System

Abstract

An object of the system according to the embodiment is to provide audio content customized based on the interest and concern of the user.SOLUTION: A system includes a content generation unit, a voice conversion unit, and a customization unit. The content generation unit generates content based on the user's interest or concern. The sound conversion unit converts the content generated by the content generation unit into sound. The customization unit customizes the voice converted by the voice conversion unit based on the user's interest and concern.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had a problem in that they have not been able to efficiently generate content based on a user's interests and provide it as audio.

[0005] The system according to the embodiment aims to provide audio content that is customized based on the user's interests and concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes a content generation unit, a voice conversion unit, and a customization unit. The content generation unit generates content based on a user's interests and concerns. The voice conversion unit converts the content generated by the content generation unit into voice. The customization unit customizes the voice converted by the voice conversion unit based on the user's interests and concerns. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized audio content based on the user's interests. [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 personalized radio system according to an embodiment of the present invention is a system for providing audio content customized based on a user's interests. Thus, the personalized radio system can provide audio content customized based on a user's interests.

[0029] A personalized radio system according to an embodiment includes a content generation unit, a voice conversion unit, and a customization unit. The content generation unit generates content based on a user's interests. For example, if a user is interested in "history" or "science," the content generation unit collects information on these topics and summarizes it appropriately. The content generation unit also generates content based on the user's instructions using a generation AI (e.g., LLM). For example, the generation AI receives a prompt such as "Tell me more about this topic," and collects and summarizes related information. The voice conversion unit converts the content generated by the content generation unit into voice. For example, the generated text content is converted into voice using TTS technology. The voice conversion unit also converts the generated text into natural-sounding voice using a generation AI (e.g., TTS technology). For example, the generation AI receives a prompt such as "Please read this text aloud," and generates voice. The customization unit customizes the voice converted by the voice conversion unit based on the user's interests. For example, if a user is interested in "astronomy," the customization unit generates content including the latest research and discoveries on astronomy and provides it as voice. The customization unit also customizes the content to be provided based on the user's profile information and past behavioral data. For example, the customization unit may provide related content based on the user's past viewing of content and search history. This allows the personalized radio system according to the embodiment to provide audio content customized based on the user's interests.

[0030] The content generation unit can provide a personalized learning experience by reflecting the user's past learning history and interests. The content generation unit, for example, reflects the user's past learning history in the content generated by the LLM. For example, new information is provided based on what the user has learned in the past. The content generation unit also generates content that reflects the user's interests. For example, if the user is interested in a particular topic, information related to that topic is provided. The content generation unit can also reflect the user's past learning history and interests in the content generated by the LLM to provide a personalized learning experience. For example, new information is provided based on what the user has learned in the past. This makes it possible to provide a personalized learning experience based on the user's past learning history and interests.

[0031] The content generation unit can reflect real-time user feedback and instantly modify and update the content. For example, the content generation unit reflects real-time user feedback in the content generated by the LLM. For example, if a user is dissatisfied with the content, the content is modified based on that feedback. The content generated by the LLM can also be instantly updated based on real-time user feedback. For example, if a user is seeking new information, the content is updated in accordance with that request. The content generated by the LLM can also be reflected in real-time user feedback and instantly modified and updated. For example, if a user provides positive feedback on the content, that content is reinforced. This allows content to be instantly modified and updated based on real-time user feedback.

[0032] The content generation unit can add visual elements to provide a multimodal learning experience. For example, the content generation unit adds visual elements to the content generated by the LLM. For example, it includes links to images and videos related to an article about history. It also adds visual elements to the content generated by the LLM to provide a multimodal learning experience. For example, it includes charts and graphs related to an article about science. It also adds visual elements to the content generated by the LLM to make it easier for users to understand. For example, it includes photos and illustrations related to an article about culture. In this way, it is possible to provide a multimodal learning experience by adding visual elements.

[0033] The content generation unit can automatically translate the content it generates into different languages ​​to accommodate international users. For example, the content generation unit automatically translates content generated by the LLM into different languages. For example, content generated in English is translated into Japanese or French. The content generated by the LLM is also automatically translated into different languages ​​to accommodate international users. For example, content generated in Spanish is translated into English or Chinese. The content generated by the LLM is also automatically translated into different languages ​​so that users can obtain information in their native language. For example, content generated in German is translated into Italian or Russian. In this way, automatic translation into different languages ​​can accommodate international users.

[0034] The speech conversion unit may have a function to customize the tone and speed of the generated speech according to the user's preferences. The speech conversion unit adds a function to customize the tone of the voice according to the user's preferences to speech generated using, for example, TTS technology. For example, if the user prefers a calm voice, speech is provided in that tone. In addition, a function to provide speech at a speed according to the user's preferences is added. For example, if the user prefers reading at a fast speed, speech is provided at that speed. In addition, a function to customize the tone and speed of the voice generated using TTS technology according to the user's preferences is added. For example, if the user prefers a bright voice, speech is provided in that tone. This makes it possible to provide speech at a tone and speed according to the user's preferences.

[0035] The speech conversion unit can add background music and sound effects to the generated speech to provide a more immersive learning experience. For example, the speech conversion unit adds background music to speech generated using TTS technology. For example, speech is provided while relaxing music is played in the background. Adding sound effects also provides a more immersive learning experience. For example, when reading a history article, sounds of a battlefield or sounds of nature can be added. Adding background music and sound effects to speech generated using TTS technology can also provide a more immersive learning experience for users. For example, when reading a science article, sounds of an experiment or sounds of nature can be added. In this way, adding background music and sound effects can provide a more immersive learning experience.

[0036] The speech conversion unit can distribute the generated speech in podcast format, allowing the user to access it anytime, anywhere. The speech conversion unit, for example, distributes speech generated using TTS technology in podcast format. For example, the user can listen to it while commuting or exercising. Distributing it in podcast format also allows the user to access it anytime, anywhere. For example, the user can study while doing housework. Distributing speech generated using TTS technology in podcast format also allows the user to study at their own pace. For example, the user can listen to it at any time they like. Distributing it in podcast format allows the user to access it anytime, anywhere.

[0037] The speech conversion unit can enable the generated speech to be synchronously played on at least one different device, such as a smartphone or a smart speaker. The speech conversion unit, for example, enables speech generated using TTS technology to be synchronously played on a smartphone or a smart speaker. This allows a user to study while on the move, for example, at home. Furthermore, by enabling synchronous playback on different devices, the user can study anywhere. For example, the user can study in a car. Furthermore, the speech generated using TTS technology can be synchronously played on different devices. For example, the user can continue studying on a smartphone using a smart speaker. This allows synchronous playback on different devices, allowing the user to study anywhere.

[0038] The customization unit can reflect the user's past behavioral data to achieve highly accurate customization. The customization unit can, for example, reflect the user's past behavioral data to achieve even more accurate customization. For example, related content is provided based on content the user has viewed in the past and their search history. The customization unit can also analyze the user's past behavioral data to perform customization based on their interests and concerns. For example, related content is provided based on keywords that the user frequently searches for. The customization unit can also reflect the user's past behavioral data to achieve even more accurate customization. For example, related content is provided based on ratings of content the user has viewed in the past. This makes it possible to achieve highly accurate customization based on the user's past behavioral data.

[0039] The customization unit can reflect real-time trend information and provide the latest information. The customization unit, for example, reflects real-time trend information in customization based on the user's interests and concerns. For example, content is provided based on the latest news and trending topics. The customization unit also reflects real-time trend information and performs customization based on the user's interests and concerns. For example, content related to currently trending topics is provided. The customization unit also reflects real-time trend information in customization based on the user's interests and concerns and provides the latest information. For example, content is provided based on the latest research results and technological trends. This makes it possible to provide the latest information based on real-time trend information.

[0040] The customization unit can incorporate information from different cultural spheres and regions to promote learning from a global perspective. The customization unit, for example, incorporates information from different cultural spheres and regions in customization based on the user's interests. For example, it provides information from different cultures related to a theme that interests the user. The customization unit also incorporates information from different cultural spheres and regions to perform customization based on the user's interests. For example, it provides an international perspective related to a theme that interests the user. The customization unit also incorporates information from different cultural spheres and regions in customization based on the user's interests to promote learning from a global perspective. For example, it provides information from around the world related to a theme that interests the user. In this way, by incorporating information from different cultural spheres and regions, it is possible to promote learning from a global perspective.

[0041] The customization unit can add a function for users to interact with each other to promote community-based learning. For example, the customization unit adds a function for users to interact with each other to customization based on user interests and concerns. For example, a forum is provided where users who are interested in the same topic can exchange opinions. In addition, a function for users to interact with each other is added to promote community-based learning. For example, a Q&A function is provided where users can post questions and answers. In addition, a function for users to interact with each other is added to customization based on user interests and concerns to promote community-based learning. For example, a collaboration function is provided where users can work together on a project. In this way, adding a function for users to interact with each other can promote community-based learning.

[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 content generation unit can also monitor the user's health status and provide health advice and information. For example, if it obtains data from the user's fitness tracker and detects a lack of exercise, it can emphasize the importance of exercise and suggest simple exercises. It can also analyze the user's food records and suggest nutritionally balanced meals. It can also provide advice on getting good quality sleep based on the user's sleep data.

[0044] The customization unit can also provide relevant event and community information based on the user's hobbies and special skills. For example, if the user is interested in music, it can provide information about nearby concerts and music events. If the user is good at cooking, it can provide information about cooking classes and recipe exchange events. Furthermore, if the user is interested in sports, it can provide information about local sports clubs and games.

[0045] The speech conversion unit can also add accents or dialects to the generated speech according to the user's preferences. For example, if the user prefers Kansai dialect, speech can be provided in that accent. If the user prefers British English, speech can be provided in that accent. Furthermore, if the user prefers a particular regional dialect, speech can be provided in that dialect.

[0046] The customization unit can also adjust the way content is provided depending on the user's learning style. For example, if the user prefers visual learning, content that makes extensive use of charts and illustrations can be provided. If the user prefers auditory learning, content that makes extensive use of audio and music can be provided. Furthermore, if the user prefers practical learning, content that includes procedures for experiments and practical training can be provided.

[0047] The customization unit can also provide information useful for daily life according to the user's lifestyle. For example, if the user leads a busy life, it can provide time-saving recipes and efficient housework methods. If the user is health-conscious, it can provide information on healthy ingredients and exercise methods. Furthermore, if the user likes to travel, it can provide recommended spots for travel destinations and local cultural information.

[0048] The customization unit can also adjust the way content is provided depending on the user's learning style. For example, if the user prefers visual learning, content that makes extensive use of charts and illustrations can be provided. If the user prefers auditory learning, content that makes extensive use of audio and music can be provided. Furthermore, if the user prefers practical learning, content that includes procedures for experiments and practical training can be provided.

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

[0050] Step 1: The content generator generates content based on the user's interests. For example, if the user is interested in "history" or "science," the content generator collects information about these topics and summarizes it appropriately. It also uses generative AI (e.g., LLM) to generate content based on the user's instructions. For example, it receives a prompt like "Tell me more about this topic," and collects and summarizes related information. Step 2: The speech conversion unit converts the content generated by the content generation unit into speech. For example, the generated text content is converted into speech using TTS technology. The speech conversion unit also converts the generated text into natural speech using a generation AI (for example, TTS technology). For example, it receives a prompt such as "Please read this text aloud" and generates speech. Step 3: The customization unit customizes the audio converted by the audio conversion unit based on the user's interests. For example, if the user is interested in "astronomy," the customization unit generates content including the latest research and discoveries in astronomy and provides it to the user through audio. The customization unit also customizes the content provided based on the user's profile information and past behavioral data. For example, it provides related content based on the content the user has previously viewed and their search history.

[0051] (Example 2) A personalized radio system according to an embodiment of the present invention is a system for providing audio content customized based on a user's interests. Thus, the personalized radio system can provide audio content customized based on a user's interests.

[0052] A personalized radio system according to an embodiment includes a content generation unit, a voice conversion unit, and a customization unit. The content generation unit generates content based on a user's interests. For example, if a user is interested in "history" or "science," the content generation unit collects information on these topics and summarizes it appropriately. The content generation unit also generates content based on the user's instructions using a generation AI (e.g., LLM). For example, the generation AI receives a prompt such as "Tell me more about this topic," and collects and summarizes related information. The voice conversion unit converts the content generated by the content generation unit into voice. For example, the generated text content is converted into voice using TTS technology. The voice conversion unit also converts the generated text into natural-sounding voice using a generation AI (e.g., TTS technology). For example, the generation AI receives a prompt such as "Please read this text aloud," and generates voice. The customization unit customizes the voice converted by the voice conversion unit based on the user's interests. For example, if a user is interested in "astronomy," the customization unit generates content including the latest research and discoveries on astronomy and provides it as voice. The customization unit also customizes the content to be provided based on the user's profile information and past behavioral data. For example, the customization unit may provide related content based on the user's past viewing of content and search history. This allows the personalized radio system according to the embodiment to provide audio content customized based on the user's interests.

[0053] The content generation unit may be equipped with an emotion estimation function that analyzes the user's emotions and adjusts the content to elicit positive emotions. For example, the content generation unit uses the emotion estimation function to analyze the user's emotions in real time for content generated by the LLM and adjusts the content to elicit positive emotions. For example, if the user is feeling stressed, the content generation unit provides relaxing content. The emotion estimation function is also used to generate content according to the user's emotional state. For example, if the user is tired, the content generation unit provides relaxing music and content containing positive messages. The emotion estimation function is also used to analyze the user's emotions for content generated by the LLM and provide feedback to elicit positive emotions. For example, if the user is excited, the content generation unit provides content containing advice to stay calm. In this way, content according to the user's emotions can be provided.

[0054] The content generation unit can provide a personalized learning experience by reflecting the user's past learning history and interests. The content generation unit, for example, reflects the user's past learning history in the content generated by the LLM. For example, new information is provided based on what the user has learned in the past. The content generation unit also generates content that reflects the user's interests. For example, if the user is interested in a particular topic, information related to that topic is provided. The content generation unit can also reflect the user's past learning history and interests in the content generated by the LLM to provide a personalized learning experience. For example, new information is provided based on what the user has learned in the past. This makes it possible to provide a personalized learning experience based on the user's past learning history and interests.

[0055] The content generation unit can reflect real-time user feedback and instantly modify and update the content. For example, the content generation unit reflects real-time user feedback in the content generated by the LLM. For example, if a user is dissatisfied with the content, the content is modified based on that feedback. The content generated by the LLM can also be instantly updated based on real-time user feedback. For example, if a user is seeking new information, the content is updated in accordance with that request. The content generated by the LLM can also be reflected in real-time user feedback and instantly modified and updated. For example, if a user provides positive feedback on the content, that content is reinforced. This allows content to be instantly modified and updated based on real-time user feedback.

[0056] The content generation unit can add visual elements to provide a multimodal learning experience. For example, the content generation unit adds visual elements to the content generated by the LLM. For example, it includes links to images and videos related to an article about history. It also adds visual elements to the content generated by the LLM to provide a multimodal learning experience. For example, it includes charts and graphs related to an article about science. It also adds visual elements to the content generated by the LLM to make it easier for users to understand. For example, it includes photos and illustrations related to an article about culture. In this way, it is possible to provide a multimodal learning experience by adding visual elements.

[0057] The content generation unit can automatically translate the content it generates into different languages ​​to accommodate international users. For example, the content generation unit automatically translates content generated by the LLM into different languages. For example, content generated in English is translated into Japanese or French. The content generated by the LLM is also automatically translated into different languages ​​to accommodate international users. For example, content generated in Spanish is translated into English or Chinese. The content generated by the LLM is also automatically translated into different languages ​​so that users can obtain information in their native language. For example, content generated in German is translated into Italian or Russian. In this way, automatic translation into different languages ​​can accommodate international users.

[0058] The content generation unit can use the emotion estimation function to identify the topic in which the user is most interested and preferentially generate content related to that topic. The content generation unit, for example, uses the emotion estimation function to identify the topic in which the user is most interested. For example, it preferentially generates content related to topics that excite the user. Furthermore, it analyzes the user's emotions and identifies the topic in which the user is most interested. For example, it preferentially generates content related to topics that relax the user. Furthermore, it uses the emotion estimation function to identify the topic in which the user is most interested and preferentially generates content related to that topic. For example, it preferentially generates content related to topics on which the user is focused. This makes it possible to preferentially generate content related to the topic in which the user is most interested.

[0059] The speech conversion unit uses an emotion estimation function to reflect the user's emotion in the generated speech, thereby providing speech rich in emotion. The speech conversion unit uses the emotion estimation function to reflect the user's emotion in speech generated using, for example, TTS technology. For example, if the user is relaxed, speech is provided in a calm tone. The emotion estimation function is also used to generate speech according to the user's emotion. For example, if the user is excited, speech is provided in an energetic tone. The emotion estimation function is also used to reflect the user's emotion in speech generated using TTS technology, thereby providing speech richer in emotion. For example, if the user is sad, speech is provided in a comforting tone. In this way, speech rich in emotion according to the user's emotion can be provided.

[0060] The speech conversion unit may have a function to customize the tone and speed of the generated speech according to the user's preferences. The speech conversion unit adds a function to customize the tone of the voice according to the user's preferences to speech generated using, for example, TTS technology. For example, if the user prefers a calm voice, speech is provided in that tone. In addition, a function to provide speech at a speed according to the user's preferences is added. For example, if the user prefers reading at a fast speed, speech is provided at that speed. In addition, a function to customize the tone and speed of the voice generated using TTS technology according to the user's preferences is added. For example, if the user prefers a bright voice, speech is provided in that tone. This makes it possible to provide speech at a tone and speed according to the user's preferences.

[0061] The speech conversion unit can add background music and sound effects to the generated speech to provide a more immersive learning experience. For example, the speech conversion unit adds background music to speech generated using TTS technology. For example, speech is provided while relaxing music is played in the background. Adding sound effects also provides a more immersive learning experience. For example, when reading a history article, sounds of a battlefield or sounds of nature can be added. Adding background music and sound effects to speech generated using TTS technology can also provide a more immersive learning experience for users. For example, when reading a science article, sounds of an experiment or sounds of nature can be added. In this way, adding background music and sound effects can provide a more immersive learning experience.

[0062] The speech conversion unit can distribute the generated speech in podcast format, allowing the user to access it anytime, anywhere. The speech conversion unit, for example, distributes speech generated using TTS technology in podcast format. For example, the user can listen to it while commuting or exercising. Distributing it in podcast format also allows the user to access it anytime, anywhere. For example, the user can study while doing housework. Distributing speech generated using TTS technology in podcast format also allows the user to study at their own pace. For example, the user can listen to it at any time they like. Distributing it in podcast format allows the user to access it anytime, anywhere.

[0063] The speech conversion unit can enable the generated speech to be synchronously played on at least one different device, such as a smartphone or a smart speaker. The speech conversion unit, for example, enables speech generated using TTS technology to be synchronously played on a smartphone or a smart speaker. This allows a user to study while on the move, for example, at home. Furthermore, by enabling synchronous playback on different devices, the user can study anywhere. For example, the user can study in a car. Furthermore, the speech generated using TTS technology can be synchronously played on different devices. For example, the user can continue studying on a smartphone using a smart speaker. This allows synchronous playback on different devices, allowing the user to study anywhere.

[0064] The voice conversion unit can be provided with a function of using an emotion estimation function to play back voice according to the time of day when the user is most relaxed. The voice conversion unit, for example, uses the emotion estimation function to play back voice according to the time of day when the user is most relaxed. For example, if the user is relaxing at night, voice is provided for that time of day. In addition, a function is added to analyze the user's emotional state and play back voice according to the time of day when the user is most relaxed. For example, if the user is relaxing during their lunch break, voice is provided for that time of day. In addition, a function is added to use the emotion estimation function to play back voice according to the time of day when the user is most relaxed. For example, if the user is relaxing on the weekend, voice is provided for that time of day. In this way, voice can be played back according to the time of day when the user is most relaxed.

[0065] The customization unit can use the emotion estimation function to analyze the user's emotions and preferentially provide content that elicits positive emotions. For example, when customizing based on the user's interests and concerns, the customization unit uses the emotion estimation function to analyze the user's emotions and preferentially provide content that elicits positive emotions. For example, if the user feels like relaxing, content that will help them relax is provided. Furthermore, the emotion estimation function is used to provide content that corresponds to the user's emotional state. For example, if the user is feeling stressed, content that is useful for relieving stress is provided. Furthermore, when customizing based on the user's interests and concerns, the emotion estimation function is used to analyze the user's emotions and preferentially provide content that elicits positive emotions. For example, if the user is excited, content that will maintain that excitement is provided. In this way, positive content that corresponds to the user's emotions can be provided.

[0066] The customization unit can reflect the user's past behavioral data to achieve highly accurate customization. The customization unit can, for example, reflect the user's past behavioral data to achieve even more accurate customization. For example, related content is provided based on content the user has viewed in the past and their search history. The customization unit can also analyze the user's past behavioral data to perform customization based on their interests and concerns. For example, related content is provided based on keywords that the user frequently searches for. The customization unit can also reflect the user's past behavioral data to achieve even more accurate customization. For example, related content is provided based on ratings of content the user has viewed in the past. This makes it possible to achieve highly accurate customization based on the user's past behavioral data.

[0067] The customization unit can reflect real-time trend information and provide the latest information. The customization unit, for example, reflects real-time trend information in customization based on the user's interests and concerns. For example, content is provided based on the latest news and trending topics. The customization unit also reflects real-time trend information and performs customization based on the user's interests and concerns. For example, content related to currently trending topics is provided. The customization unit also reflects real-time trend information in customization based on the user's interests and concerns and provides the latest information. For example, content is provided based on the latest research results and technological trends. This makes it possible to provide the latest information based on real-time trend information.

[0068] The customization unit can incorporate information from different cultural spheres and regions to promote learning from a global perspective. The customization unit, for example, incorporates information from different cultural spheres and regions in customization based on the user's interests. For example, it provides information from different cultures related to a theme that interests the user. The customization unit also incorporates information from different cultural spheres and regions to perform customization based on the user's interests. For example, it provides an international perspective related to a theme that interests the user. The customization unit also incorporates information from different cultural spheres and regions in customization based on the user's interests to promote learning from a global perspective. For example, it provides information from around the world related to a theme that interests the user. In this way, by incorporating information from different cultural spheres and regions, it is possible to promote learning from a global perspective.

[0069] The customization unit can add a function for users to interact with each other to promote community-based learning. For example, the customization unit adds a function for users to interact with each other to customization based on user interests and concerns. For example, a forum is provided where users who are interested in the same topic can exchange opinions. In addition, a function for users to interact with each other is added to promote community-based learning. For example, a Q&A function is provided where users can post questions and answers. In addition, a function for users to interact with each other is added to customization based on user interests and concerns to promote community-based learning. For example, a collaboration function is provided where users can work together on a project. In this way, adding a function for users to interact with each other can promote community-based learning.

[0070] The customization unit can use the emotion estimation function to identify the topic in which the user is most interested and provide content related to that topic preferentially. The customization unit, for example, uses the emotion estimation function to identify the topic in which the user is most interested. For example, content related to topics that excite the user is preferentially provided. The customization unit also analyzes the user's emotions to identify the topic in which the user is most interested. For example, content related to topics that relax the user is preferentially provided. The customization unit can also use the emotion estimation function to identify the topic in which the user is most interested and provide content related to that topic preferentially. For example, content related to topics that the user is focused on is preferentially provided. This makes it possible to provide content related to the topic in which the user is most interested preferentially.

[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 content generation unit can also monitor the user's health status and provide health advice and information. For example, if it obtains data from the user's fitness tracker and detects a lack of exercise, it can emphasize the importance of exercise and suggest simple exercises. It can also analyze the user's food records and suggest nutritionally balanced meals. It can also provide advice on getting good quality sleep based on the user's sleep data.

[0073] The content generator can also estimate the user's emotions and provide relaxation techniques based on the estimated emotions. For example, if the user is feeling stressed, it can provide deep breathing or meditation guidance. If the user is feeling anxious, it can provide relaxing music or nature sounds. Furthermore, if the user is tired, it can provide an alert encouraging them to take a short break.

[0074] The customization unit can also provide relevant event and community information based on the user's hobbies and special skills. For example, if the user is interested in music, it can provide information about nearby concerts and music events. If the user is good at cooking, it can provide information about cooking classes and recipe exchange events. Furthermore, if the user is interested in sports, it can provide information about local sports clubs and games.

[0075] The speech conversion unit can also add accents or dialects to the generated speech according to the user's preferences. For example, if the user prefers Kansai dialect, speech can be provided in that accent. If the user prefers British English, speech can be provided in that accent. Furthermore, if the user prefers a particular regional dialect, speech can be provided in that dialect.

[0076] The customization unit can also adjust the way content is provided depending on the user's learning style. For example, if the user prefers visual learning, content that makes extensive use of charts and illustrations can be provided. If the user prefers auditory learning, content that makes extensive use of audio and music can be provided. Furthermore, if the user prefers practical learning, content that includes procedures for experiments and practical training can be provided.

[0077] The content generator can also estimate the user's emotions and provide motivational content based on the estimated emotions. For example, if the user is feeling unmotivated, it can provide encouraging messages or success stories. If the user is working hard to achieve a goal, it can provide messages praising the user's progress. Furthermore, if the user is feeling challenged, it can provide positive feedback or advice.

[0078] The customization unit can also provide information useful for daily life according to the user's lifestyle. For example, if the user leads a busy life, it can provide time-saving recipes and efficient housework methods. If the user is health-conscious, it can provide information on healthy ingredients and exercise methods. Furthermore, if the user likes to travel, it can provide recommended spots for travel destinations and local cultural information.

[0079] The voice conversion unit can also reflect the user's emotions in the generated voice to provide an emotionally rich voice. For example, if the user is relaxed, the voice conversion unit can provide a calm tone. If the user is excited, the voice conversion unit can provide an energetic tone. If the user is sad, the voice conversion unit can provide a comforting tone.

[0080] The customization unit can also estimate the user's emotions and provide relaxation techniques based on the estimated emotions. For example, if the user is feeling stressed, the customization unit can provide deep breathing or meditation guidance. If the user is feeling anxious, the customization unit can provide relaxing music or nature sounds. Furthermore, if the user is tired, the customization unit can provide an alert to encourage them to take a short break.

[0081] The customization unit can also adjust the way content is provided depending on the user's learning style. For example, if the user prefers visual learning, content that makes extensive use of charts and illustrations can be provided. If the user prefers auditory learning, content that makes extensive use of audio and music can be provided. Furthermore, if the user prefers practical learning, content that includes procedures for experiments and practical training can be provided.

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

[0083] Step 1: The content generator generates content based on the user's interests. For example, if the user is interested in "history" or "science," the content generator collects information about these topics and summarizes it appropriately. It also uses generative AI (e.g., LLM) to generate content based on the user's instructions. For example, it receives a prompt like "Tell me more about this topic," and collects and summarizes related information. Step 2: The speech conversion unit converts the content generated by the content generation unit into speech. For example, the generated text content is converted into speech using TTS technology. The speech conversion unit also converts the generated text into natural speech using a generation AI (for example, TTS technology). For example, it receives a prompt such as "Please read this text aloud" and generates speech. Step 3: The customization unit customizes the audio converted by the audio conversion unit based on the user's interests. For example, if the user is interested in "astronomy," the customization unit generates content including the latest research and discoveries in astronomy and provides it to the user through audio. The customization unit also customizes the content provided based on the user's profile information and past behavioral data. For example, it provides related content based on the content the user has previously viewed and their search history.

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

[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 content generation unit that generates content based on the user's interests and concerns; a voice conversion unit that converts the content generated by the content generation unit into voice; a customization unit that customizes the voice converted by the voice conversion unit based on the user's interests and concerns. A system characterized by:

2. The content generation unit Emotion estimation function that analyzes the user's emotions and adjusts the content to elicit positive emotions 2. The system of claim 1.

3. The content generation unit Provide a personalized learning experience that reflects the user's past learning history and interests 2. The system of claim 1.

4. The content generation unit Reflecting real-time user feedback and instantly correcting and updating content 2. The system of claim 1.

5. The content generation unit Add visual elements and create a multimodal learning experience 2. The system of claim 1.

Images