System

Abstract

An object of a system according to an embodiment is to automatically generate a podcast in which content and audio based on a user's preference are combined.SOLUTION: A system includes a content optimization unit, a sound generation unit, a podcast generation unit, and a distribution unit. The content optimizer optimizes the content based on the user's preferences. The sound generation unit generates sound based on the content optimized by the content optimization unit. The podcast generation unit generates a podcast by combining the sound generated by the sound generation unit and the content optimized by the content optimization unit. The distribution unit distributes the podcast generated by the podcast generation unit to the device of the user.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 the problem of making it difficult to automatically generate podcasts that combine content and audio based on user preferences.

[0005] The system according to the embodiment aims to automatically generate podcasts that combine content and audio based on user preferences. [Means for solving the problem]

[0006] A system according to an embodiment includes a content optimization unit, an audio generation unit, a podcast generation unit, and a distribution unit. The content optimization unit optimizes content based on user preferences. The audio generation unit generates audio based on the content optimized by the content optimization unit. The podcast generation unit generates a podcast by combining the audio generated by the audio generation unit and the content optimized by the content optimization unit. The distribution unit distributes the podcast generated by the podcast generation unit to a user device. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate podcasts that combine content and audio based on user preferences. [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 podcast production system according to an embodiment of the present invention automatically generates a podcast program for a user. The system optimizes content based on the user's preferences, selects the user's preferred voice, and generates and distributes the podcast. This allows the podcast production system to provide podcasts optimized to the user's preferences, allowing users to enjoy blissful moments during their commute, housework, or other downtime.

[0029] A podcast generation system according to an embodiment includes a content optimization unit, an audio generation unit, a podcast generation unit, and a distribution unit. The content optimization unit optimizes content based on a user's preferences. For example, the generation AI analyzes a user's past viewing history and interests and suggests optimal content based on the analysis. The generation AI receives input from prompts including the user's viewing history and interests, and the generation AI generates optimal content based on the prompts. The audio generation unit generates audio based on the content optimized by the content optimization unit. For example, the generation AI analyzes voice characteristics preferred by the user and generates audio based on the analysis. The generation AI receives input from prompts including voice characteristics preferred by the user and generates audio based on the analysis. The podcast generation unit generates a podcast by combining the audio generated by the audio generation unit with content optimized by the content optimization unit. For example, the generation AI combines content and audio generated based on the user's preferences to automatically generate a podcast program. The distribution unit distributes the podcast generated by the podcast generation unit to the user's device. For example, the generation AI analyzes a user's schedule and lifestyle and distributes podcasts at optimal times. As a result, the podcast production system according to the embodiment can automatically generate and distribute podcasts optimized to the preferences of the user.

[0030] The content optimization unit can generate content that incorporates not only a user's past viewing history but also current trends and news. For example, the content optimization unit analyzes a user's past viewing history to generate podcasts that incorporate current trends and news. For example, it can provide episodes containing the latest news in a user's favorite genre. The content optimization unit also generates content that incorporates the latest trends and news based on the user's interests. For example, it can introduce the latest events related to topics that interest the user. The content optimization unit also combines a user's viewing history with current trends to generate new content that will pique their interest. For example, it can provide the latest topics related to episodes that the user has listened to in the past. This makes it possible to provide content that combines a user's past viewing history with current trends.

[0031] The content optimization unit can provide content based on the user's preferences not only in audio format but also in text or video format. For example, the content optimization unit provides content based on the user's preferences not only in audio format but also in text format. For example, the content of a podcast may be converted into text and provided in a format that is easy for the user to read. The content optimization unit also provides content based on the user's preferences in video format. For example, the content of a podcast may be converted into video and provided in a format that is visually enjoyable. The content optimization unit also provides content based on the user's preferences in multiple formats, including audio, text, and video. For example, the content may be enjoyed in a format selected by the user. This makes it possible to provide content based on the user's preferences in multiple formats.

[0032] The content optimization unit can match the preferences of different users and generate group podcasts for users with common interests. For example, the content optimization unit can match the preferences of different users and generate group podcasts for users with common interests. For example, it can provide episodes that bring together users who are interested in the same genre or theme. The content optimization unit can also analyze user preferences and build a system that matches users with common interests. For example, it can group users with the same hobbies or interests. The content optimization unit can also generate group podcasts for users with common interests and promote interaction between users. For example, it can provide episodes in the form of discussions that users can participate in. This makes it possible to generate group podcasts for users with common interests.

[0033] If a user likes the voice of a particular voice actor or celebrity, the voice generation unit can generate a voice that imitates that voice. For example, the voice generation unit generates a voice that imitates the voice of a particular voice actor that the user likes. For example, a podcast is generated based on the voice of a voice actor that the user likes. The voice generation unit also generates a voice that imitates the voice of a particular celebrity that the user likes. For example, a podcast is generated based on the voice of an actor that the user likes. The voice generation unit also analyzes the user's preferences and generates a voice that imitates the voice of a particular voice actor or celebrity in real time. For example, the voice is changed according to the user's request. In this way, a voice that imitates a particular voice that the user likes can be provided.

[0034] The audio generation unit can generate audio content that combines music and sound effects based on the characteristics of a user's preferred voice. The audio generation unit, for example, generates audio content that combines music based on the characteristics of a user's preferred voice. For example, a podcast that combines a voice and music that helps the user relax is provided. The audio generation unit also generates audio content that combines sound effects based on the characteristics of a user's preferred voice. For example, a podcast that combines a voice and sound effects that helps the user concentrate is provided. The audio generation unit also analyzes the user's preferences and combines music and sound effects that match the characteristics of the voice in real time. For example, the audio content is adjusted according to the user's request. This makes it possible to provide audio content that combines music and sound effects based on the characteristics of a user's preferred voice.

[0035] The distribution unit can dynamically adjust the optimal distribution timing based on the user's schedule. The distribution unit, for example, analyzes the user's schedule and dynamically adjusts the optimal distribution timing. For example, if the user likes to listen to podcasts during their commute, distribution is performed to coincide with that time. The distribution unit also analyzes the user's lifestyle and dynamically adjusts the optimal distribution timing. For example, if the user likes to listen to podcasts during housework, distribution is performed to coincide with that time. The distribution unit also adjusts the optimal distribution timing in real time based on the user's schedule data. For example, the distribution timing is immediately changed if the user's plans change. This makes it possible to adjust the optimal distribution timing based on the user's schedule.

[0036] The distribution unit can collect user feedback in real time and improve the content based on that. For example, the distribution unit collects user feedback in real time and improves the content of the podcast based on that. For example, the distribution unit adjusts episodes based on user ratings and comments. The distribution unit also analyzes user feedback and improves the structure and topics of the podcast. For example, it reflects themes and formats preferred by users. The distribution unit also improves the content of the next episode based on the feedback collected in real time. For example, it adds new topics in response to user requests. This allows the content to be improved based on user feedback.

[0037] The distribution unit can customize the content of the podcast to match the user's life events. For example, the distribution unit generates a special podcast episode to match the user's birthday. For example, the distribution unit provides an episode including a birthday message or special content. The distribution unit also generates a customized podcast to match the user's anniversary. For example, the distribution unit provides content related to a wedding anniversary or a work anniversary. The distribution unit also customizes the content of the podcast to match the user's life events. For example, the distribution unit covers topics related to the user's important events. This allows the content of the podcast to be customized to match the user's life events.

[0038] The distribution unit can enable the user to listen to podcasts on other devices. For example, the distribution unit can enable the user to listen to podcasts on a smart speaker. For example, distribution can be performed on devices such as Amazon Echo and Google Home. The distribution unit can also enable the user to listen to podcasts on an in-vehicle system. For example, distribution can be performed on devices such as Apple CarPlay and Android Auto. The distribution unit can also enable the user to seamlessly listen to podcasts on multiple devices. For example, synchronized distribution can be performed on smartphones, tablets, smart speakers, etc., thereby enabling the user to listen to podcasts on other devices.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The podcast generation system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, analyze the user's heart rate and sleep patterns and provide content appropriate to the user's health condition. For example, if the user is feeling stressed, the health management unit may provide relaxing music or podcasts. The health management unit may also analyze the user's exercise habits and provide energetic content suitable for listening to while exercising. The health management unit may also analyze the user's food records and provide content that supports a healthy diet. This allows the system to provide optimal content appropriate to the user's health condition.

[0041] The podcast generation system can further include an education support unit that provides educational content according to the user's learning needs. The education support unit, for example, analyzes the user's learning history and interests and provides optimal educational content based on that. For example, if the user is learning a language, it can provide podcasts that are useful for language learning. The education support unit can also monitor the user's learning progress and provide content according to that progress. Furthermore, the education support unit can provide educational content customized to the user's learning style. This makes it possible to provide optimal educational content according to the user's learning needs.

[0042] The podcast generation system can also provide content based on a user's preferences in text or video format, in addition to audio. For example, the podcast content can be converted into text and provided in a format that is easy for the user to read. Alternatively, the podcast content can be converted into video and provided in a format that is visually enjoyable. Furthermore, the podcast generation system can provide content based on a user's preferences in multiple formats, including audio, text, and video. This allows content based on a user's preferences to be provided in multiple formats.

[0043] The podcast generation system can also provide content based on a user's preferences in text or video format, in addition to audio. For example, the podcast content can be converted into text and provided in a format that is easy for the user to read. Alternatively, the podcast content can be converted into video and provided in a format that is visually enjoyable. Furthermore, the podcast generation system can provide content based on a user's preferences in multiple formats, including audio, text, and video. This allows content based on a user's preferences to be provided in multiple formats.

[0044] The podcast generation system can also provide content based on a user's preferences in text or video format, in addition to audio. For example, the podcast content can be converted into text and provided in a format that is easy for the user to read. Alternatively, the podcast content can be converted into video and provided in a format that is visually enjoyable. Furthermore, the podcast generation system can provide content based on a user's preferences in multiple formats, including audio, text, and video. This allows content based on a user's preferences to be provided in multiple formats.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The content optimization unit optimizes content based on the user's preferences. For example, the generation AI analyzes the user's past viewing history and interests and suggests optimal content based on that. The input to the generation AI is a prompt containing the user's viewing history and interests, and the generation AI generates optimal content based on that prompt. Step 2: The voice generation unit generates voice based on the content optimized by the content optimization unit. For example, the generation AI analyzes the voice characteristics preferred by the user and generates voice based on that. The input to the generation AI is a prompt containing the voice characteristics preferred by the user, and the generation AI generates voice based on that prompt. Step 3: The podcast generation unit generates a podcast by combining the audio generated by the audio generation unit with the content optimized by the content optimization unit. For example, the generation AI combines the content and audio generated based on the user's preferences to automatically generate a podcast program. Step 4: The distribution unit distributes the podcast generated by the podcast generation unit to the user's device. For example, the generation AI analyzes the user's schedule and lifestyle and distributes the podcast at the optimal time.

[0047] (Example 2) A podcast production system according to an embodiment of the present invention automatically generates a podcast program for a user. The system optimizes content based on the user's preferences, selects the user's preferred voice, and generates and distributes the podcast. This allows the podcast production system to provide podcasts optimized to the user's preferences, allowing users to enjoy blissful moments during their commute, housework, or other downtime.

[0048] A podcast generation system according to an embodiment includes a content optimization unit, an audio generation unit, a podcast generation unit, and a distribution unit. The content optimization unit optimizes content based on a user's preferences. For example, the generation AI analyzes a user's past viewing history and interests and suggests optimal content based on the analysis. The generation AI receives input from prompts including the user's viewing history and interests, and the generation AI generates optimal content based on the prompts. The audio generation unit generates audio based on the content optimized by the content optimization unit. For example, the generation AI analyzes voice characteristics preferred by the user and generates audio based on the analysis. The generation AI receives input from prompts including voice characteristics preferred by the user and generates audio based on the analysis. The podcast generation unit generates a podcast by combining the audio generated by the audio generation unit with content optimized by the content optimization unit. For example, the generation AI combines content and audio generated based on the user's preferences to automatically generate a podcast program. The distribution unit distributes the podcast generated by the podcast generation unit to the user's device. For example, the generation AI analyzes a user's schedule and lifestyle and distributes podcasts at optimal times. As a result, the podcast production system according to the embodiment can automatically generate and distribute podcasts optimized to the preferences of the user.

[0049] The content optimization unit can analyze the user's emotional state in real time and generate content that is optimal for the user's mood at that time. For example, the content optimization unit analyzes the user's emotional state in real time and generates relaxing music or podcasts if the user is feeling stressed. For example, the content optimization unit analyzes the user's heart rate and facial expression and provides content that has a high relaxing effect. Furthermore, the content optimization unit generates energetic music or podcasts if the user is excited. For example, the content optimization unit analyzes the user's tone of voice and movements and provides content that increases energy. Furthermore, the content optimization unit generates podcasts containing messages of comfort and encouragement if the user is sad. For example, the content optimization unit analyzes the user's facial expression and voice and provides content that soothes the user's emotions. This makes it possible to provide optimal content according to the user's emotional state.

[0050] The content optimization unit can generate content that incorporates not only a user's past viewing history but also current trends and news. For example, the content optimization unit analyzes a user's past viewing history to generate podcasts that incorporate current trends and news. For example, it can provide episodes containing the latest news in a user's favorite genre. The content optimization unit also generates content that incorporates the latest trends and news based on the user's interests. For example, it can introduce the latest events related to topics that interest the user. The content optimization unit also combines a user's viewing history with current trends to generate new content that will pique their interest. For example, it can provide the latest topics related to episodes that the user has listened to in the past. This makes it possible to provide content that combines a user's past viewing history with current trends.

[0051] The content optimization unit can use the emotion estimation function to provide optimal content when the user wants to relax or concentrate. The content optimization unit, for example, uses the user emotion estimation function to provide optimal music or podcasts when the user wants to relax. For example, the content optimization unit analyzes the user's heart rate and facial expression to select content that has a high relaxing effect. The content optimization unit also provides optimal content when the user wants to concentrate. For example, the content optimization unit analyzes the user's work environment and concentration level to provide music or podcasts that enhance concentration. The content optimization unit also provides content suitable for relaxation or concentration in real time based on the user emotion estimation data. For example, the content optimization unit dynamically adjusts the content according to changes in the user's emotions. This makes it possible to provide content suitable for relaxation or concentration according to the user's emotional state.

[0052] The content optimization unit can provide content based on the user's preferences not only in audio format but also in text or video format. For example, the content optimization unit provides content based on the user's preferences not only in audio format but also in text format. For example, the content of a podcast may be converted into text and provided in a format that is easy for the user to read. The content optimization unit also provides content based on the user's preferences in video format. For example, the content of a podcast may be converted into video and provided in a format that is visually enjoyable. The content optimization unit also provides content based on the user's preferences in multiple formats, including audio, text, and video. For example, the content may be enjoyed in a format selected by the user. This makes it possible to provide content based on the user's preferences in multiple formats.

[0053] The content optimization unit can match the preferences of different users and generate group podcasts for users with common interests. For example, the content optimization unit can match the preferences of different users and generate group podcasts for users with common interests. For example, it can provide episodes that bring together users who are interested in the same genre or theme. The content optimization unit can also analyze user preferences and build a system that matches users with common interests. For example, it can group users with the same hobbies or interests. The content optimization unit can also generate group podcasts for users with common interests and promote interaction between users. For example, it can provide episodes in the form of discussions that users can participate in. This makes it possible to generate group podcasts for users with common interests.

[0054] The content optimization unit can use the emotion estimation function to identify topics that interest the user most and generate new content based on those topics. The content optimization unit, for example, uses the emotion estimation function to identify topics that interest the user most. For example, it analyzes the user's facial expressions and voice and selects topics that interest the user. The content optimization unit also generates new content based on the user's emotion estimation data. For example, it provides the latest episodes related to topics that interest the user. The content optimization unit also uses the emotion estimation function to identify topics that interest the user in real time and dynamically generate content based on those topics. For example, it adjusts the content according to changes in the user's emotions. This makes it possible to provide new content based on topics that interest the user.

[0055] The voice generation unit can dynamically adjust the tone and tempo of the voice according to the emotional state of the user. The voice generation unit dynamically adjusts the tone of the voice according to, for example, the emotional state of the user. For example, when the user wants to relax, a calm tone of voice is provided. The voice generation unit also dynamically adjusts the tempo of the voice according to the emotional state of the user. For example, when the user wants to concentrate, a fast tempo of voice is provided. The voice generation unit also adjusts the tone and tempo of the voice in real time based on the emotion estimation data of the user. For example, the voice characteristics are dynamically changed according to changes in the user's emotions. This makes it possible to adjust the tone and tempo of the voice according to the emotional state of the user.

[0056] If a user likes the voice of a particular voice actor or celebrity, the voice generation unit can generate a voice that imitates that voice. For example, the voice generation unit generates a voice that imitates the voice of a particular voice actor that the user likes. For example, a podcast is generated based on the voice of a voice actor that the user likes. The voice generation unit also generates a voice that imitates the voice of a particular celebrity that the user likes. For example, a podcast is generated based on the voice of an actor that the user likes. The voice generation unit also analyzes the user's preferences and generates a voice that imitates the voice of a particular voice actor or celebrity in real time. For example, the voice is changed according to the user's request. In this way, a voice that imitates a particular voice that the user likes can be provided.

[0057] The voice generation unit can use the emotion estimation function to analyze the characteristics of a voice that most relaxes the user and generate voice with those characteristics. The voice generation unit, for example, uses the emotion estimation function to analyze the characteristics of a voice that most relaxes the user. For example, it analyzes the user's heart rate and facial expression to identify a voice that has a high relaxing effect. The voice generation unit also generates voice with the characteristics of a voice that is relaxing based on the user's emotion estimation data. For example, it provides a voice that is optimal for when the user wants to relax. The voice generation unit also uses the emotion estimation function to analyze the characteristics of a voice that most relaxes the user in real time and dynamically generate voice with those characteristics. For example, it adjusts the voice according to changes in the user's emotions. This makes it possible to provide voice with the characteristics of a voice that most relaxes the user.

[0058] The audio generation unit can generate audio content that combines music and sound effects based on the characteristics of a user's preferred voice. The audio generation unit, for example, generates audio content that combines music based on the characteristics of a user's preferred voice. For example, a podcast that combines a voice and music that helps the user relax is provided. The audio generation unit also generates audio content that combines sound effects based on the characteristics of a user's preferred voice. For example, a podcast that combines a voice and sound effects that helps the user concentrate is provided. The audio generation unit also analyzes the user's preferences and combines music and sound effects that match the characteristics of the voice in real time. For example, the audio content is adjusted according to the user's request. This makes it possible to provide audio content that combines music and sound effects based on the characteristics of a user's preferred voice.

[0059] The voice generation unit can use the emotion estimation function to identify the voice characteristics that the user finds most comfortable and generate voice with those characteristics. The voice generation unit, for example, uses the emotion estimation function to identify the voice characteristics that the user finds most comfortable. For example, it analyzes the user's heart rate and facial expression to identify a comfortable voice. The voice generation unit also generates voice with the voice characteristics of a comfortable voice based on the user's emotion estimation data. For example, it provides a voice that is optimal for when the user wants to relax. The voice generation unit also uses the emotion estimation function to analyze the voice characteristics that the user finds comfortable in real time and dynamically generate voice with those characteristics. For example, it adjusts the voice in response to changes in the user's emotions. This makes it possible to provide voice with the voice characteristics that the user finds most comfortable.

[0060] The distribution unit can dynamically adjust the optimal distribution timing based on the user's schedule. The distribution unit, for example, analyzes the user's schedule and dynamically adjusts the optimal distribution timing. For example, if the user likes to listen to podcasts during their commute, distribution is performed to coincide with that time. The distribution unit also analyzes the user's lifestyle and dynamically adjusts the optimal distribution timing. For example, if the user likes to listen to podcasts during housework, distribution is performed to coincide with that time. The distribution unit also adjusts the optimal distribution timing in real time based on the user's schedule data. For example, the distribution timing is immediately changed if the user's plans change. This makes it possible to adjust the optimal distribution timing based on the user's schedule.

[0061] The distribution unit can collect user feedback in real time and improve the content based on that. For example, the distribution unit collects user feedback in real time and improves the content of the podcast based on that. For example, the distribution unit adjusts episodes based on user ratings and comments. The distribution unit also analyzes user feedback and improves the structure and topics of the podcast. For example, it reflects themes and formats preferred by users. The distribution unit also improves the content of the next episode based on the feedback collected in real time. For example, it adds new topics in response to user requests. This allows the content to be improved based on user feedback.

[0062] The distribution unit can use the emotion estimation function to distribute podcasts at times when the user can be most relaxed. For example, the distribution unit uses the emotion estimation function to identify times when the user can be most relaxed and distributes podcasts at those times. For example, the distribution unit analyzes the user's heart rate and facial expressions to select times when the user can be most relaxed. The distribution unit also distributes podcasts based on the user's emotion estimation data in accordance with the time when the user can be most relaxed. For example, the distribution unit selects the optimal time when the user wants to relax. The distribution unit also uses the emotion estimation function to analyze the time when the user can be most relaxed in real time and dynamically distributes podcasts at those times. For example, the distribution timing is adjusted according to changes in the user's emotions. This allows the podcast to be distributed at times when the user can be most relaxed.

[0063] The distribution unit can customize the content of the podcast to match the user's life events. For example, the distribution unit generates a special podcast episode to match the user's birthday. For example, the distribution unit provides an episode including a birthday message or special content. The distribution unit also generates a customized podcast to match the user's anniversary. For example, the distribution unit provides content related to a wedding anniversary or a work anniversary. The distribution unit also customizes the content of the podcast to match the user's life events. For example, the distribution unit covers topics related to the user's important events. This allows the content of the podcast to be customized to match the user's life events.

[0064] The distribution unit can enable the user to listen to podcasts on other devices. For example, the distribution unit can enable the user to listen to podcasts on a smart speaker. For example, distribution can be performed on devices such as Amazon Echo and Google Home. The distribution unit can also enable the user to listen to podcasts on an in-vehicle system. For example, distribution can be performed on devices such as Apple CarPlay and Android Auto. The distribution unit can also enable the user to seamlessly listen to podcasts on multiple devices. For example, synchronized distribution can be performed on smartphones, tablets, smart speakers, etc., thereby enabling the user to listen to podcasts on other devices.

[0065] The distribution unit can use the emotion estimation function to distribute podcasts at times when the user can enjoy the most. For example, the distribution unit uses the emotion estimation function to identify times when the user can enjoy the most and distribute the podcast at that time. For example, the distribution unit may analyze the user's heart rate and facial expression to select a time period when the user can enjoy the most. The distribution unit also distributes podcasts based on the user's emotion estimation data to match the time period when the user can enjoy the most. For example, the distribution unit may select a time period that is optimal for when the user wants to relax. The distribution unit also uses the emotion estimation function to analyze the time period when the user can enjoy the most in real time and dynamically distribute the podcast at that time. For example, the distribution timing may be adjusted according to changes in the user's emotions. This allows the podcast to be distributed at a time period when the user can enjoy the most.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The podcast generation system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, analyze the user's heart rate and sleep patterns and provide content appropriate to the user's health condition. For example, if the user is feeling stressed, the health management unit may provide relaxing music or podcasts. The health management unit may also analyze the user's exercise habits and provide energetic content suitable for listening to while exercising. The health management unit may also analyze the user's food records and provide content that supports a healthy diet. This allows the system to provide optimal content appropriate to the user's health condition.

[0068] The podcast generation system may further include an emotion estimation unit that estimates the user's emotional state and generates content based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expression and voice to estimate the emotion. For example, if the user is sad, the emotion estimation unit generates a podcast containing a message of comfort or encouragement. If the user is excited, the emotion estimation unit may generate energetic music or a podcast. Furthermore, if the user wants to relax, the emotion estimation unit may provide content with a high relaxing effect. This makes it possible to provide optimal content according to the user's emotional state.

[0069] The podcast generation system can further include an education support unit that provides educational content according to the user's learning needs. The education support unit, for example, analyzes the user's learning history and interests and provides optimal educational content based on that. For example, if the user is learning a language, it can provide podcasts that are useful for language learning. The education support unit can also monitor the user's learning progress and provide content according to that progress. Furthermore, the education support unit can provide educational content customized to the user's learning style. This makes it possible to provide optimal educational content according to the user's learning needs.

[0070] The podcast generation system may further include an emotion estimation unit that estimates the user's emotional state and generates content based on the estimated emotion. The emotion estimation unit may, for example, analyze the user's heart rate or facial expression to estimate the emotion. For example, if the user wants to relax, the emotion estimation unit may provide music or podcasts that have a relaxing effect. Alternatively, if the user wants to concentrate, the emotion estimation unit may provide music or podcasts that enhance concentration. Furthermore, the emotion estimation unit may dynamically adjust the content according to changes in the user's emotion. This allows the provision of optimal content according to the user's emotional state.

[0071] The podcast generation system can also provide content based on a user's preferences in text or video format, in addition to audio. For example, the podcast content can be converted into text and provided in a format that is easy for the user to read. Alternatively, the podcast content can be converted into video and provided in a format that is visually enjoyable. Furthermore, the podcast generation system can provide content based on a user's preferences in multiple formats, including audio, text, and video. This allows content based on a user's preferences to be provided in multiple formats.

[0072] The podcast generation system may further include an emotion estimation unit that estimates the user's emotional state and generates content based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expression and voice to estimate the emotion. For example, if the user wants to relax, the emotion estimation unit may provide music or podcasts with a relaxing effect. Alternatively, if the user wants to concentrate, the emotion estimation unit may provide music or podcasts that enhance concentration. Furthermore, the emotion estimation unit may dynamically adjust the content according to changes in the user's emotion. This allows the provision of optimal content according to the user's emotional state.

[0073] The podcast generation system can also provide content based on a user's preferences in text or video format, in addition to audio. For example, the podcast content can be converted into text and provided in a format that is easy for the user to read. Alternatively, the podcast content can be converted into video and provided in a format that is visually enjoyable. Furthermore, the podcast generation system can provide content based on a user's preferences in multiple formats, including audio, text, and video. This allows content based on a user's preferences to be provided in multiple formats.

[0074] The podcast generation system may further include an emotion estimation unit that estimates the user's emotional state and generates content based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expression and voice to estimate the emotion. For example, if the user wants to relax, the emotion estimation unit may provide music or podcasts with a relaxing effect. Alternatively, if the user wants to concentrate, the emotion estimation unit may provide music or podcasts that enhance concentration. Furthermore, the emotion estimation unit may dynamically adjust the content according to changes in the user's emotion. This allows the provision of optimal content according to the user's emotional state.

[0075] The podcast generation system can also provide content based on a user's preferences in text or video format, in addition to audio. For example, the podcast content can be converted into text and provided in a format that is easy for the user to read. Alternatively, the podcast content can be converted into video and provided in a format that is visually enjoyable. Furthermore, the podcast generation system can provide content based on a user's preferences in multiple formats, including audio, text, and video. This allows content based on a user's preferences to be provided in multiple formats.

[0076] The podcast generation system may further include an emotion estimation unit that estimates the user's emotional state and generates content based on the estimated emotion. The emotion estimation unit, for example, analyzes the user's facial expression and voice to estimate the emotion. For example, if the user wants to relax, the emotion estimation unit may provide music or podcasts with a relaxing effect. Alternatively, if the user wants to concentrate, the emotion estimation unit may provide music or podcasts that enhance concentration. Furthermore, the emotion estimation unit may dynamically adjust the content according to changes in the user's emotion. This allows the provision of optimal content according to the user's emotional state.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The content optimization unit optimizes content based on the user's preferences. For example, the generation AI analyzes the user's past viewing history and interests and suggests optimal content based on that. The input to the generation AI is a prompt containing the user's viewing history and interests, and the generation AI generates optimal content based on that prompt. Step 2: The voice generation unit generates voice based on the content optimized by the content optimization unit. For example, the generation AI analyzes the voice characteristics preferred by the user and generates voice based on that. The input to the generation AI is a prompt containing the voice characteristics preferred by the user, and the generation AI generates voice based on that prompt. Step 3: The podcast generation unit generates a podcast by combining the audio generated by the audio generation unit with the content optimized by the content optimization unit. For example, the generation AI combines the content and audio generated based on the user's preferences to automatically generate a podcast program. Step 4: The distribution unit distributes the podcast generated by the podcast generation unit to the user's device. For example, the generation AI analyzes the user's schedule and lifestyle and distributes the podcast at the optimal time.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 optimization unit that optimizes content based on user preferences; a sound generation unit that generates sound based on the content optimized by the content optimization unit; a podcast generation unit that generates a podcast by combining the audio generated by the audio generation unit and the content optimized by the content optimization unit; a distribution unit that distributes the podcast generated by the podcast generation unit to a user's device. A system characterized by:

2. The content optimization unit Analyzing the emotional state of the user in real time and generating the content that best suits the mood at that time 2. The system of claim 1.

3. The content optimization unit The content is generated based on the user's past viewing history as well as current trends and news.

2. The system of claim 1.

4. The content optimization unit Providing the user with the most suitable content when the user wants to relax or concentrate 2. The system of claim 1.

5. The content optimization unit Providing the content based on the user's preferences in text and video formats as well as audio.

2. The system of claim 1.

6. The content optimization unit Matching preferences between different users to generate group podcasts for users with common interests 2. The system of claim 1.

7. The content optimization unit Identifying topics that are of most interest to the user and generating new content based on those topics 2. The system of claim 1.

8. The voice generation unit Dynamically adjusting the tone and tempo of the voice depending on the emotional state of the user.

2. The system of claim 1.

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