system

Abstract

The system according to this embodiment aims to enable users with speech impairments to converse using a voice that closely resembles their own. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a generation unit, and a reflection unit. The collection unit collects past recordings and family voices. The learning unit learns the data collected by the collection unit. The generation unit generates speech based on the data learned by the learning unit. The reflection unit reflects the speech generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a user with a speech disorder to converse in a voice close to their own voice, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user with a speech disorder to converse in a voice close to their own voice.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a learning unit, a generation unit, and a reflection unit. The collection unit collects past recordings and the voices of family members. The learning unit learns the data collected by the collection unit. The generation unit generates voices based on the data learned by the learning unit. The reflection unit reflects the voices generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users with speech impairments to converse using a voice that closely resembles their own. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The speech synthesis system according to an embodiment of the present invention is a system that enables users with speech disorders to converse in a voice similar to their own. This speech synthesis system collects past recordings and family voices, and the AI ​​learns from this data to generate a voice that reflects the user's individuality. Furthermore, it uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. For example, the speech synthesis system collects past recordings of the user and family voices. Next, the AI ​​learns from this data and generates a voice that reflects the user's voice tone, accent, and speaking habits. Furthermore, the speech synthesis system uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. For example, when the user smiles, the AR technology reflects that expression in real time, providing natural communication to the other party. As a result, the speech synthesis system enables users with speech disorders to converse in a voice similar to their own.

[0029] The speech synthesis system according to this embodiment comprises a collection unit, a learning unit, a generation unit, and a reflection unit. The collection unit collects past recordings and the voices of family members. For example, the collection unit collects past recordings of the user and the voices of family members. The collection unit can also use AI to analyze the collected data and collect data for generating speech that reflects the user's personality. The learning unit learns from the data collected by the collection unit. For example, the learning unit learns from the collected data using a machine learning algorithm. The learning unit preprocesses the data and builds a model for generating speech that reflects the user's personality. The generation unit generates speech based on the data learned by the learning unit. For example, the generation unit uses speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking habits. The generation unit can also evaluate the quality of the generated speech and select the optimal speech. The reflection unit reflects the speech generated by the generation unit. For example, the reflection unit uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs the generated voice in real time, supporting user communication. As a result, the voice synthesis system according to this embodiment enables users with speech impairments to converse in a voice that closely resembles their own.

[0030] The data collection unit collects past recordings and family voices. Specifically, it collects audio data previously recorded by the user, as well as audio data containing family voices. This includes methods for collecting audio files stored on devices such as smartphones, PCs, and tablets. Audio data stored on cloud storage services is also included in the collection. The data collection unit centrally manages this audio data and stores it in a database. Furthermore, the data collection unit uses AI to analyze the collected data and extract features necessary to generate voices that reflect the user's individuality. For example, it analyzes features such as pitch, tone, accent, and speaking habits, and uses this information to prepare the data necessary for speech synthesis. The data collection unit can also evaluate the quality of the audio data and perform pre-processing such as noise reduction and volume adjustment. As a result, the data collection unit can collect high-quality audio data that reflects the user's individuality and build a foundation for speech synthesis.

[0031] The learning unit learns from the data collected by the collection unit. Specifically, it uses machine learning algorithms to learn from the collected audio data and build a model to generate speech that reflects the user's individuality. First, the learning unit preprocesses the data, removing noise, normalizing volume, and extracting speech. Next, it extracts speech features using the preprocessed data and trains the machine learning model based on these features. The learning unit uses deep learning techniques to learn patterns and features of the audio data and builds a speech synthesis model that reflects the user's voice tone, accent, and speaking habits. The learning unit evaluates the performance of the model obtained during the learning process and adjusts the model parameters as needed. In this way, the learning unit can build a speech synthesis model that accurately reflects the user's individuality and provide it to the generation unit.

[0032] The generation unit generates speech based on data learned by the learning unit. Specifically, it uses speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking style. The generation unit uses the speech synthesis model provided by the learning unit to input text data and generate corresponding speech. The generation unit evaluates the quality of the generated speech, checking its sound quality and naturalness. For example, it evaluates how closely the generated speech resembles the user's voice and whether the speaking style is accurately reproduced. Based on the quality evaluation results, the generation unit can select the optimal speech and regenerate it as needed. The generation unit prepares the generated speech for real-time output and provides it to the reflection unit. This allows the generation unit to generate high-quality speech that reflects the user's individuality and support user communication.

[0033] The reflection unit reflects the audio generated by the generation unit. Specifically, it uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs the generated audio in real time to support user communication. For example, when a user is having a face-to-face conversation, the generated audio is output from the speaker and synchronized with the user's facial expressions and gestures. The reflection unit adjusts the timing and volume of the audio output to achieve natural communication. Furthermore, the reflection unit can apply the generated audio to other communication methods such as text chat and video calls. This allows users to communicate using a voice similar to their own in various situations. In addition, the reflection unit can collect user feedback and continuously improve the quality and naturalness of the generated audio. In this way, the reflection unit supports user communication and enables users with speech impairments to converse using a voice similar to their own.

[0034] The collection unit can collect the user's past recordings and the voices of family members. For example, the collection unit can collect the user's past recordings. The collection unit can collect recordings from a specific period or recordings of a specific event. The collection unit can collect the voices of family members. The collection unit can collect the voices of immediate family members and relatives. This makes it possible to generate voices that reflect the user's personality by collecting the user's past recordings and the voices of family members. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past recordings and the voices of family members into an AI, which can analyze the data and select the voice data to collect.

[0035] The learning unit can learn from collected data and generate speech that reflects the user's personality. For example, the learning unit learns from the collected data using a machine learning algorithm. The learning unit preprocesses the data and builds a model for generating speech that reflects the user's personality. The learning unit learns the user's voice tone, accent, and speaking habits, and generates speech that reflects them. This makes it possible to generate speech that reflects the user's personality. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input collected data into an AI, which can learn from the data and build a speech generation model.

[0036] The generation unit can learn the user's voice tone, accent, and speaking habits, and generate speech that reflects them. For example, the generation unit can use speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking habits. The generation unit can also evaluate the quality of the generated speech and select the optimal speech. This makes it possible to generate speech that reflects the user's voice tone, accent, and speaking habits. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input the user's voice tone, accent, and speaking habits into an AI, and the AI ​​can generate speech.

[0037] The reflection unit can naturally reflect facial expressions and gestures during face-to-face communication. For example, the reflection unit uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs generated audio in real time to support user communication. This enables natural communication by naturally reflecting facial expressions and gestures during face-to-face communication. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can input the user's facial expressions and gestures into the AI, which can then reflect them in real time.

[0038] The data collection unit can evaluate the quality of audio and select the optimal audio data when collecting past recordings of the user or family members. For example, the data collection unit can evaluate the noise level of the audio and select audio data with less noise. The data collection unit can also evaluate the clarity of the audio and select clear audio data. The data collection unit can also evaluate the naturalness of the audio and select natural-sounding audio data. By evaluating the quality of the audio and selecting the optimal audio data, higher quality audio generation becomes possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input audio data into an AI, which can then evaluate the quality of the audio and select the optimal audio data.

[0039] The data collection unit can adjust the timing of audio data collection based on the user's living environment and activity level. For example, the data collection unit will collect audio data when the user is in a quiet environment. If the user is active, the data collection unit can wait until the activity is finished before collecting data. The data collection unit can also collect audio data during times when the user is relaxed. By adjusting the timing of collection based on the user's living environment and activity level, audio data can be collected at a more appropriate time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's living environment and activity level into the AI, which can then adjust the timing of collection.

[0040] The learning unit can analyze the characteristics of the audio data in detail during training and optimize the learning algorithm to more accurately reflect the user's individuality. For example, the learning unit can analyze the tone and accent of the audio data in detail and optimize the learning algorithm. The learning unit can also analyze the speech habits of the audio data in detail and optimize the learning algorithm. The learning unit can also analyze the rhythm and tempo of the audio data in detail and optimize the learning algorithm. As a result, by analyzing the characteristics of the audio data in detail and optimizing the learning algorithm, it becomes possible to generate voices that more accurately reflect the user's individuality. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input audio data into AI, and the AI ​​can analyze the data and optimize the learning algorithm.

[0041] The learning unit can continuously update the learning data during the learning process, taking into account changes and growth in the user's voice. For example, the learning unit can update the learning data by considering changes in the user's voice tone and accent. The learning unit can also update the learning data by considering changes in the user's speaking habits. The learning unit can also update the learning data by considering changes in the user's rhythm and tempo. This ensures that speech generation is always based on the latest data by updating the learning data in consideration of changes and growth in the user's voice. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input changes and growth in the user's voice into the AI, and the AI ​​can update the data.

[0042] The generation unit can optimize its generation algorithm to reflect the user's speech habits and characteristics in detail during speech generation. For example, the generation unit can optimize its generation algorithm to reflect the user's speaking rhythm. The generation unit can also optimize its generation algorithm to reflect the user's accent. The generation unit can also optimize its generation algorithm to reflect the user's speaking tempo. This allows for more personalized speech generation by reflecting the user's speech habits and characteristics in detail. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's speech habits and characteristics into the AI, which can then optimize the generation algorithm.

[0043] The generation unit can continuously update the generated speech by taking into account changes and growth in the user's voice during speech generation. For example, the generation unit can update the speech by taking into account changes in the user's voice tone. The generation unit can also update the speech by taking into account changes in the user's accent. The generation unit can also update the speech by taking into account changes in the user's speaking habits. This ensures that the latest speech is always generated by updating the speech by taking into account changes and growth in the user's voice. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input changes and growth in the user's voice into the AI, and the AI ​​can update the data.

[0044] The reflection unit can select the optimal reflection method by referring to the user's past communication history when reflecting facial expressions and gestures. For example, the reflection unit can reflect facial expressions and gestures that the user has used in the past. The reflection unit can also select the optimal facial expressions and gestures from the user's past communication history. The reflection unit can also analyze and reflect the user's past communication patterns. This makes it possible to reflect more appropriate facial expressions and gestures by referring to the user's past communication history. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can input the user's communication history into AI, and the AI ​​can select the optimal reflection method.

[0045] The reflection unit can customize the reflection method when reflecting facial expressions and gestures, taking into account the user's living environment and activity level. For example, if the user is in a quiet environment, the reflection unit will reflect calm facial expressions and gestures. If the user is active, the reflection unit can also reflect lively facial expressions and gestures. The reflection unit can also reflect facial expressions and gestures during times when the user is relaxed. By customizing the reflection method to take into account the user's living environment and activity level, it becomes possible to reflect more appropriate facial expressions and gestures. Some or all of the above processing in the reflection unit may be performed using AI, or it may be performed without AI. For example, the reflection unit can input the user's living environment and activity level into the AI, which can then customize the reflection method.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The data collection unit can adjust the type of audio data collected based on the user's health condition. For example, if the user has a cold, it can collect different audio data than usual. The data collection unit can also adjust the frequency of audio data collection according to the user's health condition. For example, it can collect audio data frequently when the user is healthy and reduce the frequency when the user is unwell. This enables the collection of audio data tailored to the user's health condition, resulting in more accurate voice generation.

[0048] The learning unit can select learning data that takes into account the user's lifestyle when learning from the user's voice data. For example, if the user is a morning person, it can prioritize learning from voice data collected during the morning hours. The learning unit can also adjust the types of learning data according to the user's lifestyle. For example, if the user is a night owl, it can prioritize learning from voice data collected during the night hours. This makes it possible to select learning data that matches the user's lifestyle, resulting in more natural-sounding speech generation.

[0049] The voice generation unit can adjust the tone and accent of the voice to reflect the user's hobbies and interests when generating the user's voice. For example, if the user is interested in music, the voice can be generated with a tone and accent that reflects music-related characteristics. The voice generation unit can also adjust the content of the voice according to the user's hobbies and interests. For example, if the user is interested in sports, the voice can be generated that includes sports-related content. This makes it possible to generate voices that are tailored to the user's hobbies and interests, resulting in more personalized voice generation.

[0050] The voice reflection unit can adjust the output method of the voice, taking into account the user's communication style, when reflecting the user's voice. For example, if the user has a slow speaking style, the voice can be output at a slow pace. The voice reflection unit can also adjust the tone and accent of the voice according to the user's communication style. For example, if the user has a bright speaking style, the voice can be output in a bright tone. This enables voice output that matches the user's communication style, resulting in more natural communication.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The collection unit collects past recordings and family voices. For example, it collects past recordings of the user and family voices. The collection unit can also use AI to analyze the collected data and gather data to generate voices that reflect the user's personality. Step 2: The learning unit learns from the data collected by the collection unit. For example, it learns from the collected data using a machine learning algorithm. The learning unit preprocesses the data and builds a model to generate speech that reflects the user's individuality. Step 3: The generation unit generates speech based on the data learned by the learning unit. For example, it uses speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking habits. The generation unit can also evaluate the quality of the generated speech and select the optimal speech. Step 4: The reflection unit reflects the audio generated by the generation unit. For example, it uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs the generated audio in real time to support user communication.

[0053] (Example of form 2) The speech synthesis system according to an embodiment of the present invention is a system that enables users with speech disorders to converse in a voice similar to their own. This speech synthesis system collects past recordings and family voices, and the AI ​​learns from this data to generate a voice that reflects the user's individuality. Furthermore, it uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. For example, the speech synthesis system collects past recordings of the user and family voices. Next, the AI ​​learns from this data and generates a voice that reflects the user's voice tone, accent, and speaking habits. Furthermore, the speech synthesis system uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. For example, when the user smiles, the AR technology reflects that expression in real time, providing natural communication to the other party. As a result, the speech synthesis system enables users with speech disorders to converse in a voice similar to their own.

[0054] The speech synthesis system according to this embodiment comprises a collection unit, a learning unit, a generation unit, and a reflection unit. The collection unit collects past recordings and the voices of family members. For example, the collection unit collects past recordings of the user and the voices of family members. The collection unit can also use AI to analyze the collected data and collect data for generating speech that reflects the user's personality. The learning unit learns from the data collected by the collection unit. For example, the learning unit learns from the collected data using a machine learning algorithm. The learning unit preprocesses the data and builds a model for generating speech that reflects the user's personality. The generation unit generates speech based on the data learned by the learning unit. For example, the generation unit uses speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking habits. The generation unit can also evaluate the quality of the generated speech and select the optimal speech. The reflection unit reflects the speech generated by the generation unit. For example, the reflection unit uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs the generated voice in real time, supporting user communication. As a result, the voice synthesis system according to this embodiment enables users with speech impairments to converse in a voice that closely resembles their own.

[0055] The data collection unit collects past recordings and family voices. Specifically, it collects audio data previously recorded by the user, as well as audio data containing family voices. This includes methods for collecting audio files stored on devices such as smartphones, PCs, and tablets. Audio data stored on cloud storage services is also included in the collection. The data collection unit centrally manages this audio data and stores it in a database. Furthermore, the data collection unit uses AI to analyze the collected data and extract features necessary to generate voices that reflect the user's individuality. For example, it analyzes features such as pitch, tone, accent, and speaking habits, and uses this information to prepare the data necessary for speech synthesis. The data collection unit can also evaluate the quality of the audio data and perform pre-processing such as noise reduction and volume adjustment. As a result, the data collection unit can collect high-quality audio data that reflects the user's individuality and build a foundation for speech synthesis.

[0056] The learning unit learns from the data collected by the collection unit. Specifically, it uses machine learning algorithms to learn from the collected audio data and build a model to generate speech that reflects the user's individuality. First, the learning unit preprocesses the data, removing noise, normalizing volume, and extracting speech. Next, it extracts speech features using the preprocessed data and trains the machine learning model based on these features. The learning unit uses deep learning techniques to learn patterns and features of the audio data and builds a speech synthesis model that reflects the user's voice tone, accent, and speaking habits. The learning unit evaluates the performance of the model obtained during the learning process and adjusts the model parameters as needed. In this way, the learning unit can build a speech synthesis model that accurately reflects the user's individuality and provide it to the generation unit.

[0057] The generation unit generates speech based on data learned by the learning unit. Specifically, it uses speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking style. The generation unit uses the speech synthesis model provided by the learning unit to input text data and generate corresponding speech. The generation unit evaluates the quality of the generated speech, checking its sound quality and naturalness. For example, it evaluates how closely the generated speech resembles the user's voice and whether the speaking style is accurately reproduced. Based on the quality evaluation results, the generation unit can select the optimal speech and regenerate it as needed. The generation unit prepares the generated speech for real-time output and provides it to the reflection unit. This allows the generation unit to generate high-quality speech that reflects the user's individuality and support user communication.

[0058] The reflection unit reflects the audio generated by the generation unit. Specifically, it uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs the generated audio in real time to support user communication. For example, when a user is having a face-to-face conversation, the generated audio is output from the speaker and synchronized with the user's facial expressions and gestures. The reflection unit adjusts the timing and volume of the audio output to achieve natural communication. Furthermore, the reflection unit can apply the generated audio to other communication methods such as text chat and video calls. This allows users to communicate using a voice similar to their own in various situations. In addition, the reflection unit can collect user feedback and continuously improve the quality and naturalness of the generated audio. In this way, the reflection unit supports user communication and enables users with speech impairments to converse using a voice similar to their own.

[0059] The collection unit can collect the user's past recordings and the voices of family members. For example, the collection unit can collect the user's past recordings. The collection unit can collect recordings from a specific period or recordings of a specific event. The collection unit can collect the voices of family members. The collection unit can collect the voices of immediate family members and relatives. This makes it possible to generate voices that reflect the user's personality by collecting the user's past recordings and the voices of family members. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past recordings and the voices of family members into an AI, which can analyze the data and select the voice data to collect.

[0060] The learning unit can learn from collected data and generate speech that reflects the user's personality. For example, the learning unit learns from the collected data using a machine learning algorithm. The learning unit preprocesses the data and builds a model for generating speech that reflects the user's personality. The learning unit learns the user's voice tone, accent, and speaking habits, and generates speech that reflects them. This makes it possible to generate speech that reflects the user's personality. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input collected data into an AI, which can learn from the data and build a speech generation model.

[0061] The generation unit can learn the user's voice tone, accent, and speaking habits, and generate speech that reflects them. For example, the generation unit can use speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking habits. The generation unit can also evaluate the quality of the generated speech and select the optimal speech. This makes it possible to generate speech that reflects the user's voice tone, accent, and speaking habits. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input the user's voice tone, accent, and speaking habits into an AI, and the AI ​​can generate speech.

[0062] The reflection unit can naturally reflect facial expressions and gestures during face-to-face communication. For example, the reflection unit uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs generated audio in real time to support user communication. This enables natural communication by naturally reflecting facial expressions and gestures during face-to-face communication. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can input the user's facial expressions and gestures into the AI, which can then reflect them in real time.

[0063] The collection unit can estimate the user's emotions and adjust the type of audio data collected based on the estimated emotions. For example, if the user is stressed, the collection unit will prioritize collecting relaxing audio data. If the user is relaxed, the collection unit can also collect detailed audio data. If the user is in a hurry, the collection unit can also prioritize audio data that can be collected quickly. This allows for the collection of more appropriate audio data by adjusting the type of audio data collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into an AI, which can then estimate the emotions and adjust the type of audio data collected.

[0064] The data collection unit can evaluate the quality of audio and select the optimal audio data when collecting past recordings of the user or family members. For example, the data collection unit can evaluate the noise level of the audio and select audio data with less noise. The data collection unit can also evaluate the clarity of the audio and select clear audio data. The data collection unit can also evaluate the naturalness of the audio and select natural-sounding audio data. By evaluating the quality of the audio and selecting the optimal audio data, higher quality audio generation becomes possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input audio data into an AI, which can then evaluate the quality of the audio and select the optimal audio data.

[0065] The data collection unit can adjust the timing of audio data collection based on the user's living environment and activity level. For example, the data collection unit will collect audio data when the user is in a quiet environment. If the user is active, the data collection unit can wait until the activity is finished before collecting data. The data collection unit can also collect audio data during times when the user is relaxed. By adjusting the timing of collection based on the user's living environment and activity level, audio data can be collected at a more appropriate time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's living environment and activity level into the AI, which can then adjust the timing of collection.

[0066] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is stressed, the learning unit can also select simpler training data. If the user is in a hurry, the learning unit can also select data that can be learned in a short time. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI, which can estimate the emotions and select training data.

[0067] The learning unit can analyze the characteristics of the audio data in detail during training and optimize the learning algorithm to more accurately reflect the user's individuality. For example, the learning unit can analyze the tone and accent of the audio data in detail and optimize the learning algorithm. The learning unit can also analyze the speech habits of the audio data in detail and optimize the learning algorithm. The learning unit can also analyze the rhythm and tempo of the audio data in detail and optimize the learning algorithm. As a result, by analyzing the characteristics of the audio data in detail and optimizing the learning algorithm, it becomes possible to generate voices that more accurately reflect the user's individuality. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input audio data into AI, and the AI ​​can analyze the data and optimize the learning algorithm.

[0068] The learning unit can continuously update the learning data during the learning process, taking into account changes and growth in the user's voice. For example, the learning unit can update the learning data by considering changes in the user's voice tone and accent. The learning unit can also update the learning data by considering changes in the user's speaking habits. The learning unit can also update the learning data by considering changes in the user's rhythm and tempo. This ensures that speech generation is always based on the latest data by updating the learning data in consideration of changes and growth in the user's voice. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input changes and growth in the user's voice into the AI, and the AI ​​can update the data.

[0069] The generation unit can estimate the user's emotions and adjust the tone and accent of the generated voice based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a voice in a calm tone. If the user is excited, the generation unit can also generate a voice in an energetic tone. If the user is sad, the generation unit can also generate a voice in a gentle tone. This allows for more natural voice generation by adjusting the tone and accent of the voice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can estimate the emotion and adjust the tone and accent of the voice.

[0070] The generation unit can optimize its generation algorithm to reflect the user's speech habits and characteristics in detail during speech generation. For example, the generation unit can optimize its generation algorithm to reflect the user's speaking rhythm. The generation unit can also optimize its generation algorithm to reflect the user's accent. The generation unit can also optimize its generation algorithm to reflect the user's speaking tempo. This allows for more personalized speech generation by reflecting the user's speech habits and characteristics in detail. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's speech habits and characteristics into the AI, which can then optimize the generation algorithm.

[0071] The generation unit can continuously update the generated speech by taking into account changes and growth in the user's voice during speech generation. For example, the generation unit can update the speech by taking into account changes in the user's voice tone. The generation unit can also update the speech by taking into account changes in the user's accent. The generation unit can also update the speech by taking into account changes in the user's speaking habits. This ensures that the latest speech is always generated by updating the speech by taking into account changes and growth in the user's voice. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input changes and growth in the user's voice into the AI, and the AI ​​can update the data.

[0072] The reflection unit can estimate the user's emotions and adjust how facial expressions and gestures are reflected based on the estimated emotions. For example, if the user is relaxed, the reflection unit will reflect a natural smile. If the user is excited, the reflection unit can also reflect lively gestures. If the user is sad, the reflection unit can also reflect a gentle expression. This allows for more natural communication by adjusting how facial expressions and gestures are reflected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can input user emotion data into an AI, which can estimate the emotion and adjust how facial expressions and gestures are reflected.

[0073] The reflection unit can select the optimal reflection method by referring to the user's past communication history when reflecting facial expressions and gestures. For example, the reflection unit can reflect facial expressions and gestures that the user has used in the past. The reflection unit can also select the optimal facial expressions and gestures from the user's past communication history. The reflection unit can also analyze and reflect the user's past communication patterns. This makes it possible to reflect more appropriate facial expressions and gestures by referring to the user's past communication history. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can input the user's communication history into AI, and the AI ​​can select the optimal reflection method.

[0074] The reflection unit can customize the reflection method when reflecting facial expressions and gestures, taking into account the user's living environment and activity level. For example, if the user is in a quiet environment, the reflection unit will reflect calm facial expressions and gestures. If the user is active, the reflection unit can also reflect lively facial expressions and gestures. The reflection unit can also reflect facial expressions and gestures during times when the user is relaxed. By customizing the reflection method to take into account the user's living environment and activity level, it becomes possible to reflect more appropriate facial expressions and gestures. Some or all of the above processing in the reflection unit may be performed using AI, or it may be performed without AI. For example, the reflection unit can input the user's living environment and activity level into the AI, which can then customize the reflection method.

[0075] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0076] The data collection unit can adjust the type of audio data collected based on the user's health condition. For example, if the user has a cold, it can collect different audio data than usual. The data collection unit can also adjust the frequency of audio data collection according to the user's health condition. For example, it can collect audio data frequently when the user is healthy and reduce the frequency when the user is unwell. This enables the collection of audio data tailored to the user's health condition, resulting in more accurate voice generation.

[0077] The learning unit can select learning data that takes into account the user's lifestyle when learning from the user's voice data. For example, if the user is a morning person, it can prioritize learning from voice data collected during the morning hours. The learning unit can also adjust the types of learning data according to the user's lifestyle. For example, if the user is a night owl, it can prioritize learning from voice data collected during the night hours. This makes it possible to select learning data that matches the user's lifestyle, resulting in more natural-sounding speech generation.

[0078] The voice generation unit can adjust the tone and accent of the voice to reflect the user's hobbies and interests when generating the user's voice. For example, if the user is interested in music, the voice can be generated with a tone and accent that reflects music-related characteristics. The voice generation unit can also adjust the content of the voice according to the user's hobbies and interests. For example, if the user is interested in sports, the voice can be generated that includes sports-related content. This makes it possible to generate voices that are tailored to the user's hobbies and interests, resulting in more personalized voice generation.

[0079] The voice reflection unit can adjust the output method of the voice, taking into account the user's communication style, when reflecting the user's voice. For example, if the user has a slow speaking style, the voice can be output at a slow pace. The voice reflection unit can also adjust the tone and accent of the voice according to the user's communication style. For example, if the user has a bright speaking style, the voice can be output in a bright tone. This enables voice output that matches the user's communication style, resulting in more natural communication.

[0080] The data collection unit can estimate the user's emotions and adjust the type of audio data collected based on those emotions. For example, if the user is stressed, it will prioritize collecting relaxing audio data. If the user is relaxed, the data collection unit can also collect detailed audio data. If the user is in a hurry, the data collection unit can also prioritize audio data that can be collected quickly. By adjusting the type of audio data collected according to the user's emotions, more appropriate audio data can be collected.

[0081] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is relaxed, it can select detailed training data. If the user is stressed, the learning unit can select simpler training data. If the user is in a hurry, the learning unit can select data that can be learned in a short amount of time. This allows for more appropriate learning by selecting training data according to the user's emotions.

[0082] The voice generator can estimate the user's emotions and adjust the tone and accent of the generated voice based on those emotions. For example, if the user is relaxed, it will generate a calm tone of voice. If the user is excited, the generator can also generate a lively tone of voice. If the user is sad, the generator can also generate a gentle tone of voice. By adjusting the tone and accent of the voice according to the user's emotions, it becomes possible to generate more natural-sounding voices.

[0083] The reflection unit can estimate the user's emotions and adjust how facial expressions and gestures are reflected based on the estimated emotions. For example, if the user is relaxed, it will reflect a natural smile. If the user is excited, the reflection unit can also reflect lively gestures. If the user is sad, the reflection unit can also reflect a gentle expression. By adjusting how facial expressions and gestures are reflected according to the user's emotions, more natural communication becomes possible.

[0084] The feedback unit can estimate the user's emotions and adjust the timing of voice output based on the estimated emotions. For example, if the user is relaxed, it will output voice at a slow pace. If the user is excited, the feedback unit can output voice at a faster pace. If the user is sad, the feedback unit can output voice in a gentle tone. By adjusting the timing of voice output according to the user's emotions, more natural communication becomes possible.

[0085] The feedback unit can estimate the user's emotions and adjust the voice output method based on the estimated emotions. For example, if the user is relaxed, it will output voice in a calm tone. If the user is excited, the feedback unit can output voice in an energetic tone. If the user is sad, the feedback unit can output voice in a gentle tone. By adjusting the voice output method according to the user's emotions, more natural communication becomes possible.

[0086] The following briefly describes the processing flow for example form 2.

[0087] Step 1: The collection unit collects past recordings and family voices. For example, it collects past recordings of the user and family voices. The collection unit can also use AI to analyze the collected data and gather data to generate voices that reflect the user's personality. Step 2: The learning unit learns from the data collected by the collection unit. For example, it learns from the collected data using a machine learning algorithm. The learning unit preprocesses the data and builds a model to generate speech that reflects the user's individuality. Step 3: The generation unit generates speech based on the data learned by the learning unit. For example, it uses speech synthesis technology to generate speech that reflects the user's voice tone, accent, and speaking habits. The generation unit can also evaluate the quality of the generated speech and select the optimal speech. Step 4: The reflection unit reflects the audio generated by the generation unit. For example, it uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The reflection unit outputs the generated audio in real time to support user communication.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0090] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, and reflection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects past recordings and family voices using the camera 42 and microphone 38B of the smart device 14. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates voice that reflects the user's voice tone, accent, and speaking habits. The reflection unit is implemented in the control unit 46A of the smart device 14 and uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0092] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0093] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0098] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0099] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0100] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0101] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0102] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, and reflection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect past recordings and family voices. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates voice that reflects the user's voice tone, accent, and speaking habits. The reflection unit is implemented in the control unit 46A of the smart glasses 214 and uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0108] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0109] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0112] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, and reflection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect past recordings and family voices. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates voice that reflects the user's voice tone, accent, and speaking habits. The reflection unit is implemented in the control unit 46A of the headset terminal 314 and uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0125] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0128] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0132] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, learning unit, generation unit, and reflection unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect past recordings and family voices. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates voice that reflects the user's voice tone, accent, and speaking habits. The reflection unit is implemented, for example, by the control unit 46A of the robot 414, and uses AR technology to naturally reflect facial expressions and gestures during face-to-face communication. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0143] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0144] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0145] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0149] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0150] 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.

[0151] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0152] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0153] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0154] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0156] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0157] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0158] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0159] (Note 1) The collection department collects past recordings and family voices, A learning unit that learns from the data collected by the aforementioned collection unit, A generation unit that generates speech based on data learned by the learning unit, The system includes a reflecting unit that reflects the sound generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects past recordings of the user and the voices of family members. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, It learns from collected data and generates voices that reflect the user's personality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It learns the user's voice tone, accent, and speaking habits, and generates speech that reflects them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reflection unit is, It naturally reflects facial expressions and gestures used during face-to-face communication. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the type of audio data collected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting past recordings of the user or family members' voices, the system evaluates the audio quality and selects the best audio data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting voice data, the timing of data collection is adjusted based on the user's living environment and activity status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, the features of the audio data are analyzed in detail to optimize the learning algorithm for a more accurate reflection of the user's individuality. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During the learning process, the learning data is continuously updated to take into account changes and growth in user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the tone and accent of the generated voice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Optimize the speech generation algorithm to accurately reflect the user's speech habits and characteristics during voice generation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During voice generation, the generated voice is continuously updated to take into account changes and growth in the user's voice. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reflection unit is, It estimates the user's emotions and adjusts how facial expressions and gestures are reflected based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reflection unit is, When reflecting facial expressions and gestures, the system selects the optimal method of reflection by referring to the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reflection unit is, When reflecting facial expressions and gestures, the method of reflection is customized to take into account the user's living environment and activity status. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects past recordings and family voices, A learning unit that learns from the data collected by the aforementioned collection unit, A generation unit that generates speech based on data learned by the learning unit, The system includes a reflecting unit that reflects the sound generated by the generation unit. A system characterized by the following features.

2. The aforementioned learning unit, It learns from collected data and generates voices that reflect the user's personality. The system according to feature 1.

3. The generating unit is It learns the user's voice tone, accent, and speaking habits, and generates speech that reflects them. The system according to feature 1.

4. The aforementioned reflection unit is, It naturally reflects facial expressions and gestures used during face-to-face communication. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the type of audio data collected based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting past recordings of the user or family members' voices, the system evaluates the audio quality and selects the best audio data. The system according to feature 1.

7. The aforementioned collection unit is When collecting voice data, the timing of data collection is adjusted based on the user's living environment and activity status. The system according to feature 1.

8. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

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