system

Abstract

An object of a system according to an embodiment is to compensate for hearing loss of elderly people and activate communication.SOLUTION: A system according to an embodiment includes a TV speaker, a voice recognition unit, and a speech generation unit. The TV speaker is easy to hear. The voice recognition section recognizes voice from the TV speaker. The speech generation unit generates a response on the basis of the voice recognized by the voice recognition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not provided sufficient effective measures to prevent hearing loss and dementia in the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to compensate for the hearing loss of elderly people and stimulate communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a TV speaker, a voice recognition unit, and a conversation generation unit. The TV speaker makes it easy to hear voices. The voice recognition unit recognizes voices from the TV speaker. The conversation generation unit generates a response based on the voices recognized by the voice recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can compensate for the hearing loss of elderly people and stimulate communication. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The system according to the embodiment of the present invention is a system that complements the hearing of elderly people and stimulates communication by combining TV speakers that make speech easier to hear with AI-based voice recognition. This system can help prevent dementia and support a richer life both physically and mentally.

[0029] The system according to the embodiment includes a TV speaker, a voice recognition unit, and a conversation generation unit. The TV speaker is designed to make voices easier to hear. For example, it adjusts the frequency band of the voice to emphasize a range that is easy for elderly people to hear. The TV speaker can also make the voices of news and dramas sound clearer. The voice recognition unit recognizes voices from the TV speaker. For example, the voice recognition unit analyzes voice information from the TV using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice recognition unit can also recognize a user's voice input. The conversation generation unit generates a response based on the voice recognized by the voice recognition unit. For example, the conversation generation unit generates a conversation with the user using a generation AI. The conversation generation unit can also customize the voice according to the user's preferences. This allows the system to compensate for the hearing impairment of elderly people and stimulate communication.

[0030] TV speakers can add a function that makes individual adjustments based on the hearing characteristics of the elderly, making it possible to automatically generate the optimal audio profile for each user. For example, TV speakers can measure the hearing characteristics of the elderly in advance and generate an individual audio profile based on that data. For example, hearing test results can be input and settings can be automatically made to emphasize specific frequency bands. TV speakers can also build a system that automatically generates the optimal audio profile for each user. For example, the volume and frequency bands can be adjusted based on the user's hearing characteristics. This makes it possible to provide the optimal audio profile for each user.

[0031] TV speakers can reduce ambient noise by analyzing environmental sounds in real time and incorporating noise-canceling functions. For example, TV speakers use built-in microphones to collect ambient sounds in real time and apply noise-canceling algorithms to reduce household noise and external noise. TV speakers can also reduce ambient noise using active noise-canceling technology. For example, they can generate anti-phase sound waves to cancel out the noise. This reduces ambient noise and makes speech easier to hear.

[0032] TV speakers can be equipped with a vibration feedback function, allowing users to experience the rhythm or emphasis of audio. For example, TV speakers can have a built-in vibration motor that generates vibrations in time with the rhythm or emphasis of audio. For example, they vibrate in time with the beat of music. TV speakers can also generate vibrations in time with the emphasis of audio. For example, they can generate vibrations during action scenes in a movie. This allows users to experience the rhythm or emphasis of audio.

[0033] The audio output of a TV speaker can be linked with other home appliances to improve the viewing experience. For example, a TV speaker can create a system that automatically adjusts the brightness and color of smart lights based on the audio output. For example, the color of the lights can change to match the scene in a movie. Also, a TV speaker can automatically adjust the temperature of an air conditioner based on the audio output. For example, the temperature can be lowered to match a relaxing scene. This improves the viewing experience.

[0034] The speech recognition unit can learn from the user's past conversation history and generate more personalized responses. For example, the speech recognition unit stores the user's past conversation history in a database, and builds a system in which the generation AI learns from that data. For example, it generates responses that reflect the user's preferences and interests. The speech recognition unit can also ask appropriate questions to the user based on the past conversation history. For example, it can ask questions related to topics the user has previously discussed. This allows for more personalized responses to be provided.

[0035] The speech recognition unit can summarize the contents of news or programs in real time and provide concise information to users. For example, the speech recognition unit constructs a system in which a generation AI analyzes the contents of news or programs in real time and generates summaries. For example, it provides a concise summary of the main points of the news. The speech recognition unit can also summarize the contents of programs in real time and provide concise information to users. For example, it can provide a summary of the story of a drama. This allows the contents of news or programs to be provided concisely.

[0036] The voice recognition unit can suggest related information and content based on the user's hobbies or interests. For example, the voice recognition unit stores the user's hobbies and interests in a database, and a system is built in which the generation AI suggests related information and content based on that data. For example, it recommends movies and music that the user likes. The voice recognition unit can also suggest related news and articles based on the user's interests. For example, it can provide news related to topics that the user is interested in. This makes it possible to provide information and content based on the user's hobbies and interests.

[0037] The voice recognition unit can work with other smart devices to realize multi-device conversations. For example, the voice recognition unit's generation AI works with smartphones and tablets to build a system that realizes multi-device conversations. For example, a conversation on a TV can be handed over to a smartphone. The voice recognition unit can also work with other smart devices to share the user's voice input. For example, voice input on a tablet can be played on a TV speaker. This makes it possible to realize multi-device conversations.

[0038] The conversation generation unit collects the user's voice as a sample and allows the AI ​​to respond by imitating that voice. The conversation generation unit, for example, builds a system in which the conversation generation unit collects the user's voice as a sample and the generation AI responds by imitating that voice. For example, the user's voice is recorded and the generation AI responds based on that audio data. The conversation generation unit also allows the generation AI to generate natural responses based on the user's voice. For example, it learns the characteristics of the user's voice and generates responses based on that. This allows the system to respond by imitating the user's voice.

[0039] The conversation generation unit enables the voice customization function to be set to the voices of other family members or friends, enabling more diverse communication. For example, the conversation generation unit constructs a system in which a user collects voice samples of other family members or friends, and the generation AI responds by imitating those voices. For example, the user can record the voices of family members, and the generation AI responds based on that voice data. The conversation generation unit also enables the generation AI to generate natural responses based on the voices of other family members or friends. For example, the conversation generation unit can learn the characteristics of a friend's voice and generate a response based on that. This enables more diverse communication using the voices of other family members or friends.

[0040] The conversation generation unit can make the customized voice available to other applications. For example, the conversation generation unit develops an API for making the customized voice available to other applications. For example, it works with a navigation system or a smart home device. The conversation generation unit also builds a system for applying the customized voice to other applications. For example, it uses the customized voice for voice guidance in a smart home device. This allows the customized voice to be used in other applications.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] TV speakers can monitor the user's health and issue alerts if they detect any abnormalities. For example, they can have built-in sensors that measure heart rate and blood pressure, and issue a voice warning if they detect abnormal values. TV speakers can also send the user's health data to the cloud and connect with medical institutions. This allows users' health conditions to be monitored in real time, and any abnormalities can be dealt with quickly.

[0043] TV speakers can adjust the screen display based on the user's visual characteristics. For example, they can adjust color contrast for users with color blindness. They can also automatically increase text size for users with poor eyesight. They also have a night mode and can switch to a display that is easier on the eyes. This allows them to provide the optimal display for each user's visual characteristics.

[0044] TV speakers can adjust audio output based on the user's lifestyle. For example, they can play uplifting music in the morning and relaxing music in the evening. They can also provide voice reminders before important appointments based on the user's schedule. Furthermore, they can play relaxing music before the user goes to sleep to help them sleep well. This allows them to provide audio output that matches the user's lifestyle.

[0045] TV speakers can customize audio output based on user preferences. For example, a user can register their favorite music genres and artists, and the TV can recommend music based on that information. They can also learn a user's viewing history and recommend programs and movies that match their preferences. Furthermore, they can collect user feedback and improve the quality of the audio output. This allows the TV to provide audio output tailored to the user's preferences.

[0046] TV speakers can adjust audio output based on a user's activity level. For example, they can play invigorating music when the user is exercising and relaxing music when the user is relaxing. They can also collect user activity data and use it to help manage health. Furthermore, they can adjust the volume and tone of audio output according to the user's activity level. This allows them to provide audio output that matches the user's activity level.

[0047] The TV speaker can collect a sample of the user's voice and respond with an AI that imitates that voice. For example, the user's voice can be recorded and the AI ​​can respond based on that voice data. The AI ​​can also generate natural responses based on the user's voice. Furthermore, the user's voice can be set to the voice of another family member or friend. This allows the AI ​​to respond by imitating the user's voice.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: TV speakers are designed to make audio easier to hear. For example, they can adjust the audio frequency range to emphasize the range that is easier for elderly people to hear. They can also make the audio for news and dramas clearer. Step 2: The speech recognition unit recognizes the speech coming from the TV speaker. For example, it uses a generation AI (e.g., text generation AI or multimodal generation AI) to analyze the speech information coming from the TV. It can also recognize the user's voice input. Step 3: The conversation generation unit generates a response based on the voice recognized by the voice recognition unit. For example, a generation AI can be used to generate a conversation with the user and customize the voice according to the user's preferences.

[0050] (Example 2) The system according to the embodiment of the present invention is a system that complements the hearing of elderly people and stimulates communication by combining TV speakers that make speech easier to hear with AI-based voice recognition. This system can help prevent dementia and support a richer life both physically and mentally.

[0051] The system according to the embodiment includes a TV speaker, a voice recognition unit, and a conversation generation unit. The TV speaker is designed to make voices easier to hear. For example, it adjusts the frequency band of the voice to emphasize a range that is easy for elderly people to hear. The TV speaker can also make the voices of news and dramas sound clearer. The voice recognition unit recognizes voices from the TV speaker. For example, the voice recognition unit analyzes voice information from the TV using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice recognition unit can also recognize a user's voice input. The conversation generation unit generates a response based on the voice recognized by the voice recognition unit. For example, the conversation generation unit generates a conversation with the user using a generation AI. The conversation generation unit can also customize the voice according to the user's preferences. This allows the system to compensate for the hearing impairment of elderly people and stimulate communication.

[0052] TV speakers can add a function that makes individual adjustments based on the hearing characteristics of the elderly, making it possible to automatically generate the optimal audio profile for each user. For example, TV speakers can measure the hearing characteristics of the elderly in advance and generate an individual audio profile based on that data. For example, hearing test results can be input and settings can be automatically made to emphasize specific frequency bands. TV speakers can also build a system that automatically generates the optimal audio profile for each user. For example, the volume and frequency bands can be adjusted based on the user's hearing characteristics. This makes it possible to provide the optimal audio profile for each user.

[0053] TV speakers can reduce ambient noise by analyzing environmental sounds in real time and incorporating noise-canceling functions. For example, TV speakers use built-in microphones to collect ambient sounds in real time and apply noise-canceling algorithms to reduce household noise and external noise. TV speakers can also reduce ambient noise using active noise-canceling technology. For example, they can generate anti-phase sound waves to cancel out the noise. This reduces ambient noise and makes speech easier to hear.

[0054] TV speakers can use emotion estimation functions to automatically adjust the tone or volume of audio according to the user's emotional state. For example, TV speakers can analyze the user's facial expressions and tone of voice to build a system that estimates the user's emotional state. For example, they can soften the tone of the audio when the user is relaxed. TV speakers can also use emotion estimation functions to automatically adjust the volume of the audio according to the user's emotional state. For example, they can lower the volume of the audio when the user is concentrating. This allows the tone and volume of the audio to be adjusted according to the user's emotional state.

[0055] TV speakers can be equipped with a vibration feedback function, allowing users to experience the rhythm or emphasis of audio. For example, TV speakers can have a built-in vibration motor that generates vibrations in time with the rhythm or emphasis of audio. For example, they vibrate in time with the beat of music. TV speakers can also generate vibrations in time with the emphasis of audio. For example, they can generate vibrations during action scenes in a movie. This allows users to experience the rhythm or emphasis of audio.

[0056] The audio output of a TV speaker can be linked with other home appliances to improve the viewing experience. For example, a TV speaker can create a system that automatically adjusts the brightness and color of smart lights based on the audio output. For example, the color of the lights can change to match the scene in a movie. Also, a TV speaker can automatically adjust the temperature of an air conditioner based on the audio output. For example, the temperature can be lowered to match a relaxing scene. This improves the viewing experience.

[0057] Using the emotion estimation function, TV speakers can automatically play music or environmental sounds that are optimal for when the user is relaxing. For example, the TV speaker can analyze the user's emotional state in real time and build a system that automatically plays music that is optimal for when the user is relaxing. For example, classical music or nature sounds can be played. Also, using the emotion estimation function, TV speakers can automatically play environmental sounds that are optimal for when the user is relaxing. For example, the sound of waves or birds chirping can be played. This allows the user to be provided with the optimal music or environmental sounds when they are relaxing.

[0058] The speech recognition unit can learn from the user's past conversation history and generate more personalized responses. For example, the speech recognition unit stores the user's past conversation history in a database, and builds a system in which the generation AI learns from that data. For example, it generates responses that reflect the user's preferences and interests. The speech recognition unit can also ask appropriate questions to the user based on the past conversation history. For example, it can ask questions related to topics the user has previously discussed. This allows for more personalized responses to be provided.

[0059] The speech recognition unit can summarize the contents of news or programs in real time and provide concise information to users. For example, the speech recognition unit constructs a system in which a generation AI analyzes the contents of news or programs in real time and generates summaries. For example, it provides a concise summary of the main points of the news. The speech recognition unit can also summarize the contents of programs in real time and provide concise information to users. For example, it can provide a summary of the story of a drama. This allows the contents of news or programs to be provided concisely.

[0060] The speech recognition unit can use the emotion estimation function to select questions and topics that correspond to the user's emotions, thereby improving the quality of the conversation. For example, the speech recognition unit can analyze the user's emotional state in real time and build a system that selects appropriate questions and topics based on that data. For example, when the user is relaxed, it can provide light topics. The speech recognition unit can also use the emotion estimation function to ask questions that correspond to the user's emotions. For example, it can ask questions related to topics that the user is interested in. This makes it possible to provide questions and topics that correspond to the user's emotions.

[0061] The voice recognition unit can suggest related information and content based on the user's hobbies or interests. For example, the voice recognition unit stores the user's hobbies and interests in a database, and a system is built in which the generation AI suggests related information and content based on that data. For example, it recommends movies and music that the user likes. The voice recognition unit can also suggest related news and articles based on the user's interests. For example, it can provide news related to topics that the user is interested in. This makes it possible to provide information and content based on the user's hobbies and interests.

[0062] The voice recognition unit can work with other smart devices to realize multi-device conversations. For example, the voice recognition unit's generation AI works with smartphones and tablets to build a system that realizes multi-device conversations. For example, a conversation on a TV can be handed over to a smartphone. The voice recognition unit can also work with other smart devices to share the user's voice input. For example, voice input on a tablet can be played on a TV speaker. This makes it possible to realize multi-device conversations.

[0063] The speech recognition unit can use the emotion estimation function to suggest topics or activities that will help the user relax when they are feeling stressed. The speech recognition unit, for example, analyzes the user's emotional state in real time and builds a system that suggests topics that will help the user relax when they are feeling stressed. For example, it can provide topics that have a relaxing effect. The speech recognition unit can also use the emotion estimation function to suggest activities that will help the user relax when they are feeling stressed. For example, it can suggest deep breathing or light exercise. This makes it possible to provide topics or activities that will help the user relax when they are feeling stressed.

[0064] The conversation generation unit collects the user's voice as a sample and allows the AI ​​to respond by imitating that voice. The conversation generation unit, for example, builds a system in which the conversation generation unit collects the user's voice as a sample and the generation AI responds by imitating that voice. For example, the user's voice is recorded and the generation AI responds based on that audio data. The conversation generation unit also allows the generation AI to generate natural responses based on the user's voice. For example, it learns the characteristics of the user's voice and generates responses based on that. This allows the system to respond by imitating the user's voice.

[0065] The conversation generation unit can use the emotion estimation function to automatically adjust the tone or speed of the voice depending on the emotional state of the user. The conversation generation unit, for example, analyzes the emotional state of the user in real time and builds a system that automatically adjusts the tone and speed of the voice based on that data. For example, the tone of the voice can be softened when the user is relaxed. The conversation generation unit can also use the emotion estimation function to automatically adjust the speed of the voice depending on the emotional state of the user. For example, the speed of the voice can be increased when the user is in a hurry. In this way, the tone and speed of the voice can be adjusted depending on the emotional state of the user.

[0066] The conversation generation unit enables the voice customization function to be set to the voices of other family members or friends, enabling more diverse communication. For example, the conversation generation unit constructs a system in which a user collects voice samples of other family members or friends, and the generation AI responds by imitating those voices. For example, the user can record the voices of family members, and the generation AI responds based on that voice data. The conversation generation unit also enables the generation AI to generate natural responses based on the voices of other family members or friends. For example, the conversation generation unit can learn the characteristics of a friend's voice and generate a response based on that. This enables more diverse communication using the voices of other family members or friends.

[0067] The conversation generation unit can make the customized voice available to other applications. For example, the conversation generation unit develops an API for making the customized voice available to other applications. For example, it works with a navigation system or a smart home device. The conversation generation unit also builds a system for applying the customized voice to other applications. For example, it uses the customized voice for voice guidance in a smart home device. This allows the customized voice to be used in other applications.

[0068] The conversation generation unit can use the emotion estimation function to automatically apply optimal voice settings when the user feels a specific emotion. The conversation generation unit, for example, analyzes the user's emotional state in real time and builds a system that automatically applies optimal voice settings based on that data. For example, the tone of the voice can be softened when the user is relaxed. The conversation generation unit can also use the emotion estimation function to automatically apply optimal voice settings when the user feels a specific emotion. For example, the voice speed can be increased when the user is excited. This makes it possible to provide optimal voice settings when the user feels a specific emotion.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] TV speakers can monitor the user's health and issue alerts if they detect any abnormalities. For example, they can have built-in sensors that measure heart rate and blood pressure, and issue a voice warning if they detect abnormal values. TV speakers can also send the user's health data to the cloud and connect with medical institutions. This allows users' health conditions to be monitored in real time, and any abnormalities can be dealt with quickly.

[0071] TV speakers can adjust the screen display based on the user's visual characteristics. For example, they can adjust color contrast for users with color blindness. They can also automatically increase text size for users with poor eyesight. They also have a night mode and can switch to a display that is easier on the eyes. This allows them to provide the optimal display for each user's visual characteristics.

[0072] Using emotion estimation capabilities, TV speakers can automatically play relaxing music or environmental sounds when a user is feeling stressed. For example, if the TV analyzes the user's facial expression and tone of voice and determines that the user is feeling stressed, it can play classical music or natural sounds. It can also play relaxing music when the user is feeling relaxed. This allows the TV speaker to provide music and environmental sounds that match the user's emotional state.

[0073] TV speakers can adjust audio output based on the user's lifestyle. For example, they can play uplifting music in the morning and relaxing music in the evening. They can also provide voice reminders before important appointments based on the user's schedule. Furthermore, they can play relaxing music before the user goes to sleep to help them sleep well. This allows them to provide audio output that matches the user's lifestyle.

[0074] Using emotion estimation capabilities, TV speakers can automatically play calming music or environmental sounds when a user is excited. For example, by analyzing the user's facial expression and tone of voice, if the user is determined to be excited, the speakers can play calming music or natural sounds. Furthermore, when the user is relaxing, the speakers can play music with a relaxing effect. This allows the speakers to provide music and environmental sounds that match the user's emotional state.

[0075] TV speakers can customize audio output based on user preferences. For example, a user can register their favorite music genres and artists, and the TV can recommend music based on that information. They can also learn a user's viewing history and recommend programs and movies that match their preferences. Furthermore, they can collect user feedback and improve the quality of the audio output. This allows the TV to provide audio output tailored to the user's preferences.

[0076] Using emotion estimation, TV speakers can automatically play uplifting music or messages when a user is sad. For example, if a TV speaker analyzes a user's facial expression and tone of voice and determines that the user is sad, it can play uplifting music or an encouraging message. It can also play relaxing music when the user is relaxing. This allows the TV speaker to provide music and messages that match the user's emotional state.

[0077] TV speakers can adjust audio output based on a user's activity level. For example, they can play invigorating music when the user is exercising and relaxing music when the user is relaxing. They can also collect user activity data and use it to help manage health. Furthermore, they can adjust the volume and tone of audio output according to the user's activity level. This allows them to provide audio output that matches the user's activity level.

[0078] Using emotion estimation capabilities, TV speakers can automatically play music and environmental sounds that are optimal for when the user is relaxed. For example, by analyzing the user's facial expressions and vocal tone, if the speaker determines that the user is relaxed, it can play classical music or natural sounds. It can also play relaxing music when the user is feeling stressed. This allows the speaker to provide music and environmental sounds that match the user's emotional state.

[0079] The TV speaker can collect a sample of the user's voice and respond with an AI that imitates that voice. For example, the user's voice can be recorded and the AI ​​can respond based on that voice data. The AI ​​can also generate natural responses based on the user's voice. Furthermore, the user's voice can be set to the voice of another family member or friend. This allows the AI ​​to respond by imitating the user's voice.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: TV speakers are designed to make audio easier to hear. For example, they can adjust the audio frequency range to emphasize the range that is easier for elderly people to hear. They can also make the audio for news and dramas clearer. Step 2: The speech recognition unit recognizes the speech coming from the TV speaker. For example, it uses a generation AI (e.g., text generation AI or multimodal generation AI) to analyze the speech information coming from the TV. It can also recognize the user's voice input. Step 3: The conversation generation unit generates a response based on the voice recognized by the voice recognition unit. For example, a generation AI can be used to generate a conversation with the user and customize the voice according to the user's preferences.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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. TV speakers that make it easy to hear the audio, a voice recognition unit that recognizes voice from the TV speaker; a conversation generation unit that generates a response based on the speech recognized by the speech recognition unit; A system characterized by:

2. The TV speaker is Adds a function to individually adjust the sound based on the hearing characteristics of elderly people, automatically generating the optimal voice profile for each user.

2. The system of claim 1.

3. The TV speaker is Add vibration feedback to allow you to feel the rhythm or emphasis of the audio.

2. The system of claim 1.

4. The voice recognition unit Learn from a user's past conversation history to generate more personalized responses 2. The system of claim 1.

5. The conversation generation unit The AI ​​collects a sample of the user's voice and responds by imitating that voice.

2. The system of claim 1.

6. The TV speaker is Automatically adjust the tone or volume of the audio depending on the user's emotional state 2. The system of claim 1.

7. The voice recognition unit Select questions and topics based on the user's emotions to improve the quality of conversations 2. The system of claim 1.

8. The conversation generation unit Automatically apply optimal voice settings when the user feels a certain emotion 2. The system of claim 1.

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