone of the audio data from the broadcasting system. For example, the broadcast collaboration unit inserts relevant advertisements during news broadcasts with a positive emotional tone. The broadcast collaboration unit also inserts relevant advertisements during lectures with a negative emotional tone. The broadcast collaboration unit also inserts relevant advertisements during interviews with a surprised emotional tone. In this way, inserting optimal advertisements based on the emotional tone of the audio data from the broadcasting system makes it easier to attract viewers' attention.

[0065] The content recommendation unit can analyze a user's viewing history and search history and suggest highly relevant content based on emotional tone. The content recommendation unit, for example, analyzes a user's viewing history and search history and recommends highly relevant content based on emotional tone. For example, the content recommendation unit recommends similarly positive content to a user with a viewing history containing a positive emotional tone. Furthermore, the content recommendation unit recommends relaxing content to a user with a viewing history containing a negative emotional tone. Furthermore, the content recommendation unit recommends content with a surprise element to a user with a viewing history containing a surprising emotional tone. In this way, highly relevant content is recommended based on the user's emotional tone, thereby improving user satisfaction.

[0066] The content recommendation unit can analyze a user's viewing history and search history and suggest content based on viewing time or viewing frequency. The content recommendation unit introduces, for example, a technology that analyzes a user's viewing history and search history and recommends content based on viewing time and viewing frequency. For example, the content recommendation unit recommends series content to users who watch for long periods of time. Furthermore, the content recommendation unit recommends short videos to users who watch for short periods of time. Furthermore, the content recommendation unit recommends the latest content to users who watch frequently. In this way, the user's viewing experience is optimized by recommending content based on viewing time and viewing frequency.

[0067] The content recommendation unit can analyze a user's viewing history and search history and suggest content based on the viewer's interests. The content recommendation unit adds a function to analyze a user's viewing history and search history and recommend content based on the viewer's interests. For example, to a user who is interested in a particular genre, the content recommendation unit recommends new releases in that genre. For example, to a user who is interested in a particular theme, the content recommendation unit recommends documentaries related to that theme. For example, to a user who is interested in works starring a particular actor, the content recommendation unit recommends other works by that actor. In this way, content is recommended based on the viewer's interests, improving viewer satisfaction.

[0068] The content recommendation unit can analyze a user's viewing history and search history and suggest related offline events or seminars. The content recommendation unit, for example, analyzes a user's viewing history and search history and recommends related offline events and seminars. For example, to a user who is interested in a particular technology, the content recommendation unit recommends a seminar related to that technology. Furthermore, to a user who is interested in a particular movie, the content recommendation unit recommends an event showing that movie. Furthermore, to a user who is interested in a particular author, the content recommendation unit recommends a lecture by that author. In this way, by recommending related offline events and seminars based on the viewing history and search history, it is easier to attract the user's interest.

[0069] The content recommendation unit can analyze a user's viewing history and search history and suggest related books or papers. The content recommendation unit adds a function of, for example, analyzing a user's viewing history and search history and recommending related books and papers. For example, to a user who is interested in a particular theme, the content recommendation unit recommends books related to that theme. Furthermore, for example, to a user who is interested in a particular technology, the content recommendation unit recommends papers related to that technology. Furthermore, for example, to a user who is interested in a particular historical event, the content recommendation unit recommends books related to that event. In this way, the user's knowledge is deepened by recommending related books and papers based on the viewing history and search history.

[0070] The content recommendation unit can use the emotion estimation function to suggest content that promotes relaxation or concentration based on the user's emotional tone. The content recommendation unit, for example, uses the emotion estimation function to recommend content that promotes relaxation or concentration based on the user's emotional tone. For example, the content recommendation unit recommends relaxing music to a user who is feeling stressed. Furthermore, the content recommendation unit recommends environmental sounds that promote concentration to a user who wants to improve their concentration. Furthermore, the content recommendation unit recommends a meditation guide to a user who wants to relax. In this way, by recommending content that promotes relaxation or concentration based on the user's emotional tone, it is possible to provide optimal content according to the user's psychological state.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The UMC system may further include a visual customization unit that customizes content based on the user's visual preferences. For example, the visual customization unit may analyze the user's preferred color tone and design style and change the interface color and layout accordingly. If the user prefers a particular visual effect, the visual customization unit may preferentially display content that includes that effect. Furthermore, the visual customization unit may automatically adjust the screen brightness and contrast to reduce the user's visual fatigue. This allows customization according to the user's visual preferences, providing a more comfortable viewing experience.

[0073] The UMC system may further include a feedback unit that provides appropriate feedback based on the user's emotional tone. For example, the feedback unit may display a message of encouragement or praise when the user is expressing positive emotions. The feedback unit may also display a message of comfort or encouragement when the user is expressing negative emotions. Furthermore, the feedback unit may display a message for sharing the user's emotions when the user is expressing surprise or excitement. This allows the user's satisfaction to be improved by providing appropriate feedback according to the user's emotions.

[0074] The UMC system may further include a health monitoring unit that monitors the user's health status and provides appropriate content. For example, the health monitoring unit may analyze the user's heart rate and stress level and recommend relaxing content based on the results. The health monitoring unit may also analyze the user's sleep patterns and provide content to improve sleep quality. Furthermore, the health monitoring unit may analyze the user's exercise volume and provide content to promote exercise. This allows the user's health to be supported by providing appropriate content according to the user's health status.

[0075] The UMC system may further include an education customization unit that customizes educational content based on the user's learning style. For example, if the user is a visual learner, the education customization unit may provide content that makes extensive use of diagrams and videos. Alternatively, if the user is an auditory learner, the education customization unit may provide content that emphasizes audio commentary. Furthermore, if the user is a hands-on learner, the education customization unit may provide content that includes interactive exercises and simulations. This maximizes learning effectiveness by providing educational content that matches the user's learning style.

[0076] The UMC system may further include a reminder unit that provides appropriate reminders based on the user's emotional tone. For example, if the user is feeling stressed, the reminder unit may display a reminder to take a break to relax. Also, if the user wants to improve their concentration, the reminder unit may display a reminder to maintain their concentration. Furthermore, if the user is expressing positive emotions, the reminder unit may display a reminder to maintain those emotions. This allows the user's quality of life to be improved by providing appropriate reminders according to the user's emotional tone.

[0077] The UMC system may further include a community recommendation unit that recommends related communities based on the user's hobbies and interests. For example, if the user is interested in a particular sport, the community recommendation unit may recommend online communities related to that sport. Alternatively, if the user has a particular hobby, the community recommendation unit may recommend forums and groups related to that hobby. Furthermore, if the user is interested in a particular topic, the community recommendation unit may recommend discussion groups related to that topic. This allows the user's social connections to be strengthened by recommending communities based on the user's hobbies and interests.

[0078] The UMC system may further include an exercise suggestion unit that suggests appropriate exercises based on the user's emotional tone. For example, if the user is feeling stressed, the exercise suggestion unit may suggest relaxing yoga or stretching. If the user is feeling energetic, the exercise suggestion unit may suggest running or dancing to release energy. Furthermore, if the user wants to improve their concentration, the exercise suggestion unit may suggest meditation or breathing techniques to maintain concentration. In this way, by suggesting appropriate exercises according to the user's emotional tone, the user's health and well-being can be supported.

[0079] The UMC system may further include a podcast recommendation unit that recommends related podcasts based on the user's viewing history. For example, if the user has watched a movie of a particular genre, the podcast recommendation unit may recommend podcasts related to that genre. If the user is interested in a particular theme, the podcast recommendation unit may also recommend podcasts related to that theme. Furthermore, if the user is interested in a particular celebrity, the podcast recommendation unit may also recommend podcasts featuring that celebrity. This makes it easier to attract the user's interest by recommending related podcasts based on the user's viewing history.

[0080] The UMC system may further include a relaxation suggestion unit that suggests appropriate relaxation techniques based on the user's emotional tone. For example, if the user is feeling stressed, the relaxation suggestion unit may suggest deep breathing or meditation. Also, if the user is feeling anxious, the relaxation suggestion unit may suggest relaxing music or natural sounds. Furthermore, if the user is feeling fatigued, the relaxation suggestion unit may suggest light stretching or massage. By suggesting appropriate relaxation techniques according to the user's emotional tone, the system can reduce the user's stress and promote relaxation.

[0081] The UMC system may further include an online course recommendation unit that recommends related online courses based on the user's viewing history. For example, if the user is interested in a particular technology, the online course recommendation unit may recommend online courses related to that technology. If the user wants to learn a particular language, the online course recommendation unit may also recommend online courses related to that language. Furthermore, if the user has a particular hobby, the online course recommendation unit may also recommend online courses related to that hobby. By recommending related online courses based on the user's viewing history, the user's motivation to learn can be increased.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The media conversion unit converts media formats such as voice, text, and images into each other. For example, voice can be converted into text using voice recognition technology, and the text can be translated and converted into voice in another language using voice synthesis technology. Images can also be converted into text using image recognition technology. Step 2: The broadcasting linkage unit distributes the media converted by the media conversion unit in cooperation with the broadcasting system. For example, it converts the audio of a news broadcast into text in real time, and then converts that text into audio in another language for delivery to overseas viewers. Step 3: The content recommendation unit recommends relevant content based on the user's viewing and search history. For example, if a user reads a particular news article, it will recommend other news articles and videos related to that article. Step 4: The anomaly detection unit analyzes the content and issues a warning if an anomaly is detected. For example, if a news article or video contains inappropriate content, the unit will detect the content and issue a warning.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a media conversion unit that converts audio, text, and image media formats into each other; a broadcasting cooperation unit that distributes the media converted by the media conversion unit in cooperation with a broadcasting system; a content recommendation unit that recommends highly relevant content based on the user's viewing history and search history; An abnormality detection unit that analyzes the content and issues a warning if an abnormality is detected. A system characterized by:

2. The media conversion unit Analyzes the emotional tone of voice data and converts it into text based on emotions 2. The system of claim 1.

3. The media conversion unit Automatically remove background sounds or noise from audio data to produce clear speech to text 2. The system of claim 1.

4. The media conversion unit Analyzes the speaker's characteristics in the audio data and converts it into text based on that.

2. The system of claim 1.

5. The media conversion unit When converting audio data into text, it simultaneously converts it into sign language video to provide information for the hearing impaired.

2. The system of claim 1.

6. The media conversion unit Analyzes text data and automatically generates related images or video clips to provide visual supplementary information 2. The system of claim 1.

7. The media conversion unit Automatically add music or sound effects that best suit the user based on the emotional tone of the audio data 2. The system of claim 1.

8. The broadcasting cooperation unit Analyzing audio data from the broadcast system in real time and automatically highlighting specific keywords or phrases 2. The system of claim 1.

Images