system

Abstract

The system according to the embodiment aims to restore natural speech to people who have lost their voice. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video data of a user. The analysis unit analyzes the video data collected by the collection unit to identify characteristics of the speaker's speech. The collection unit collects electroencephalogram data of the speaker. The analysis unit analyzes the electroencephalogram data collected by the collection unit to identify specific thought patterns of the speaker. The generation unit combines the speech characteristics identified by the analysis unit with the electroencephalogram data to generate a voice pattern.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty in restoring natural speech to people who have lost their voice, and there is room for improvement.

[0005] The system according to the embodiment aims to restore natural speech to people who have lost their voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video data of a user. The analysis unit analyzes the video data collected by the collection unit and identifies characteristics of the speaker's speech. The collection unit collects electroencephalogram data of the speaker. The analysis unit analyzes the electroencephalogram data collected by the collection unit and identifies specific thought patterns of the speaker. The generation unit generates a voice pattern by combining the characteristics of the speech identified by the analysis unit with the electroencephalogram data. [Effects of the Invention]

[0007] Systems according to embodiments allow people who have lost their voice to regain natural speech. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A voice generation system according to an embodiment of the present invention analyzes a user's video data and electroencephalogram (EEG) data to generate a voice pattern. The voice generation system collects and analyzes the user's video data to identify the speaker's speech characteristics and habits. Furthermore, the voice generation system collects and analyzes the speaker's EEG data to identify the speaker's thoughts. Finally, the voice generation system generates a voice pattern by combining the analyzed speech characteristics and habits with the EEG data. For example, the voice generation system collects videos of a user's past speech recordings. Next, the collected video data is trained by an AI to analyze the speaker's speech characteristics and habits. For example, the system analyzes the speaker's tone, rhythm, and unique pronunciation. Next, the voice generation system collects the speaker's EEG data. This includes EEG data obtained when the speaker is thinking. The AI ​​is trained on the collected EEG data to analyze the speaker's thoughts. For example, the EEG pattern obtained when the speaker thinks "hello" is analyzed. Finally, the voice generation system combines the analyzed speech characteristics and habits with the EEG data to create a voice pattern. This allows a lost voice to be recreated based on the speaker's thoughts. For example, when a speaker says "hello," a natural voice is generated based on the tone and rhythm of the voice learned from past video data. This allows people who have lost their voice to communicate again using their own voice. Furthermore, the tempo, pause, and atmosphere of communication can also be restored, enabling more natural conversations. This allows the voice generation system to not only recover a lost voice, but also restore the tempo, pause, and atmosphere of communication. For example, it can reproduce the pause when a speaker tells a joke, or emotional remarks.

[0029] A voice generation system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video data of a user. The video data of the user includes, but is not limited to, video calls, recorded presentations, and daily video clips. The collection unit collects, for example, videos of the user's past speaking. The collection unit also collects electroencephalogram (EEG) data of a speaker. The electroencephalogram data includes, but is not limited to, EEG (electroencephalography) data and specific frequency bands. The analysis unit analyzes the video data collected by the collection unit to identify characteristics of the speaker's speech. Examples of the speech characteristics include, but are not limited to, voice tone, rhythm, pronunciation habits, and the like. The analysis unit analyzes the collected video data to identify, for example, the speaker's voice tone, rhythm, and unique pronunciation pattern. The analysis unit also analyzes the electroencephalogram (EEG) data collected by the collection unit to identify a specific thought pattern of the speaker. Examples of the specific thought pattern include, but are not limited to, brain wave patterns and emotional states when thinking of specific words. The analysis unit, for example, analyzes the collected electroencephalogram data and identifies the electroencephalogram pattern when the speaker thinks a specific word. The generation unit combines the speech characteristics identified by the analysis unit with the electroencephalogram data to generate a voice pattern. Voice patterns include, but are not limited to, voice synthesis technology and quality standards for the generated voice. The generation unit, for example, combines the analyzed speech characteristics with the electroencephalogram data to generate a voice pattern based on the speaker's specific thoughts. As a result, the voice generation system according to the embodiment analyzes the user's video data and electroencephalogram data and generates a voice pattern, thereby enabling the recovery of a lost voice and natural communication.

[0030] The collection unit can collect previously recorded videos of the user's speech. Examples of speech videos include, but are not limited to, interview videos and recordings of everyday conversations. The collection unit, for example, collects videos in which the user has previously spoken. By collecting the user's past video data, speech characteristics and habits can be more accurately identified. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past speech videos into a generation AI and cause the generation AI to identify the speech characteristics and habits.

[0031] The analysis unit can analyze the collected video data and identify the speaker's vocal tone, rhythm, and unique pronunciation pattern. Vocal tone includes, but is not limited to, high and low pitches, intonation patterns, etc. Rhythm includes, but is not limited to, speaking speed and pauses, etc. Unique pronunciation patterns include, but are not limited to, how to pronounce specific sounds, accents, etc. For example, the analysis unit analyzes the collected video data and identifies the speaker's vocal tone, rhythm, and unique pronunciation pattern. By identifying the speaker's vocal tone, rhythm, and unique pronunciation, more natural voice patterns can be generated. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected video data into a generation AI and cause the generation AI to identify the vocal tone, rhythm, and pronunciation pattern.

[0032] The collection unit can collect brain waves when a speaker is thinking a specific thought. Specific thoughts include, but are not limited to, brain wave patterns when a speaker thinks of a specific word or phrase. The collection unit, for example, collects brain waves when a speaker is thinking a specific thought. This makes it possible to collect brain wave data for identifying what the speaker is thinking. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input brain wave data when a speaker is thinking a specific thought into the generation AI and cause the generation AI to collect brain wave patterns.

[0033] The analysis unit can analyze the collected electroencephalogram data and identify the electroencephalogram pattern when the speaker thinks a specific word. Specific words include, but are not limited to, specific words such as "hello" and "thank you." The analysis unit, for example, analyzes the collected electroencephalogram data and identifies the electroencephalogram pattern when the speaker thinks a specific word. This makes it possible to analyze the electroencephalogram pattern to identify what the speaker was thinking. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected electroencephalogram data to a generation AI and cause the generation AI to identify the electroencephalogram pattern when the speaker thinks a specific word.

[0034] The generation unit can combine the analyzed speech characteristics and the electroencephalogram data to generate a voice pattern based on the speaker's specific thought content. The specific thought content includes, but is not limited to, thought words, phrases, and emotional states. The generation unit, for example, combines the analyzed speech characteristics and the electroencephalogram data to generate a voice pattern based on the speaker's specific thought content. This allows for the generation of a natural voice pattern based on the speaker's thought content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analyzed speech characteristics and the electroencephalogram data into a generation AI and cause the generation AI to generate a voice pattern.

[0035] The collection unit can analyze the user's past video data collection history and select an appropriate collection method. The collection history includes, for example, but is not limited to, the type of videos collected in the past and the collection date and time. The appropriate collection method includes, for example, but is not limited to, the type of collection means and collection conditions. The collection unit, for example, analyzes the patterns of video data collected by the user in the past and selects the most effective collection method. Furthermore, if the user collects a lot of data during a specific time period, the collection unit can concentrate collection during that time period. Furthermore, the collection unit can optimize the collection method for specific events or situations based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past collection history data into a generation AI and have the generation AI select the optimal collection method.

[0036] When collecting video data, the collection unit can filter the video data based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, daily activity patterns and living environments. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit preferentially collects video data related to topics in which the user is currently interested. The collection unit can also collect appropriate video data depending on the user's living situation (e.g., at work, on vacation, etc.). The collection unit can also filter and collect highly relevant video data based on the user's areas of interest. This allows highly relevant video data to be collected based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0037] When collecting video data, the collection unit can select an appropriate collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of appropriate collection means include, but are not limited to, the device used and the collection process. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user uses image input, the collection unit can prioritize collecting image data. This allows the optimal collection means to be selected depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into the generation AI and have the generation AI select the collection means.

[0038] When collecting video data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant data include, but are not limited to, video data related to the user's current location. For example, when the user is in a specific location, the collection unit prioritizes collecting video data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting video data related to the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting video data related to the user's home. This allows highly relevant video data to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting video data, the collection unit can collect related data based on the user's social media activity. Social media activity includes, but is not limited to, for example, posted content, like history, and follower trends. Related data includes, but is not limited to, for example, social media trends and video data related to the user's interests. For example, the collection unit collects videos shared by the user on social media. The collection unit can also analyze the user's social media posts to collect related video data. The collection unit can also collect related video data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0040] When collecting video data, the collection unit can customize the collection method based on the user's past feedback. Examples of the feedback include, but are not limited to, user ratings, comments, and usage history. Examples of the customization include, but are not limited to, adjusting the collection method and changing the collection frequency. For example, the collection unit can optimize the collection method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular collection method, the collection unit can preferentially use that method. Furthermore, the collection unit can analyze the user's past feedback and customize the collection method. This allows the collection method to be customized based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback data into a generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis taking into account the importance of the video data. Examples of the importance include, but are not limited to, the importance of the video content and the user's level of interest. Examples of the level of detail include, but are not limited to, the depth of the analysis and the number of analysis items. For example, the analysis unit performs a detailed analysis on important video data. The analysis unit can also perform a concise analysis on less important video data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the video data. This allows the level of detail of the analysis to be adjusted according to the importance of the video data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the importance of the video data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms based on the category of the video data. Examples of categories include, but are not limited to, educational videos, entertainment videos, and news videos. Examples of analysis algorithms include, but are not limited to, machine learning algorithms and natural language processing algorithms. For example, the analysis unit can apply an analysis algorithm specifically for education to educational video data. Furthermore, the analysis unit can also apply an analysis algorithm specifically for entertainment to entertainment-related video data. Furthermore, the analysis unit can also apply an analysis algorithm specifically for news to news-related video data. This allows the optimal analysis algorithm to be applied depending on the category of the video data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the category of the video data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, previous analysis reports and analysis data history. Accuracy includes, but is not limited to, the accuracy of the analysis results and the margin of error. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority by taking into account the time when the video data was collected. The collection time includes, for example, but is not limited to, the date and time when the video was collected and the season when it was collected. The priority includes, for example, but is not limited to, the recency of the collection time and the importance of the collection time. For example, the analysis unit prioritizes analysis of the most recent video data. The analysis unit can also lower the analysis priority of older video data. The analysis unit can also dynamically adjust the analysis priority based on the collection time. This allows the analysis priority to be determined based on the time when the video data was collected. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the video data was collected to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the analysis order taking into account the relevance of the video data. Relevance includes, for example, but is not limited to, the relevance of the video content and the relevance to the user's interests. The order includes, for example, but is not limited to, the order in which the analysis is performed and the order in which the analysis results are displayed. For example, the analysis unit prioritizes the analysis of highly relevant video data. The analysis unit can also postpone the analysis of less relevant video data. The analysis unit can also dynamically adjust the analysis order based on the relevance of the video data. This allows the analysis order to be adjusted based on the relevance of the video data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the video data to the generation AI and cause the generation AI to adjust the analysis order.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis, taking into account the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. Examples of technical terminology include, but are not limited to, technical terms and terms from specialized fields. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis results according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] During generation, the generation unit can adjust the level of detail of the generated speech by taking into account the importance of the analyzed speech characteristics. Examples of the importance include, but are not limited to, the importance of speech features and the user's level of interest. Examples of the detail include, but are not limited to, the depth of generation and the number of generated items. For example, the generation unit generates detailed speech patterns for important speech features and habits. The generation unit can also generate concise speech patterns for less important speech features and habits. The generation unit can also dynamically adjust the level of detail of the generated speech depending on the importance of the speech features and habits. This allows the level of detail of the generated speech to be adjusted depending on the importance of the speech features and habits. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the analyzed speech features and habits into the generation AI and cause the generation AI to adjust the level of detail of the generated speech.

[0048] During generation, the generation unit can apply different generation algorithms based on the category of the analyzed EEG data. Examples of categories include, but are not limited to, the type of EEG data and the frequency band of the EEG. Examples of generation algorithms include, but are not limited to, a voice synthesis algorithm and a machine learning algorithm. For example, the generation unit applies a generation algorithm dedicated to emotion to EEG data related to emotion. The generation unit can also apply a generation algorithm dedicated to memory to EEG data related to memory. The generation unit can also apply a generation algorithm dedicated to thought to EEG data related to thought. This allows the application of an optimal generation algorithm depending on the category of the EEG data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the category of the analyzed EEG data to the generation AI and cause the generation AI to apply the generation algorithm.

[0049] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. Past generation results include, for example, but are not limited to, previous generated voices and generation data history. Accuracy includes, for example, but is not limited to, the accuracy of the generated voice and the margin of error. The generation unit, for example, optimizes the generation algorithm based on the user's past generation results. The generation unit can also improve the generation accuracy by referring to feedback provided by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation accuracy. In this way, the generation accuracy is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input data of the user's past generation results into the generation AI and cause the generation AI to improve the generation accuracy.

[0050] During generation, the generation unit can determine the generation priority by taking into account the collection time of the analyzed speech characteristics. Examples of the collection time include, but are not limited to, the date and time when the speech characteristics and habits were collected and the season when they were collected. Examples of the priority include, but are not limited to, the recency of the collection time and the importance of the collection time. For example, the generation unit prioritizes the generation of the most recent speech characteristics and habits. The generation unit can also lower the generation priority of speech characteristics and habits that were collected earlier. The generation unit can also dynamically adjust the generation priority based on the collection time. This allows the generation priority to be determined based on the collection time of the speech characteristics and habits. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the collection time of the analyzed speech characteristics and habits into the generation AI and have the generation AI determine the generation priority.

[0051] During generation, the generation unit can adjust the order of generation taking into account the relevance of the analyzed speech characteristics. Examples of relevance include, but are not limited to, the relevance of speech features and habits, and the relevance to the user's interests. Examples of the order include, but are not limited to, the order in which generation is performed and the order in which the generation results are displayed. For example, the generation unit prioritizes the generation of highly relevant speech features and habits. The generation unit can also postpone the generation order of less relevant speech features and habits. The generation unit can also dynamically adjust the order of generation based on the relevance of the speech features and habits. This allows the order of generation to be adjusted based on the relevance of the speech features and habits. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the relevance of the analyzed speech features and habits into the generation AI and cause the generation AI to adjust the order of generation.

[0052] The generation unit can adjust the use of technical terms in the generated speech pattern during generation, taking into account the user's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and expert levels. Examples of technical terms include, but are not limited to, technical terms and terms from specialized fields. For example, if the user has specialized knowledge, the generation unit can generate a speech pattern that uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the generation unit can generate a concise and easy-to-understand speech pattern. Furthermore, the generation unit can dynamically adjust the use of technical terms in the speech pattern according to the user's level of expertise. This allows the use of technical terms in the generated speech pattern to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can also analyze the user's lifestyle rhythm and adjust the timing of video data collection based on the analyzed lifestyle rhythm. For example, if the user is active in the morning, video data can be collected during that time period. Alternatively, if the user is relaxed in the evening, video data can be collected during that time period. Furthermore, the collection timing can be optimized based on the user's weekend activity patterns. This allows video data to be collected at the optimal timing according to the user's lifestyle rhythm.

[0055] The analysis unit can also analyze the user's past speech data and identify changes in speech characteristics and habits based on the analyzed data. For example, it can identify how the user's tone and rhythm of voice have changed over time. It can also identify changes in the user's pronunciation patterns. It can also identify changes in speech characteristics and habits based on changes in the user's emotional state. This makes it possible to identify changes in the user's speech characteristics and habits and generate more accurate voice patterns.

[0056] The collection unit can also analyze the user's social media activity and collect related video data based on the analyzed activity. For example, it can collect videos shared by the user on social media. It can also analyze the content posted by the user on social media to collect related video data. It can also collect related video data by referring to the activity of the user's friends on social media. In this way, it is possible to collect related video data based on the user's social media activity.

[0057] The collection unit can also analyze the user's geographical location information and collect related video data based on the analyzed location information. For example, if the user is in a specific location, video data related to that location can be collected. If the user is traveling, video data related to the travel destination can be collected. Furthermore, if the user is at home, video data related to the home can be collected. In this way, related video data can be collected based on the user's geographical location information.

[0058] The analysis unit can also analyze the user's level of expertise and adjust the way the analysis results are presented based on the analyzed level of expertise. For example, if the user has expertise, it can provide analysis results that make extensive use of technical terms. On the other hand, if the user does not have expertise, it can provide analysis results that are concise and easy to understand. Furthermore, it can dynamically adjust the way the analysis results are presented depending on the user's level of expertise. This makes it possible to adjust the way the analysis results are presented depending on the user's level of expertise.

[0059] The collection unit can also analyze the user's past feedback and customize the collection method based on the analyzed feedback. For example, the collection method can be optimized based on feedback provided by the user in the past. Also, if the user prefers a particular collection method, that method can be used preferentially. Furthermore, the collection unit can analyze the user's past feedback and customize the collection method. This makes it possible to customize the collection method based on the user's past feedback.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects the user's video data. The user's video data includes video calls, recorded presentations, daily video clips, etc. The collection unit also collects the speaker's brain wave data. The brain wave data includes EEG (electroencephalography) data and specific frequency bands. Step 2: The analysis unit analyzes the video data collected by the collection unit and identifies the speaker's speech characteristics. The speech characteristics include voice tone, rhythm, pronunciation habits, etc. The analysis unit also analyzes the electroencephalogram data collected by the collection unit and identifies the speaker's specific thought patterns. The specific thought patterns include the electroencephalogram patterns and emotional states when thinking of specific words, etc. Step 3: The generator combines the speech characteristics identified by the analyzer with the EEG data to generate a voice pattern, which includes the voice synthesis technology and the quality standards of the generated voice.

[0062] (Example 2) A voice generation system according to an embodiment of the present invention analyzes a user's video data and electroencephalogram (EEG) data to generate a voice pattern. The voice generation system collects and analyzes the user's video data to identify the speaker's speech characteristics and habits. Furthermore, the voice generation system collects and analyzes the speaker's EEG data to identify the speaker's thoughts. Finally, the voice generation system generates a voice pattern by combining the analyzed speech characteristics and habits with the EEG data. For example, the voice generation system collects videos of a user's past speech recordings. Next, the collected video data is trained by an AI to analyze the speaker's speech characteristics and habits. For example, the system analyzes the speaker's tone, rhythm, and unique pronunciation. Next, the voice generation system collects the speaker's EEG data. This includes EEG data obtained when the speaker is thinking. The AI ​​is trained on the collected EEG data to analyze the speaker's thoughts. For example, the EEG pattern obtained when the speaker thinks "hello" is analyzed. Finally, the voice generation system combines the analyzed speech characteristics and habits with the EEG data to create a voice pattern. This allows a lost voice to be recreated based on the speaker's thoughts. For example, when a speaker says "hello," a natural voice is generated based on the tone and rhythm of the voice learned from past video data. This allows people who have lost their voice to communicate again using their own voice. Furthermore, the tempo, pause, and atmosphere of communication can also be restored, enabling more natural conversations. This allows the voice generation system to not only recover a lost voice, but also restore the tempo, pause, and atmosphere of communication. For example, it can reproduce the pause when a speaker tells a joke, or emotional remarks.

[0063] A voice generation system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video data of a user. The video data of the user includes, but is not limited to, video calls, recorded presentations, and daily video clips. The collection unit collects, for example, videos of the user's past speaking. The collection unit also collects electroencephalogram (EEG) data of a speaker. The electroencephalogram data includes, but is not limited to, EEG (electroencephalography) data and specific frequency bands. The analysis unit analyzes the video data collected by the collection unit to identify characteristics of the speaker's speech. Examples of the speech characteristics include, but are not limited to, voice tone, rhythm, pronunciation habits, and the like. The analysis unit analyzes the collected video data to identify, for example, the speaker's voice tone, rhythm, and unique pronunciation pattern. The analysis unit also analyzes the electroencephalogram (EEG) data collected by the collection unit to identify a specific thought pattern of the speaker. Examples of the specific thought pattern include, but are not limited to, brain wave patterns and emotional states when thinking of specific words. The analysis unit, for example, analyzes the collected electroencephalogram data and identifies the electroencephalogram pattern when the speaker thinks a specific word. The generation unit combines the speech characteristics identified by the analysis unit with the electroencephalogram data to generate a voice pattern. Voice patterns include, but are not limited to, voice synthesis technology and quality standards for the generated voice. The generation unit, for example, combines the analyzed speech characteristics with the electroencephalogram data to generate a voice pattern based on the speaker's specific thoughts. As a result, the voice generation system according to the embodiment analyzes the user's video data and electroencephalogram data and generates a voice pattern, thereby enabling the recovery of a lost voice and natural communication.

[0064] The collection unit can collect previously recorded videos of the user's speech. Examples of speech videos include, but are not limited to, interview videos and recordings of everyday conversations. The collection unit, for example, collects videos in which the user has previously spoken. By collecting the user's past video data, speech characteristics and habits can be more accurately identified. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past speech videos into a generation AI and cause the generation AI to identify the speech characteristics and habits.

[0065] The analysis unit can analyze the collected video data and identify the speaker's vocal tone, rhythm, and unique pronunciation pattern. Vocal tone includes, but is not limited to, high and low pitches, intonation patterns, etc. Rhythm includes, but is not limited to, speaking speed and pauses, etc. Unique pronunciation patterns include, but are not limited to, how to pronounce specific sounds, accents, etc. For example, the analysis unit analyzes the collected video data and identifies the speaker's vocal tone, rhythm, and unique pronunciation pattern. By identifying the speaker's vocal tone, rhythm, and unique pronunciation, more natural voice patterns can be generated. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected video data into a generation AI and cause the generation AI to identify the vocal tone, rhythm, and pronunciation pattern.

[0066] The collection unit can collect brain waves when a speaker is thinking a specific thought. Specific thoughts include, but are not limited to, brain wave patterns when a speaker thinks of a specific word or phrase. The collection unit, for example, collects brain waves when a speaker is thinking a specific thought. This makes it possible to collect brain wave data for identifying what the speaker is thinking. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input brain wave data when a speaker is thinking a specific thought into the generation AI and cause the generation AI to collect brain wave patterns.

[0067] The analysis unit can analyze the collected electroencephalogram data and identify the electroencephalogram pattern when the speaker thinks a specific word. Specific words include, but are not limited to, specific words such as "hello" and "thank you." The analysis unit, for example, analyzes the collected electroencephalogram data and identifies the electroencephalogram pattern when the speaker thinks a specific word. This makes it possible to analyze the electroencephalogram pattern to identify what the speaker was thinking. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected electroencephalogram data to a generation AI and cause the generation AI to identify the electroencephalogram pattern when the speaker thinks a specific word.

[0068] The generation unit can combine the analyzed speech characteristics and the electroencephalogram data to generate a voice pattern based on the speaker's specific thought content. The specific thought content includes, but is not limited to, thought words, phrases, and emotional states. The generation unit, for example, combines the analyzed speech characteristics and the electroencephalogram data to generate a voice pattern based on the speaker's specific thought content. This allows for the generation of a natural voice pattern based on the speaker's thought content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analyzed speech characteristics and the electroencephalogram data into a generation AI and cause the generation AI to generate a voice pattern.

[0069] The collection unit can analyze the user's emotions and appropriately adjust the timing of video data collection based on the analyzed emotions. Emotions include, but are not limited to, emotional states such as joy, sadness, and anger. The appropriate adjustment can include, but is not limited to, a specific method of adjusting the collection timing. For example, the collection unit collects past video data when the user is relaxed. The collection unit can also temporarily stop collection when the user is stressed and resume collection when the user is relaxed. The collection unit can also collect video data when the user is concentrating to obtain higher quality data. This allows video data to be collected at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0070] The collection unit can analyze the user's past video data collection history and select an appropriate collection method. The collection history includes, for example, but is not limited to, the type of videos collected in the past and the collection date and time. The appropriate collection method includes, for example, but is not limited to, the type of collection means and collection conditions. The collection unit, for example, analyzes the patterns of video data collected by the user in the past and selects the most effective collection method. Furthermore, if the user collects a lot of data during a specific time period, the collection unit can concentrate collection during that time period. Furthermore, the collection unit can optimize the collection method for specific events or situations based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past collection history data into a generation AI and have the generation AI select the optimal collection method.

[0071] When collecting video data, the collection unit can filter the video data based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, daily activity patterns and living environments. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit preferentially collects video data related to topics in which the user is currently interested. The collection unit can also collect appropriate video data depending on the user's living situation (e.g., at work, on vacation, etc.). The collection unit can also filter and collect highly relevant video data based on the user's areas of interest. This allows highly relevant video data to be collected based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0072] When collecting video data, the collection unit can select an appropriate collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of appropriate collection means include, but are not limited to, the device used and the collection process. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user uses image input, the collection unit can prioritize collecting image data. This allows the optimal collection means to be selected depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into the generation AI and have the generation AI select the collection means.

[0073] The collection unit can analyze the user's emotions and determine the priority of the video data to be collected based on the analyzed emotions. Examples of emotions include, but are not limited to, emotional states such as joy, sadness, and anger. Examples of priorities include, but are not limited to, the importance and urgency of the video data to be collected. For example, the collection unit prioritizes the collection of important video data when the user is relaxed. The collection unit can also lower the collection priority when the user is feeling stressed. The collection unit can also raise the collection priority when the user is concentrating. This allows the priority of the video data to be collected to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the video data to be collected.

[0074] When collecting video data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Examples of highly relevant data include, but are not limited to, video data related to the user's current location. For example, when the user is in a specific location, the collection unit prioritizes collecting video data related to that location. Furthermore, when the user is traveling, the collection unit can prioritize collecting video data related to the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting video data related to the user's home. This allows highly relevant video data to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0075] When collecting video data, the collection unit can collect related data based on the user's social media activity. Social media activity includes, but is not limited to, for example, posted content, like history, and follower trends. Related data includes, but is not limited to, for example, social media trends and video data related to the user's interests. For example, the collection unit collects videos shared by the user on social media. The collection unit can also analyze the user's social media posts to collect related video data. The collection unit can also collect related video data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0076] When collecting video data, the collection unit can customize the collection method based on the user's past feedback. Examples of the feedback include, but are not limited to, user ratings, comments, and usage history. Examples of the customization include, but are not limited to, adjusting the collection method and changing the collection frequency. For example, the collection unit can optimize the collection method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular collection method, the collection unit can preferentially use that method. Furthermore, the collection unit can analyze the user's past feedback and customize the collection method. This allows the collection method to be customized based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback data into a generation AI and have the generation AI customize the collection method.

[0077] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed emotions. Emotions include, but are not limited to, emotional states such as joy, sadness, and anger. Presentation methods include, but are not limited to, the display format of the analysis results and the selection of words to use. For example, the analysis unit can provide detailed analysis results when the user is relaxed. For example, the analysis unit can provide concise analysis results when the user is stressed. For example, the analysis unit can provide in-depth analysis results when the user is focused. This allows the way the analysis is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis taking into account the importance of the video data. Examples of the importance include, but are not limited to, the importance of the video content and the user's level of interest. Examples of the level of detail include, but are not limited to, the depth of the analysis and the number of analysis items. For example, the analysis unit performs a detailed analysis on important video data. The analysis unit can also perform a concise analysis on less important video data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the video data. This allows the level of detail of the analysis to be adjusted according to the importance of the video data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the importance of the video data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms based on the category of the video data. Examples of categories include, but are not limited to, educational videos, entertainment videos, and news videos. Examples of analysis algorithms include, but are not limited to, machine learning algorithms and natural language processing algorithms. For example, the analysis unit can apply an analysis algorithm specifically for education to educational video data. Furthermore, the analysis unit can also apply an analysis algorithm specifically for entertainment to entertainment-related video data. Furthermore, the analysis unit can also apply an analysis algorithm specifically for news to news-related video data. This allows the optimal analysis algorithm to be applied depending on the category of the video data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the category of the video data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, previous analysis reports and analysis data history. Accuracy includes, but is not limited to, the accuracy of the analysis results and the margin of error. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0081] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed emotions. Emotions include, but are not limited to, emotional states such as joy, sadness, and anger. Length includes, but is not limited to, the analysis time and the number of pages in the analysis report. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer result. Furthermore, if the user is stressed, the analysis unit can perform a concise analysis and provide a shorter result. Furthermore, if the user is concentrating, the analysis unit can perform an in-depth analysis and provide a more appropriate result. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0082] During analysis, the analysis unit can determine the analysis priority by taking into account the time when the video data was collected. The collection time includes, for example, but is not limited to, the date and time when the video was collected and the season when it was collected. The priority includes, for example, but is not limited to, the recency of the collection time and the importance of the collection time. For example, the analysis unit prioritizes analysis of the most recent video data. The analysis unit can also lower the analysis priority of older video data. The analysis unit can also dynamically adjust the analysis priority based on the collection time. This allows the analysis priority to be determined based on the time when the video data was collected. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the video data was collected to the generation AI and have the generation AI determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the analysis order taking into account the relevance of the video data. Relevance includes, for example, but is not limited to, the relevance of the video content and the relevance to the user's interests. The order includes, for example, but is not limited to, the order in which the analysis is performed and the order in which the analysis results are displayed. For example, the analysis unit prioritizes the analysis of highly relevant video data. The analysis unit can also postpone the analysis of less relevant video data. The analysis unit can also dynamically adjust the analysis order based on the relevance of the video data. This allows the analysis order to be adjusted based on the relevance of the video data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the video data to the generation AI and cause the generation AI to adjust the analysis order.

[0084] During analysis, the analysis unit can adjust the use of technical terminology in the analysis, taking into account the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. Examples of technical terminology include, but are not limited to, technical terms and terms from specialized fields. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis results according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0085] The generation unit can analyze the user's emotions and adjust the expression method of the generated voice pattern based on the analyzed emotions. Emotions include, but are not limited to, emotional states such as joy, sadness, and anger. Expression methods include, but are not limited to, the tone, rhythm, and word selection of the voice pattern. For example, the generation unit generates a calm voice pattern when the user is relaxed. The generation unit can also generate a calm voice pattern when the user is stressed. The generation unit can also generate a lively voice pattern when the user is excited. This allows the expression method of the generated voice pattern to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression method of the voice pattern.

[0086] During generation, the generation unit can adjust the level of detail of the generated speech by taking into account the importance of the analyzed speech characteristics. Examples of the importance include, but are not limited to, the importance of speech features and the user's level of interest. Examples of the detail include, but are not limited to, the depth of generation and the number of generated items. For example, the generation unit generates detailed speech patterns for important speech features and habits. The generation unit can also generate concise speech patterns for less important speech features and habits. The generation unit can also dynamically adjust the level of detail of the generated speech depending on the importance of the speech features and habits. This allows the level of detail of the generated speech to be adjusted depending on the importance of the speech features and habits. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the analyzed speech features and habits into the generation AI and cause the generation AI to adjust the level of detail of the generated speech.

[0087] During generation, the generation unit can apply different generation algorithms based on the category of the analyzed EEG data. Examples of categories include, but are not limited to, the type of EEG data and the frequency band of the EEG. Examples of generation algorithms include, but are not limited to, a voice synthesis algorithm and a machine learning algorithm. For example, the generation unit applies a generation algorithm dedicated to emotion to EEG data related to emotion. The generation unit can also apply a generation algorithm dedicated to memory to EEG data related to memory. The generation unit can also apply a generation algorithm dedicated to thought to EEG data related to thought. This allows the application of an optimal generation algorithm depending on the category of the EEG data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the category of the analyzed EEG data to the generation AI and cause the generation AI to apply the generation algorithm.

[0088] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. Past generation results include, for example, but are not limited to, previous generated voices and generation data history. Accuracy includes, for example, but is not limited to, the accuracy of the generated voice and the margin of error. The generation unit, for example, optimizes the generation algorithm based on the user's past generation results. The generation unit can also improve the generation accuracy by referring to feedback provided by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation accuracy. In this way, the generation accuracy is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input data of the user's past generation results into the generation AI and cause the generation AI to improve the generation accuracy.

[0089] The generation unit can analyze the user's emotions and adjust the length of the generated voice pattern based on the analyzed emotions. Emotions include, but are not limited to, emotional states such as joy, sadness, and anger. Lengths include, but are not limited to, the duration of the generated voice pattern and the length of the voice phrases. For example, the generation unit can generate a longer voice pattern when the user is relaxed. The generation unit can also generate a shorter voice pattern when the user is stressed. The generation unit can also generate a voice pattern of an appropriate length when the user is concentrating. This allows the length of the generated voice pattern to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the voice pattern.

[0090] During generation, the generation unit can determine the generation priority by taking into account the collection time of the analyzed speech characteristics. Examples of the collection time include, but are not limited to, the date and time when the speech characteristics and habits were collected and the season when they were collected. Examples of the priority include, but are not limited to, the recency of the collection time and the importance of the collection time. For example, the generation unit prioritizes the generation of the most recent speech characteristics and habits. The generation unit can also lower the generation priority of speech characteristics and habits that were collected earlier. The generation unit can also dynamically adjust the generation priority based on the collection time. This allows the generation priority to be determined based on the collection time of the speech characteristics and habits. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the collection time of the analyzed speech characteristics and habits into the generation AI and have the generation AI determine the generation priority.

[0091] During generation, the generation unit can adjust the order of generation taking into account the relevance of the analyzed speech characteristics. Examples of relevance include, but are not limited to, the relevance of speech features and habits, and the relevance to the user's interests. Examples of the order include, but are not limited to, the order in which generation is performed and the order in which the generation results are displayed. For example, the generation unit prioritizes the generation of highly relevant speech features and habits. The generation unit can also postpone the generation order of less relevant speech features and habits. The generation unit can also dynamically adjust the order of generation based on the relevance of the speech features and habits. This allows the order of generation to be adjusted based on the relevance of the speech features and habits. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the relevance of the analyzed speech features and habits into the generation AI and cause the generation AI to adjust the order of generation.

[0092] The generation unit can adjust the use of technical terms in the generated speech pattern during generation, taking into account the user's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and expert levels. Examples of technical terms include, but are not limited to, technical terms and terms from specialized fields. For example, if the user has specialized knowledge, the generation unit can generate a speech pattern that uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the generation unit can generate a concise and easy-to-understand speech pattern. Furthermore, the generation unit can dynamically adjust the use of technical terms in the speech pattern according to the user's level of expertise. This allows the use of technical terms in the generated speech pattern to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects video data and electroencephalogram data of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected video data and electroencephalogram data to identify the speaker's speech characteristics and thought patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a voice pattern by combining the analyzed speech characteristics and electroencephalogram data. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects video data and electroencephalogram data of the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected video data and electroencephalogram data to identify the speaker's speech characteristics and thought patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a voice pattern by combining the analyzed speech characteristics and electroencephalogram data. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects video data and electroencephalogram data of the user using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected video data and electroencephalogram data to identify the speaker's speech characteristics and thought patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a voice pattern by combining the analyzed speech characteristics and electroencephalogram data. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects video data and electroencephalogram data of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected video data and electroencephalogram data to identify the speaker's speech characteristics and thought patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a voice pattern by combining the analyzed speech characteristics and electroencephalogram data.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, important analysis can be prioritized. Also, if the user is feeling stressed, the priority of analysis can be lowered. Furthermore, if the user is concentrating, detailed analysis can be performed to improve the accuracy of the analysis. This makes it possible to dynamically adjust the priority of analysis according to the user's emotions.

[0095] The collection unit can also analyze the user's lifestyle rhythm and adjust the timing of video data collection based on the analyzed lifestyle rhythm. For example, if the user is active in the morning, video data can be collected during that time period. Alternatively, if the user is relaxed in the evening, video data can be collected during that time period. Furthermore, the collection timing can be optimized based on the user's weekend activity patterns. This allows video data to be collected at the optimal timing according to the user's lifestyle rhythm.

[0096] The analysis unit can also analyze the user's past speech data and identify changes in speech characteristics and habits based on the analyzed data. For example, it can identify how the user's tone and rhythm of voice have changed over time. It can also identify changes in the user's pronunciation patterns. It can also identify changes in speech characteristics and habits based on changes in the user's emotional state. This makes it possible to identify changes in the user's speech characteristics and habits and generate more accurate voice patterns.

[0097] The collection unit can also analyze the user's social media activity and collect related video data based on the analyzed activity. For example, it can collect videos shared by the user on social media. It can also analyze the content posted by the user on social media to collect related video data. It can also collect related video data by referring to the activity of the user's friends on social media. In this way, it is possible to collect related video data based on the user's social media activity.

[0098] The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. Furthermore, if the user is concentrating, in-depth analysis results can be provided. This makes it possible to adjust the way the analysis is presented depending on the user's emotions.

[0099] The generation unit can also estimate the user's emotion and adjust the tone and rhythm of the generated voice pattern based on the estimated emotion. For example, if the user is relaxed, a voice pattern with a gentle tone and rhythm can be generated. If the user is stressed, a voice pattern with a calm tone and rhythm can be generated. Furthermore, if the user is excited, a voice pattern with a lively tone and rhythm can be generated. In this way, the tone and rhythm of the generated voice pattern can be adjusted according to the user's emotion.

[0100] The collection unit can also analyze the user's geographical location information and collect related video data based on the analyzed location information. For example, if the user is in a specific location, video data related to that location can be collected. If the user is traveling, video data related to the travel destination can be collected. Furthermore, if the user is at home, video data related to the home can be collected. In this way, related video data can be collected based on the user's geographical location information.

[0101] The analysis unit can also analyze the user's level of expertise and adjust the way the analysis results are presented based on the analyzed level of expertise. For example, if the user has expertise, it can provide analysis results that make extensive use of technical terms. On the other hand, if the user does not have expertise, it can provide analysis results that are concise and easy to understand. Furthermore, it can dynamically adjust the way the analysis results are presented depending on the user's level of expertise. This makes it possible to adjust the way the analysis results are presented depending on the user's level of expertise.

[0102] The generation unit can also estimate the user's emotion and adjust the length of the generated voice pattern based on the estimated emotion. For example, if the user is relaxed, a longer voice pattern can be generated. Also, if the user is feeling stressed, a shorter voice pattern can be generated. Furthermore, if the user is concentrating, a voice pattern of an appropriate length can be generated. In this way, the length of the generated voice pattern can be adjusted according to the user's emotion.

[0103] The collection unit can also analyze the user's past feedback and customize the collection method based on the analyzed feedback. For example, the collection method can be optimized based on feedback provided by the user in the past. Also, if the user prefers a particular collection method, that method can be used preferentially. Furthermore, the collection unit can analyze the user's past feedback and customize the collection method. This makes it possible to customize the collection method based on the user's past feedback.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects the user's video data. The user's video data includes video calls, recorded presentations, daily video clips, etc. The collection unit also collects the speaker's brain wave data. The brain wave data includes EEG (electroencephalography) data and specific frequency bands. Step 2: The analysis unit analyzes the video data collected by the collection unit and identifies the speaker's speech characteristics. The speech characteristics include voice tone, rhythm, pronunciation habits, etc. The analysis unit also analyzes the electroencephalogram data collected by the collection unit and identifies the speaker's specific thought patterns. The specific thought patterns include the electroencephalogram patterns and emotional states when thinking of specific words, etc. Step 3: The generator combines the speech characteristics identified by the analyzer with the EEG data to generate a voice pattern, which includes the voice synthesis technology and the quality standards of the generated voice.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects video data of users; an analysis unit that analyzes the video data collected by the collection unit and identifies characteristics of the speaker's speech; a collection unit that collects electroencephalogram data of a speaker; an analysis unit that analyzes the electroencephalogram data collected by the collection unit and identifies a specific thought pattern of the speaker; a generation unit that generates a voice pattern by combining the speech characteristics identified by the analysis unit with electroencephalogram data. A system characterized by:

2. The collecting unit Collect previously recorded user speech videos 2. The system of claim 1.

3. The analysis unit Analyzes collected video data to identify the speaker's tone, rhythm, and unique pronunciation patterns 2. The system of claim 1.

4. The collecting unit Collecting brain waves when a speaker is thinking a specific thought 2. The system of claim 1.

5. The analysis unit Analyzing the collected EEG data to identify the brain wave patterns that occur when a speaker thinks about specific words 2. The system of claim 1.

6. The generation unit Combining analyzed speech characteristics with EEG data to generate speech patterns based on the speaker's specific thoughts 2. The system of claim 1.

7. The collecting unit Analyze user emotions and appropriately adjust the timing of video data collection based on the analyzed emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past video data collection history and select the appropriate collection method 2. The system of claim 1.

9. The collecting unit As video data is collected, it is filtered based on the user's current lifestyle and interests.

2. The system of claim 1.

10. The collecting unit When collecting video data, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

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